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

Abstract

The application discloses an online frequency selection data transmission method, system and online frequency selection platform, relates to the field of data transmission, and comprises the following steps: collecting multi-frequency electromagnetic radiation data; performing environment interference compensation on the multi-frequency electromagnetic radiation data to obtain a corrected electromagnetic radiation data set; generating a data table according to the corrected electromagnetic radiation data set, and dispersively storing the corrected electromagnetic radiation data into 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 the online frequency selection platform through a dynamic block transmission strategy; in response to receiving electromagnetic radiation modes and target areas returned by the online frequency selection platform, performing data interaction between the electromagnetic radiation modes and the target areas and the plurality of physical nodes, and generating a three-dimensional space distribution map of an electromagnetic field through a preset map API and displaying the three-dimensional space distribution map through a client. The application can effectively improve the efficiency and security of data transmission.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the field of data transmission, in particular to an online frequency selection data transmission method, system and online frequency selection platform. BACKGROUND

[0002] With the rapid development of electronic and communication technology, the electromagnetic radiation level in the city continues to rise, and the frequency band range continues to expand. Electromagnetic radiation not only poses a potential threat to human health, but also may interfere with the normal operation of electronic equipment, even cause equipment failure, and affect the stability of social production and life. Therefore, real-time monitoring and analysis of the electromagnetic radiation environment has become an important topic in the current research field. The electromagnetic radiation monitoring system can provide 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 problems. At present, the monitoring process of the existing electromagnetic radiation monitoring system is as follows: first, the electromagnetic radiation data is collected by the sensor, the TCP / IP protocol or the HTTP protocol is used for data transmission, the collected electromagnetic radiation data is stored in the centralized database in time sequence, and the electromagnetic radiation data is displayed through the two-dimensional chart.

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

[0005] In summary, with the increase in the amount of data transmission, when facing high-concurrency data transmission of large-scale equipment, the electromagnetic radiation monitoring system in the prior art is prone to transmission delay, packet loss and other problems, which greatly reduces the efficiency and security of data transmission. SUMMARY

[0006] The embodiment of the present application provides an online frequency selection data transmission method, system and online frequency selection platform, which is used to effectively alleviate the transmission delay, packet loss and other problems in the prior art, and then improve the efficiency and security of data transmission.

[0007] To achieve the above purpose, the embodiment of the present application adopts the following technical scheme:

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

[0009] Collecting multi-band electromagnetic radiation data of a plurality of monitoring sites through the group of intelligent hardware terminals;

[0010] Compensating the multi-band electromagnetic radiation data for environmental interference to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set;

[0011] Generating a data table according to the corrected electromagnetic radiation data set, and dispersively storing the corrected electromagnetic radiation data into a plurality of physical nodes based on the data table, wherein the data type of the electromagnetic radiation data stored by each physical node is consistent;

[0012] 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 configured to receive data blocks of the encrypted electromagnetic radiation data set and store all the data blocks of the encrypted electromagnetic radiation data set, and the analysis engine is configured to decrypt each data block and determine an electromagnetic radiation mode and a target area of similar electromagnetic environment characteristics based on the decrypted electromagnetic radiation data;

[0013] In response to receiving the electromagnetic radiation mode and the target area returned by the online frequency selection platform, performing data interaction between the electromagnetic radiation mode and the target area and a plurality of physical nodes, and generating a three-dimensional spatial distribution map of an electromagnetic field through a preset map API and displaying the three-dimensional spatial distribution map through the client.

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

[0015] Obtaining current environmental data, and calculating an electromagnetic wave attenuation coefficient in air based on the current environmental data by using a preset electromagnetic wave attenuation formula;

[0016] Compensating the multi-band electromagnetic radiation data for environmental interference according to the attenuation coefficient to correct the multi-band electromagnetic radiation data to obtain a corrected electromagnetic radiation data set.

[0017] In a possible implementation manner of the first aspect, the smart hardware terminal group comprises n sensors, n being an integer greater than 2, and the environment interference compensation on the multi-band electromagnetic radiation data according to the attenuation coefficient comprises:

[0018] obtaining installation positions of each of the sensors in the smart hardware terminal group;

[0019] determining, for each of the electromagnetic radiation data, geographical position information of the monitoring site where the electromagnetic radiation data is located;

[0020] calculating, according to the installation positions and the geographical position information of the monitoring sites, straight-line distances of electromagnetic waves from the sensors to the monitoring sites by using a distance calculation formula;

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

[0022] calculating a product of the attenuation coefficient and the electromagnetic wave propagation distance to obtain an attenuation amount of electromagnetic waves in air;

[0023] compensating the multi-band electromagnetic radiation data according to the attenuation amount to obtain a corrected electromagnetic radiation data set.

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

[0025] generating, according to the corrected electromagnetic radiation data set, a data table containing data types, time stamps and geographical position information, wherein the data table comprises all electromagnetic radiation data in the corrected electromagnetic radiation data set, and each of the electromagnetic radiation data corresponds to a data type, a time stamp and geographical position information;

[0026] dividing the data table into multiple sub-tables according to the data types, and dispersively storing electromagnetic radiation data corresponding to each of the sub-tables into a corresponding physical node, wherein the multiple sub-tables comprise 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 a possible implementation manner of the first aspect, the encrypting the corrected electromagnetic radiation data set comprises:

[0028] encrypting the corrected electromagnetic radiation data set by using a preset encryption algorithm to obtain an encrypted electromagnetic radiation data set.

[0029] obtaining a current network bandwidth and a current network delay;

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

[0031] calculating a first hash value of the temporary key generation parameter and taking the first hash value as a temporary key;

[0032] binding the temporary key with the encrypted electromagnetic radiation dataset to obtain an encrypted electromagnetic radiation dataset.

[0033] In a possible implementation of the first aspect, the dynamic block transmission strategy comprises:

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

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

[0036] obtaining a dataset size of the encrypted electromagnetic radiation dataset, taking a product of the dataset size and the data block size coefficient as a size threshold of each data block, and dynamically dividing the encrypted electromagnetic radiation dataset into a plurality of data blocks by using a dynamic division strategy, wherein a size of each data block is less than the size threshold;

[0037] for each data block, calculating a second hash value of a product of the timestamp and the geographic location information, and taking the second hash value as a unique classification identifier of the data block;

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

[0039] In a possible implementation of the first aspect, the dynamic division strategy comprises:

[0040] setting an initial data block size as the size threshold;

[0041] sequentially reading data in the encrypted electromagnetic radiation dataset, and dividing the read data into a data block when a cumulative data amount reaches a current data block size;

[0042] calculating an average size of the divided data block;

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

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

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

[0046] repeating the above steps until the entire encrypted electromagnetic radiation dataset is divided.

[0047] In a possible implementation of the first aspect, the analysis engine performs block decryption on each of the data blocks, and determines the electromagnetic radiation pattern and the target region with similar electromagnetic environment features based on the block-decrypted electromagnetic radiation data, including:

[0048] the analysis engine decrypts each of the data blocks using the temporary key to obtain the block-decrypted electromagnetic radiation data;

[0049] extracting the spectral features of the block-decrypted electromagnetic radiation data;

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

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

[0052] merging cluster centers with similar average electromagnetic radiation intensity and spatial distribution features to obtain m target regions, where m is less than or equal to k;

[0053] determining the boundary of each target region according to the spatial distribution features to obtain target regions with similar electromagnetic environment features;

[0054] inputting the spectral features into a pre-constructed CNN model to determine the electromagnetic radiation pattern.

[0055] In a possible implementation of the first aspect, the data interaction between the electromagnetic radiation pattern and the target region and the plurality of physical nodes, and the generation and display of the three-dimensional spatial distribution map of the electromagnetic field through the preset map API, include:

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

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

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

[0059] The central control device comprises a local database, the local database comprises 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 with the central control device.

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

[0062] a cloud data center; and

[0063] an analysis engine.

[0064] Through the above technical solution, the intelligent hardware terminal group collects multi-band electromagnetic radiation data of multiple monitoring sites, and compensates the data for environmental interference, ensuring the accuracy and reliability of the data, and providing a high-quality data basis for subsequent analysis. Secondly, the corrected electromagnetic radiation data is dispersedly stored in multiple physical nodes according to the data type, avoiding the I / O load pressure of centralized databases in high-concurrency data transmission, thereby greatly improving the read-write performance and data storage efficiency of the database. In addition, by encrypting the electromagnetic radiation data set and combining a dynamic block transmission strategy, the data is transmitted in encrypted form to the online frequency selection platform, not only enhancing the security of data transmission, but also alleviating the congestion problem of the data transmission channel through block transmission, effectively reducing the transmission delay and packet loss rate. The cloud data center of the online frequency selection platform receives and classifies the encrypted data blocks, the analysis engine decrypts each data block, and determines the electromagnetic radiation mode and the target area of similar electromagnetic environment characteristics based on the decrypted data, not only improving the efficiency and accuracy of data analysis, but also providing a scientific basis for real-time monitoring and evaluation of the electromagnetic radiation environment. Finally, by interacting the analysis results with multiple physical nodes and using a 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 visual support. In summary, the above effectively solves the problems of transmission delay, packet loss and database read-write performance bottleneck faced by existing electromagnetic radiation monitoring systems in large-scale equipment high-concurrency data transmission, significantly improving the efficiency and security of data transmission.

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

[0066] Figure 1 A flowchart of an online frequency selection data transmission method provided by the embodiments of the application is shown;

[0067] Figure 2 A software architecture diagram of an online frequency selection platform provided by the embodiments of the application is shown.

[0068] Figure 3 A system topology diagram of an electromagnetic radiation environment automatic monitoring system provided by an embodiment of the present application is shown.

[0069] Figure 4 A structural schematic diagram of an online frequency selection system provided by an embodiment of the present application is shown.

[0070] Figure 5 A structural schematic diagram of an online frequency selection platform provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0071] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific embodiments described herein are only used to explain and illustrate the embodiments of the present application and should not be used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present 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, etc.), the directional indications are only used to explain the relative positional relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly.

[0073] In addition, if the embodiments of the present application involve descriptions such as “first”, “second”, etc., the descriptions of “first”, “second”, etc. are only for description purposes and should not be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by “first”, “second” can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0074] Figure 1 A flowchart of an online frequency selection data transmission method according to an embodiment of the present application is shown schematically. As shown in the flowchart, the online frequency selection data transmission method according to the embodiment of the present application includes the following steps. Figure 1As shown, the 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, a group of intelligent hardware terminals and a client, the central control device is connected with the group of intelligent hardware terminals, 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, and the method can include the following steps.

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

[0076] S120, compensating the multi-band electromagnetic radiation data for environmental interference 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 into the plurality of physical nodes based on the data table, wherein the data type of the electromagnetic radiation data stored by 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 all data blocks of the encrypted electromagnetic radiation data set are stored in a classified manner, the analysis engine is used to decrypt each data block in a block manner, and the electromagnetic radiation mode and the target area of similar electromagnetic environment characteristics are determined based on the electromagnetic radiation data after the block decryption;

[0079] S150, in response to receiving the electromagnetic radiation mode and the target area returned by the online frequency selection platform, data interaction is performed between the electromagnetic radiation mode and the target area and the plurality of physical nodes, and a three-dimensional space distribution map of the electromagnetic field is generated through a preset map API and displayed through the client.

[0080] In the 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, etc. with a processor. Of course, the central control device can also be a server. The specific form of the central control device is not specially limited in the embodiment of the present application.

[0081] In the online frequency selection system, the intelligent hardware terminal group is the core component of data collection, responsible for collecting multi-band electromagnetic radiation data from multiple monitoring sites. Monitoring sites are usually distributed in different geographical locations, covering cities, rural areas, industrial areas and other environments 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 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, and the intensity of the induced current is proportional to the intensity of the electromagnetic waves. 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 by an embodiment of the application is shown, wherein the data collected by the intelligent hardware terminal group is sent to the site data collection server through the 4G / 5G communication module, the data collection server is used for data analysis of the received data, and the data is stored in the local database.

[0083] In this embodiment, the sensor is also equipped with a GPS module for recording the geographical position information of each monitoring site to ensure the geographical position accuracy of the data. In actual implementation, the intelligent hardware terminal group will automatically collect data at a predetermined time interval (e.g. 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 disturbed 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 the original data for environmental interference to improve the accuracy of the data. The process of environmental interference compensation first needs to obtain the current environmental data, including temperature, humidity, air pressure, wind speed, etc. meteorological parameters, as well as the height and density of surrounding buildings, etc. geographical information. These environmental data can be obtained through environmental sensors in the intelligent hardware terminal group or external weather stations. Next, a 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 the frequency of the electromagnetic wave, the propagation distance, the dielectric constant and conductivity of the air, etc. For example, for high-frequency electromagnetic waves, the humidity of the air has a greater impact on its attenuation, so the impact of humidity needs to be considered when calculating the attenuation coefficient. According to the calculated attenuation coefficient, the original electromagnetic radiation data is compensated.

[0085] Specifically, for each monitoring station's electromagnetic radiation data, its geographic location information is first determined, and then according to the installation position of the sensor and the geographic location information of the monitoring station, the straight-line distance of the electromagnetic wave from the sensor to the monitoring station is calculated using the distance calculation formula. The average value of all straight-line distances is calculated and taken as the propagation distance of the electromagnetic wave. Finally, the product of the attenuation coefficient and the propagation distance of the electromagnetic wave is calculated to obtain the attenuation amount of the electromagnetic wave in the air, and the original electromagnetic radiation data is compensated according to the attenuation amount. In this way, the influence of environmental factors on electromagnetic radiation data can be effectively eliminated, and the corrected electromagnetic radiation data set is 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 generation process of the data table 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 station. According to the above information, a data table containing all electromagnetic radiation data is generated, where each data item corresponds to a data type, timestamp and geographic location information.

[0087] Next, the data table is divided 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 the electromagnetic radiation data in the past period of time, and the alarm data sub-table is used to store the electromagnetic radiation data exceeding the preset threshold. The electromagnetic radiation data corresponding to each sub-table is 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 dispersing the data to multiple physical nodes (such as Figure 2 the data storage side of the station data shown), the read-write performance of the data can be effectively improved, and the load of a single node can be avoided. In addition, the data type stored in each physical node is consistent, which facilitates the classification management and fast query of the data. In actual implementation, distributed database technology such as Hadoop can be used to realize the dispersed storage and management of data.

[0088] In order to ensure the security of the electromagnetic radiation data during transmission, the corrected electromagnetic radiation dataset needs to be encrypted. The encryption process first uses a preset encryption algorithm (such as AES or RSA) to encrypt the electromagnetic radiation dataset, obtaining an encrypted electromagnetic radiation dataset. The encryption algorithm usually uses a symmetric key or an asymmetric key. The symmetric key encryption is fast and suitable for large data encryption; the asymmetric key has high 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 the symmetric key. Next, the current network bandwidth and network delay are obtained, and 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 the 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 the temporary key. The temporary key is bound to the encrypted electromagnetic radiation dataset, obtaining an encrypted electromagnetic radiation dataset. 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 dataset 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 reference bandwidth, obtaining a network bandwidth factor. The reciprocal of the product of the network bandwidth factor and the temporary key is calculated, obtaining a data block size coefficient. The dataset size of the encrypted electromagnetic radiation dataset is obtained, and the product of the dataset size and the data block size coefficient is used as the size threshold of each data block. Using a dynamic division strategy, the encrypted electromagnetic radiation dataset is dynamically divided into multiple data blocks, each of which has a size smaller than the size threshold. For each data block, a 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 to the cloud data center of the online frequency selection platform according to the unique classification identifier. After receiving the data blocks, the cloud data center stores the data blocks according to the unique classification identifier. The analysis engine decrypts each data block, decrypts the data block using the temporary key, and obtains the decrypted electromagnetic radiation data. The frequency spectrum features of the decrypted electromagnetic radiation data are extracted, and K-means clustering analysis is performed on the geographic location information and electromagnetic radiation data of all monitoring stations, obtaining k cluster centers. The average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center are calculated, and cluster centers with similar average electromagnetic radiation intensity and spatial distribution characteristics are merged, obtaining m target regions, where m is less than or equal to k. According to the spatial distribution characteristics, the boundaries of each target region are determined, obtaining target regions with similar electromagnetic environment characteristics. The frequency spectrum features are input into the pre-constructed CNN model to determine the electromagnetic radiation mode.

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

[0090] Next, according to the electromagnetic radiation pattern and target area, an electromagnetic radiation intensity distribution map is generated. The electromagnetic radiation intensity distribution map usually takes the form of a heat map, which represents the strength of electromagnetic radiation intensity through color depth. The process of generating a heat map first needs to convert electromagnetic radiation data into geographic spatial data, and then use interpolation algorithms (such as Kriging) to generate a continuous electromagnetic radiation intensity distribution map. The electromagnetic radiation intensity distribution map is dynamically corresponding to the geographic location data of multiple physical nodes to ensure the accuracy and real-time of the distribution map. Finally, combined with the preset map API (such as Google Maps or Baidu Map), a three-dimensional spatial distribution map of the electromagnetic field is generated. The three-dimensional spatial distribution map usually takes the form of a three-dimensional column chart or contour map, which represents the change of electromagnetic radiation intensity through height or color. The process of generating a three-dimensional spatial distribution map first needs to match the electromagnetic radiation intensity distribution map with the geographic coordinate system of the map API, and then use three-dimensional rendering technology (such as WebGL or Three.js) to generate three-dimensional graphics. The three-dimensional spatial distribution map is displayed through the client, and users can intuitively view the spatial distribution of the electromagnetic field and understand the electromagnetic radiation intensity and pattern of different areas.

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

[0092] The embodiment collects multi-band electromagnetic radiation data of multiple monitoring sites through intelligent hardware terminal groups, and compensates the data for environmental interference to ensure the accuracy and reliability of the data, providing a high-quality data basis for subsequent analysis. Secondly, the dispersed storage strategy is adopted, and the corrected electromagnetic radiation data is dispersed and stored in multiple physical nodes according to the data type, avoiding the I / O load pressure of centralized databases in high-concurrency data transmission, thereby greatly improving the read-write performance and data storage efficiency of the database. 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, not only enhancing the security of data transmission, but also relieving the congestion problem of data transmission channels 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, and determines the electromagnetic radiation mode and the target area of 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 interacting the analysis results with 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 visual support. In summary, the problems of transmission delay, packet loss and database read-write performance bottleneck faced by existing electromagnetic radiation monitoring systems in large-scale equipment high-concurrency data transmission are effectively solved, and the efficiency and security of data transmission are significantly improved.

[0093] In one embodiment of the present embodiment, the multi-band electromagnetic radiation data is compensated for environmental interference to correct the multi-band electromagnetic radiation data, to obtain a corrected electromagnetic radiation data set, including the following steps:

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

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

[0096] During the collection of electromagnetic radiation data, environmental factors have a significant impact on the accuracy of the data. To eliminate these effects, it is first necessary to obtain current environmental data, including temperature, humidity, air pressure, wind speed, and other meteorological parameters, as well as 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 weather stations. For example, a temperature sensor can measure the current air temperature, a humidity sensor can measure the water vapor content in the air, an air pressure sensor can measure atmospheric pressure, and a wind speed sensor can measure the speed of air flow. In addition, geographic information can be obtained through a geographic information system (GIS). Next, a pre-set 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, etc. For example, for high-frequency electromagnetic waves, the humidity of the air has a greater impact on their attenuation, so the impact of humidity needs to be considered when calculating the attenuation coefficient. The electromagnetic wave attenuation formula can be expressed as:

[0097]

[0098] where 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 the air, ∈' is the real part of the dielectric constant of the air, and ∈" is the imaginary part of the dielectric constant of the air. In actual calculations, the real and imaginary parts of the dielectric constant of the air can be calculated based on 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 the air is approximately 1.0006, and the imaginary part is approximately 0.0001. By substituting these parameters into the formula, the attenuation coefficient of the electromagnetic wave in the air can be calculated. For example, for an electromagnetic wave with a frequency of 1 GHz, under the conditions of a temperature of 25°C and a humidity of 60%, the attenuation coefficient is approximately 0.001 dB / m.

[0099] After calculating the attenuation coefficient of the electromagnetic wave in the air, environmental interference compensation is needed for the multi-band electromagnetic radiation data based on the coefficient to correct the original data. The process of environmental interference compensation first needs to determine 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, longitude, and height of the sensor. Finally, the straight-line distance of the electromagnetic wave from the sensor to the monitoring site is calculated using the distance calculation formula. 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 position coordinate of the sensor, and (x2, y2, z2) is the geographic position coordinate of the monitoring site. For example, if the installation position coordinate of the sensor is (10, 20, 5) and the geographic position coordinate of the monitoring site is (15, 25, 0), the propagation distance of the electromagnetic wave is:

[0102]

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

[0104]

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

[0106] Attenuation amount = 0.001 x 8.5 = 0.0085 dB.

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

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

[0109] The present embodiment can significantly improve the accuracy and reliability of the data by compensating for environmental interference on multi-band electromagnetic radiation data. First, the current environmental data is obtained and the electromagnetic wave attenuation coefficient is calculated, which can scientifically quantify the influence of environmental factors on electromagnetic radiation data. Second, the original data is compensated according to the attenuation coefficient, which can effectively eliminate environmental interference and obtain a more accurate electromagnetic radiation data set. The corrected data not only truly reflects the actual situation of electromagnetic radiation, but also provides a reliable basis for subsequent data processing and analysis.

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

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

[0112] S320, for each electromagnetic radiation data, determine the geographical position information of the monitoring station where it is located;

[0113] S330, according to the installation position and the geographical position information of the monitoring station, calculate the straight-line distance of electromagnetic wave from the sensor to the monitoring station by using the distance calculation formula;

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

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

[0116] S360, according to the attenuation amount, compensate the multi-frequency electromagnetic radiation data to obtain the corrected electromagnetic radiation data set.

[0117] In the intelligent hardware terminal group, the installation position of each sensor is one of the key parameters for environmental interference compensation calculation. The installation position of the sensor usually includes its latitude and longitude coordinates, height, orientation and other information. These information can be obtained by GPS module, laser range finder or other positioning devices. For example, the GPS module can record the latitude and longitude coordinates of the sensor in real time, the laser range finder can measure the height difference between the sensor and the ground or other reference points, and the orientation information can be obtained by electronic compass. In actual implementation, the installation position information of the sensor is usually accurately measured at the time of installation and stored in the local database or central control device for subsequent calling. For example, assuming that there are 10 sensors in the intelligent hardware terminal group, their installation position coordinates are (10, 20, 5), (15, 25, 6), (20, 30, 7) and so on, where the first two numbers represent the latitude and longitude coordinates, and the third number represents the height. By obtaining these installation position information, accurate basic data can be provided for subsequent distance calculation. In addition, the installation position 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 position of a sensor changes, the installation position information can be re-measured and updated to ensure that subsequent data processing and analysis are not affected.

[0118] After obtaining the installation position information of the sensor, the geographical position information of the monitoring station where each electromagnetic radiation data is located needs to be determined. The geographical position information of the monitoring station usually includes its latitude and longitude coordinates, height and other information. These information can be obtained by GPS module, laser range finder or other positioning devices. In actual implementation, the geographical position information of the monitoring station is usually accurately measured at the time of installation and stored in the local database or central control device for subsequent calling.

[0119] After obtaining the installation location of the sensor and the geographic location information of the monitoring site, the straight-line distance of the electromagnetic wave from the sensor to the monitoring site needs to be calculated. The calculation of the straight-line distance usually uses the distance calculation formula in three-dimensional space. For details, refer to the description of S220, which will not be repeated here.

[0120] In actual implementation, an automatic script or software tool 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 calling. For example, assuming that there are 10 sensors and 5 monitoring sites in the intelligent hardware terminal group, 50 distance values need to be calculated. By batch calculation, the calculation efficiency can be greatly improved, and the workload of manual operation can be reduced.

[0121] After calculating the straight-line distances between all sensors and monitoring sites, the average value of these distances needs to be calculated and used as the propagation distance of the electromagnetic wave. The average value is usually calculated by the arithmetic mean method, that is, all distance values are added and then divided by the total number of distance values.

[0122] After calculating the average propagation distance of the electromagnetic wave, the attenuation amount of the electromagnetic wave in the air needs to be calculated according to the attenuation coefficient. The attenuation amount is the product of the attenuation coefficient and the propagation distance of the electromagnetic wave, which can be expressed as:

[0123] Attenuation amount = a x d;

[0124] Where a is the attenuation coefficient, and d is the propagation distance of the electromagnetic wave.

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

[0126] The present embodiment can significantly improve the accuracy and reliability of the data by compensating the multi-band electromagnetic radiation data for environmental interference. First, the installation location of the sensor and the geographic location information of the monitoring site are obtained, which can provide accurate basic data for subsequent distance calculation. Second, the straight-line distance of the electromagnetic wave from the sensor to the monitoring site is calculated using the distance calculation formula, which can accurately quantify the propagation distance of the electromagnetic wave. Then, the average value of all straight-line distances is calculated and used as the propagation distance of the electromagnetic wave, which can further improve the accuracy of the calculation. Then, the attenuation amount of the electromagnetic wave in the air is calculated according to the attenuation coefficient, which can scientifically quantify the influence 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 embodiment of the present embodiment, a data table is generated from the corrected electromagnetic radiation data set, and the corrected electromagnetic radiation data is stored in multiple physical nodes based on the data table, including the following steps:

[0128] S410, generating a data table containing data types, timestamps, and geographic location information from 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, a timestamp, and geographic location information;

[0129] S420, dividing the data table into multiple sub-tables according to the data type, and storing the electromagnetic radiation data corresponding to each sub-table in the corresponding physical node, 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 to facilitate subsequent storage and query. The generation process of the data table 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. According to these information, a data table containing all electromagnetic radiation data is generated, wherein each data item corresponds to a data type, a timestamp, and geographic location information. For example, assuming that the corrected electromagnetic radiation data set contains 1000 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 represented as:

[0131] Data type Time stamp Geolocation 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 to facilitate subsequent storage and query. In actual implementation, a database management system (such as MySQL or PostgreSQL) can be used to generate and manage the data table. For example, SQL statements can be used to create data tables and insert corrected electromagnetic radiation data into data tables. 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 generating the data table, it is necessary to divide the data table into multiple sub-tables according to data types, and store the corresponding electromagnetic radiation data of each sub-table in the corresponding physical node. 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 the electromagnetic radiation data in the past period of time, and the alarm data sub-table is used to store the electromagnetic radiation data exceeding the preset threshold. For example, assuming that the data table contains 1000 data items, of which 500 are real-time data, 300 are historical data, and 200 are alarm data, the data table can be divided into three sub-tables:

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

[0135] 2. Historical data sub-table: contains 300 historical data items, each data item has a data type of "historical electromagnetic radiation intensity", a timestamp of the collection time in the past period of time, and geographic location information of the monitoring site's latitude and longitude coordinates.

[0136] 3. Alarm data sub-table: contains 200 alarm data items, each data item has a data type of "alarm electromagnetic radiation intensity", a timestamp of the collection time exceeding the preset threshold, and geographic location information of the monitoring site's latitude and longitude coordinates.

[0137] In this way, the data table can be divided into multiple sub-tables, which facilitates subsequent classification management and quick query. In actual implementation, distributed database technology (such as Hadoop) can be used to realize the distributed storage and management of data. For example, the real-time data sub-table can be stored in one physical node, the historical data sub-table can be stored in another physical node, and the alarm data sub-table can be stored in a third physical node. In this way, the read-write performance of the data can be effectively improved, and the load of a single node can be avoided. In addition, the data types stored in each physical node are consistent, which facilitates the classification management and quick query of the data.

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

[0139] In one embodiment of the present embodiment, the corrected electromagnetic radiation data set is encrypted to obtain an encrypted electromagnetic radiation data set, including 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 the current network bandwidth and the current network delay;

[0142] S530, calculating a 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 the temporary key generation parameter and using the first hash value as the temporary key;

[0144] S550, binding the temporary key with the encrypted electromagnetic radiation data set to obtain an encrypted electromagnetic radiation data set.

[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, suitable for large data encryption; RSA (Asymmetric Encryption Algorithm) has higher security, suitable for key distribution and management. In actual implementation, a hybrid encryption method can be used, i.e. using a symmetric key to encrypt data and using an asymmetric key to encrypt the symmetric key. For example, first use the AES algorithm to generate a symmetric key, then use the symmetric key to encrypt the electromagnetic radiation data set. Next, use the RSA algorithm to encrypt the symmetric key, and bind the encrypted symmetric key with the encrypted electromagnetic radiation data set. In this way, the security of data during transmission and storage can be ensured, even if the data is intercepted, it cannot be decrypted.

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

[0147] After the encrypted electromagnetic radiation dataset is generated, the current network bandwidth and network delay need to be obtained to provide parameters for subsequent temporary key generation. Network bandwidth generally represents the ability of the network to transmit data, with units of Mbps (megabits per second); network delay represents the transmission time of data from the sending end to the receiving end, with units of 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, temporary key generation parameters need to be calculated based on these parameters. Temporary key generation parameters are 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 represented as:

[0149]

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

[0151] After calculating the temporary key generation parameter, the first hash value of the parameter needs to be calculated, and the hash value is used as the temporary key. The calculation of the hash value usually uses 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, using the SHA-256 algorithm to calculate the hash value of the temporary key generation parameter can be represented as:

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

[0153] For example, assuming that the temporary key generation parameter is 3392313600000, its hash value is calculated using the SHA-256 algorithm, a 256-bit binary number is obtained, which is converted into hexadecimal representation, that is, the temporary key. For example, if 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 the hashlib library of Python) 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, the key needs to be bound with the encrypted electromagnetic radiation dataset to obtain the encrypted electromagnetic radiation dataset. The binding process usually uses 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 the metadata of the encrypted electromagnetic radiation dataset, stored in the header or tail of the dataset. In this way, the temporary key can be bound with the encrypted electromagnetic radiation dataset, ensuring the security of the data during transmission and storage. In actual implementation, a data binding library (such as the struct library of Python) can be used to implement the data binding algorithm. For example, the struct library can be used to bind the temporary key with the encrypted electromagnetic radiation dataset.

[0155] The present embodiment can significantly improve the security and transmission efficiency of the data by using a preset encryption algorithm to encrypt the corrected electromagnetic radiation dataset and generating a temporary key bound with the encrypted dataset. First, the electromagnetic radiation dataset is encrypted using an encryption algorithm such as AES or RSA, which can ensure the security of the data during transmission and storage. Second, the current network bandwidth and network delay are obtained, and the temporary key generation parameter is calculated based on these parameters, which can provide a scientific basis for the generation of the temporary key. Then, the hash value of the temporary key generation parameter 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 with the encrypted electromagnetic radiation dataset, which can 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 embodiment of the present embodiment, the dynamic block transmission strategy includes the following steps:

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

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

[0159] S630, obtain the data set size of the encrypted electromagnetic radiation data set, and take the product of the data set size and the data block size coefficient as a size threshold of each data block, and dynamically divide the encrypted electromagnetic radiation data set into a plurality of data blocks by using a dynamic division strategy, wherein the size of each data block is less 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 take the second hash value as a unique classification identifier of the data block;

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

[0162] The dynamic division strategy comprises:

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

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

[0165] S3, calculate the average size of the divided data block;

[0166] S4, if the average size is greater than 90% of the size threshold and less than the size threshold, keep the current data block size 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 the encrypted electromagnetic radiation data set is completely divided.

[0170] In the dynamic block transmission strategy, the network bandwidth factor is one of the key parameters for determining the data block size. The network bandwidth factor is obtained by calculating the ratio of the current network bandwidth to the preset reference bandwidth. The preset reference bandwidth is usually a fixed value, representing the transmission capacity of the network in an ideal state, for example, 100 Mbps. The current network bandwidth can be obtained in real time by network monitoring tools.

[0171] After calculating the network bandwidth factor, the data block size coefficient needs to be calculated based on the factor and the temporary key. The data block size coefficient is another key parameter that determines the size of the data block, and is obtained by calculating the reciprocal 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 numerical value of the temporary key is 1234567890 and the network bandwidth factor is 0.8, then the data block size coefficient is:

[0172]

[0173] After calculating the data block size coefficient, the size threshold of each data block needs to be calculated based on the coefficient and the size of the encrypted electromagnetic radiation data set. The size of the data set is usually measured in bytes and can be obtained through a file system or a database management system. For example, assuming the size of the encrypted electromagnetic radiation data set is 1GB (1,073,741,824 bytes) and 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 ≈ 1.086MB.

[0175] In this way, the size threshold of each data block can be accurately calculated. In actual implementation, a dynamic division strategy can be adopted to dynamically divide the encrypted electromagnetic radiation data set into multiple data blocks. The dynamic division strategy first sets the initial data block size to the size threshold (1.086 MB), and then reads the data in the encrypted electromagnetic radiation data set in turn. When the cumulative data amount 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 case where the cumulative data amount of a data block is much lower than the initial data block size. In this case, the cumulative data amount of the data block is the data amount itself. This situation is caused by the following reasons: 1. During the division of data blocks, 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 data block is divided, the remaining data amount 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 10.86 MB, and the initial data block size is 1.086 MB, theoretically, 10 complete data blocks (each 1.086 MB) can be divided. However, if the total size of the data set is 10.86 MB + 0.5 MB = 11.36 MB, the size of the last data block is only 0.5 MB, which is much lower than the initial data block size (1.086 MB). In this case, the cumulative data amount of the last data block is its own data amount (0.5 MB). 2. The data distribution in the encrypted electromagnetic radiation data set may not be uniform, and the cumulative data amount of some data blocks may be much lower than the initial data block size. For example, assuming that the data in the data set is sparse in some areas, resulting in that the cumulative data amount of some data blocks cannot reach the initial data block size when dividing data blocks. In this case, even if the total size of the data set is large enough, the cumulative data amount of some data blocks may still be much lower than the initial data block size.

[0176] After dividing the encrypted electromagnetic radiation data set 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 time stamp and the geographic location information. The time stamp usually represents the specific time of data collection, such as "2023-10-01 12:00:00", which can be converted into a numerical value for calculation. The geographic location information usually represents 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 numerical value of the time stamp is 1696156800000, and the numerical value of the geographic location information is 116.3975 x 39.9087 ≈ 4645.67.

[0177] Next, the second hash value is calculated, which can be represented as:

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

[0179] In actual implementation, a hash library (such as the hashlib library of Python) 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 the unique classification identifier of each data block, it is necessary to bind the identifier with the corresponding data block and transmit all data blocks according to the unique classification identifier to the cloud data center of the online frequency selection platform one by one. The binding process usually uses a data binding algorithm that can associate the unique classification identifier with the data block. For example, the unique classification identifier can be stored as metadata of the data block in the header or tail of the data block. In this way, the unique classification identifier can be bound with the data block, ensuring the integrity and traceability of the data during transmission and storage.

[0181] Due to the uneven distribution of data in the encrypted electromagnetic radiation data set, there are cases where the cumulative data volume of some data blocks is close to but does not completely reach the current data block size threshold, or the cumulative data volume of some data blocks is far from reaching the current data block size threshold, which may cause the cumulative data volume of the data blocks to be close to but not completely reach (far from reaching) the current data block size threshold during the division of the data blocks, and thus the average size is too low or deviates 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 data set needs to be read in sequence, and the data whose cumulative data volume reaches the current data block size is divided into a data block. The reading process usually uses a streaming reading method, i.e., reading data byte by byte or block by block, to reduce memory usage. In this way, the encrypted electromagnetic radiation data set 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, the average size of the divided data blocks needs to be calculated to evaluate the rationality of the data block size. The calculation of the average size usually uses the arithmetic mean method, i.e., 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 according to 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 is kept unchanged. For example, assuming the size threshold is 1.086 MB (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 is kept unchanged. In this way, it can be ensured that the size of the data block is neither too large to cause transmission delay in network transmission, nor too small to cause low transmission efficiency.

[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 1.086 MB (1,086,000 bytes) and the average size is 900,000 bytes, 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%, i.e. the new data block size is:

[0186] The new data block size = 1,086,000 x 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 the size threshold is 1.086 MB (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 the entire encrypted electromagnetic radiation dataset is divided. The repeated process includes reading data, dividing data blocks, calculating average size, adjusting data block size, and other steps to ensure that the size of the data block is neither too large to cause transmission delay nor too small to cause low transmission efficiency in network transmission. Through the dynamic division strategy, the efficiency and flexibility of data transmission can be significantly improved. First, set the initial data block size to the size threshold, which can provide a scientific starting point for data division. Second, read the data and divide the data block, which can ensure that the size of the data block is neither too large to cause transmission delay nor too small to cause low transmission efficiency in network transmission. Next, calculate the average size of the divided data block, which can evaluate the rationality of the data block size. Then, adjust the data block size according to the relationship between the average size and the size threshold, which can optimize the efficiency of data transmission. Finally, repeat the above steps until the entire dataset is divided, which can ensure that the size of the data block always remains optimal in network transmission. In this way, a large amount of encrypted electromagnetic radiation data can be efficiently divided and managed.

[0189] First, the current network bandwidth and the preset reference bandwidth are calculated to obtain the network bandwidth factor, which can provide a scientific basis for the division of data block size. Second, the reciprocal 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. Next, a dynamic division strategy is adopted to dynamically divide the encrypted electromagnetic radiation dataset 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 this 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 embodiment of the present embodiment, the analysis engine decrypts each data block and determines the target area of the electromagnetic radiation pattern and similar electromagnetic environment features based on the decrypted electromagnetic radiation data, including the following steps:

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

[0192] S720, extract the frequency spectrum features of the decrypted electromagnetic radiation data;

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

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

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

[0196] S760, according to the spatial distribution characteristics, the boundary of each target region is determined to obtain a target region with similar electromagnetic environment characteristics;

[0197] S770, the spectral characteristics are input into the pre-constructed CNN model to determine the electromagnetic radiation mode.

[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 is used for encryption, the AES algorithm also needs to 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 electromagnetic radiation data is decrypted, its spectral characteristics need to be extracted for further analysis. The spectral characteristics usually include the frequency distribution, intensity distribution, and peak frequency of electromagnetic radiation. The process of extracting spectral characteristics 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, each with an electromagnetic radiation intensity of-49.9915 dBm and a frequency range of 1 GHz, the FFT algorithm can be used to convert these data into frequency-domain signals to obtain the spectral characteristics.

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

[0201] After obtaining k cluster centers, the average electromagnetic radiation intensity and spatial distribution characteristics of each cluster center need to be calculated. The average electromagnetic radiation intensity generally represents the average electromagnetic radiation level of the area represented by the cluster center, and the spatial distribution characteristics represent the electromagnetic radiation distribution of the area. For example, assuming that cluster center 1 contains 3 monitoring sites with electromagnetic radiation intensities of -49.9915 dBm, -50.0123 dBm, and -50.0331 dBm, the average electromagnetic radiation intensity is:

[0202]

[0203] The spatial distribution characteristics can be obtained by calculating the geographical location information and the variance of the electromagnetic radiation intensity of the cluster center. 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 represented 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, the 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 pre-set threshold (e.g., 0.0002), the two cluster centers are merged into a 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, the boundaries of each target area need to be determined according to the spatial distribution characteristics to obtain target areas with similar electromagnetic environment characteristics. Boundary determination usually uses the convex hull algorithm. For example, assuming that target area 1 contains 3 monitoring sites with geographical location information (116.3975, 39.9087), (116.3976, 39.9088), and (116.3977, 39.9089), the convex hull algorithm can be used to determine the boundary of the target area. For example, the convex hull algorithm can be represented as:

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

[0207] 2. From this point, find the next point in the clockwise direction so that all other points are on the right side of this point.

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

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

[0210] After determining the target region, the spectral features are input into a pre-constructed CNN model to determine the electromagnetic radiation pattern. CNN (Convolutional Neural Network) is a commonly used deep learning model suitable for processing image and spectral data. The pre-constructed 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 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] The present embodiment can significantly improve the accuracy and efficiency of electromagnetic environment monitoring by analyzing the engine for each data block, and determining the target region of the electromagnetic radiation pattern and similar electromagnetic environment features based on the decrypted electromagnetic radiation data. First, the data block is decrypted using a temporary key, which can restore the original electromagnetic radiation data and ensure data integrity and security. Second, the spectral features are extracted, which can provide a scientific basis for subsequent electromagnetic radiation pattern analysis. Next, the K-means clustering analysis is used to determine the cluster center, 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 subsequent target region merging. Next, the cluster centers with similar characteristics are merged to accurately determine the target region. Finally, the CNN model is used to determine the electromagnetic radiation pattern, which can provide a scientific basis for electromagnetic environment monitoring.

[0212] In one embodiment of the present embodiment, the electromagnetic radiation pattern and the target region are interacted with a plurality of physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated and displayed through a pre-set map API, including the following steps:

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

[0214] S820, dynamically corresponding the electromagnetic radiation intensity distribution map with the geographical position data of the plurality of physical nodes, and generating a three-dimensional spatial distribution map of the electromagnetic field in combination with a pre-set map API.

[0215] After determining the electromagnetic radiation pattern and the 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 represents the strength of electromagnetic radiation intensity through color depth. The process of generating a heat map first requires converting 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 a heat map. For example, ArcGIS software can be used for interpolation analysis of electromagnetic radiation data.

[0216] After generating the electromagnetic radiation intensity distribution map, it needs to be dynamically corresponding with the geographic location data of multiple physical nodes, and combined with the preset map API to generate a three-dimensional spatial distribution map of electromagnetic field. The process of dynamic correspondence 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 coordinate matching algorithm can be used to match the geographic location information of these physical nodes with the geographic coordinates of the electromagnetic radiation intensity distribution map. 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 realized.

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

[0218] The embodiment can significantly improve the intuitiveness and accuracy of electromagnetic environment monitoring by interacting data between electromagnetic radiation pattern and target area and multiple physical nodes, and generating a three-dimensional spatial distribution map of electromagnetic field in combination with a preset map API. First, generating an electromagnetic radiation intensity distribution map based on electromagnetic radiation pattern and target area can provide a scientific basis for subsequent three-dimensional spatial distribution map generation. Second, dynamically corresponding the electromagnetic radiation intensity distribution map with the geographic location data of the physical nodes can accurately realize data matching and superimposition. Finally, generating a three-dimensional spatial distribution map in combination with a preset map API can intuitively display the spatial distribution of 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 embodiment of the present application also provides an online frequency selection system, as shown in the accompanying drawings, comprising: Figure 4

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

[0221] A group of intelligent hardware terminals, connected with the central control device.

[0222] The embodiment of the present application also provides an online frequency selection platform, as shown in the accompanying drawings, comprising: Figure 5

[0223] A cloud data center; and

[0224] An analysis engine.

[0225] Those skilled in the art should understand that the embodiment of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a 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.) containing computer usable program codes.

[0226] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (system), computer program product according to the embodiment of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for realizing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The devices for realizing the functions specified in one or more flows and / or blocks.

[0227] These computer program instructions can also be stored in a computer readable storage medium, which can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which realize the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The devices for realizing the functions specified in one or more flows and / or blocks.

[0228] ​​These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

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

[0230] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory is an example of computer readable storage media.

[0231] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as 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 programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0232] It should also be noted that the terms "comprising", "comprises", "including", "includes" or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article or apparatus that includes the element.

[0233] ​​The above merely provides an example of the present application, and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall fall into the scope of claims of the present application.

Claims

1. An online frequency-selective data transmission method, characterized in that, An online frequency selection system is applied, comprising a central control device, a group of intelligent hardware terminals, and a client. The central control device is connected to the group of intelligent hardware terminals. The central control device includes a local database, which includes at least one physical node. The central control device is also equipped with an online frequency selection platform, which includes a cloud data center and an analysis engine. The method includes: Multi-band electromagnetic radiation data from multiple monitoring stations are collected through the aforementioned intelligent hardware terminal group. Environmental interference compensation is performed on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data and obtain a corrected electromagnetic radiation dataset. A data table is generated based on the corrected electromagnetic radiation dataset, and the corrected electromagnetic radiation data is distributed and stored in multiple physical nodes based on the data table, wherein the data type of the electromagnetic radiation data stored in each physical node is consistent. The encrypted electromagnetic radiation dataset is obtained by encrypting and correcting the electromagnetic radiation dataset. The encrypted electromagnetic radiation dataset is then sent to the online frequency selection platform through a dynamic block transmission strategy. The cloud data center is used to receive the data blocks of the encrypted electromagnetic radiation dataset and classify and store all the data blocks of the encrypted electromagnetic radiation dataset. The analysis engine is used to decrypt each data block and determine the electromagnetic radiation mode and target areas with similar electromagnetic environment characteristics based on the decrypted electromagnetic radiation data. 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 exchanged 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 step of performing environmental interference compensation on the multi-band electromagnetic radiation data to correct the multi-band electromagnetic radiation data and obtain a corrected electromagnetic radiation dataset includes: Acquire current environmental data and use a preset electromagnetic wave attenuation formula to calculate the attenuation coefficient of electromagnetic waves in the air based on the current environmental data; Environmental interference compensation is performed on the multi-band electromagnetic radiation data based on the attenuation coefficient to correct the multi-band electromagnetic radiation data and obtain the corrected electromagnetic radiation dataset.

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. 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, resulting in a corrected electromagnetic radiation dataset, including: Obtain the installation location of each sensor in the smart hardware terminal group; For each electromagnetic radiation data point, determine the geographical location information of the monitoring station where it is located; Based on the installation location and the geographical location information of the monitoring station, the straight-line distance of the electromagnetic wave from the sensor to the monitoring station is calculated using a distance calculation formula; Calculate the average of all the stated straight-line distances and use the average as the electromagnetic wave propagation distance; The attenuation coefficient is calculated by multiplying it by the propagation distance of the electromagnetic wave to obtain the attenuation of the electromagnetic wave in the air. Based on the attenuation amount, the multi-band electromagnetic radiation data is compensated to obtain a corrected electromagnetic radiation dataset.

4. The method according to claim 3, characterized in that, The step of generating a data table based on the corrected electromagnetic radiation dataset and distributing the corrected electromagnetic radiation data to multiple physical nodes based on the data table includes: Based on the corrected electromagnetic radiation dataset, a data table containing data type, timestamp, and geographic location information is generated. The data table includes all electromagnetic radiation data in the corrected electromagnetic radiation dataset, 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 distributed and stored in the corresponding physical nodes. 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 yields an encrypted electromagnetic radiation dataset, including: The corrected electromagnetic radiation dataset is encrypted using a preset encryption algorithm to obtain an encrypted electromagnetic radiation dataset. Get the current network bandwidth and current network latency; Based on the current network bandwidth and the current network latency, the temporary key generation parameters are calculated using a preset parameter calculation formula. Calculate the first hash value of the temporary key generation parameters, and use the first hash value as the temporary key; The temporary key is bound to the encrypted electromagnetic radiation dataset to obtain the encrypted electromagnetic radiation dataset.

6. The method according to claim 5, characterized in that, The dynamic chunking transmission strategy includes: Calculate the ratio of the current network bandwidth to the preset baseline bandwidth to obtain the network bandwidth factor; Calculate the reciprocal of the product of the network bandwidth factor and the temporary key to obtain the data block size coefficient; The size of the encrypted electromagnetic radiation dataset is obtained, and the product of the dataset size and the data block size coefficient is used as the size threshold of each data block. A dynamic partitioning strategy is adopted to dynamically divide the encrypted electromagnetic radiation dataset into multiple data blocks, wherein the size of each data block is smaller than the size threshold. For each data block, calculate a second hash value for the product of the timestamp and the geographic location information, and use the second hash value as a unique classification identifier for the data block; The unique classification identifier is bound to the corresponding data block, and all the data blocks are transmitted 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: Set the initial data block size to the aforementioned size threshold; The data in the encrypted electromagnetic radiation dataset is read sequentially. When the accumulated data volume reaches the current data block size, the read data is divided 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 but less than the size threshold, then the current data block size remains unchanged; If the average size is less than 90% of the size threshold, then the current data block size is increased by 10%. If the average size is equal to the size threshold, then the current data block size is reduced by 5%. Repeat the above steps until the encrypted electromagnetic radiation dataset is completely divided.

7. The method according to claim 5, characterized in that, The analysis engine decrypts each data block and determines target areas with electromagnetic radiation patterns and similar electromagnetic environment characteristics based on the decrypted electromagnetic radiation data, including: The analysis engine uses the temporary key to decrypt each data block to obtain the decrypted electromagnetic radiation data. Extract the spectral characteristics of the electromagnetic radiation data after block decryption; K-means clustering analysis was performed on the geographical location information of all the monitoring stations 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; Cluster centers with similar average electromagnetic radiation intensity and spatial distribution characteristics are merged to obtain m target regions, where m is less than or equal to k; Based on the spatial distribution characteristics, the boundary of each target region is determined to obtain target regions 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 interacting with multiple physical nodes to exchange data on the electromagnetic radiation pattern and the target area, and generating and displaying a three-dimensional spatial distribution map of the electromagnetic field through a preset map API, includes: Based on the electromagnetic radiation pattern and the target area, an electromagnetic radiation intensity distribution map is generated; The electromagnetic radiation intensity distribution map is dynamically mapped to the geographical location data of multiple physical nodes, and a three-dimensional spatial distribution map of the electromagnetic field is generated by combining the map 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 1-8, characterized in that, include: The central control device includes a local database, the local database including at least one physical node, and the central control device is equipped with an online frequency selection platform; A group of intelligent hardware terminals 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 1-8, characterized in that, include: Cloud data centers; as well as Analysis engine.

Images