Real-time communication and data interaction method for coordinated operation of energy storage power station and power grid dispatching

Abstract

The application discloses a real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching, and relates to the technical field of data processing.The application solves the technical problems that transmission rate control is only dependent on simple indexes and fixed adjustment mode, cannot adapt to dynamic changes of network load, and has data redundancy and low transmission efficiency in the transmission process.The application can more flexibly and accurately adjust the data transmission rate according to the real-time changes of the network load, improve the data transmission efficiency and network utilization, and guarantee the stability and smoothness of data transmission by constructing a multi-dimensional load monitoring system, comprehensively considering indexes such as CPU usage, memory usage, network bandwidth utilization and packet loss rate, and processing the data through binary conversion, segmentation, recombination and encryption, classifying and recombining the data according to the total sum of binary numbers, combining parity encryption, and enhancing data security; and adding meta information header to integrate data and optimize the transmission structure.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a real-time communication and data interaction method for the coordinated operation of energy storage power stations and power grid dispatch. Background Technology

[0002] In scenarios where energy storage power stations and power grid dispatch operate in coordination, real-time communication and data interaction are the key foundations for achieving efficient collaborative control. With the development of intelligent and digital power systems, the types of data generated by energy storage power stations are becoming increasingly complex, including equipment operating status data, power regulation data, and fault alarm data. Power grid dispatch also has increasingly higher requirements for the real-time performance, accuracy, and reliability of data transmission.

[0003] According to patent application CN109039851A, an interactive data processing method, apparatus, computer device, and storage medium are disclosed. The method includes: real-time interaction through virtual session members in a virtual session scenario; obtaining a trigger instruction for an asynchronous message; interrupting the real-time interaction when the asynchronous message acquisition method corresponding to the trigger instruction is mutually exclusive with the real-time interaction; obtaining an asynchronous message for playback in the virtual session scenario according to the trigger instruction; sending the obtained asynchronous message; and resuming the interrupted real-time interaction. This integrates real-time interaction and asynchronous messaging, enabling users to increase the amount of information conveyed during real-time interactive communication by sending asynchronous interactive messages.

[0004] Traditional data exchange methods often employ fixed transmission strategies and simple classification approaches, making them ill-suited to complex and ever-changing network environments and diverse data demands. In communication channel management, transmission rate adjustments are typically based on a single metric, lacking multi-dimensional monitoring and dynamic optimization of network load. Data classification fails to adequately consider the real-time characteristics of data and differences in business requirements, resulting in insufficient priority for critical data transmission. Pre-transmission processing is often rudimentary, lacking in-depth data segmentation, reassembly, and encryption, thus failing to effectively guarantee data security and transmission efficiency. These issues limit the synergistic effect between energy storage power stations and grid dispatch, making it difficult to meet the requirements of stable operation and efficient dispatching in new power systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a real-time communication and data interaction method for the coordinated operation of energy storage power stations and power grid dispatching. This method solves the problems of relying solely on simple indicators and fixed adjustment methods for transmission rate control, which cannot adapt to dynamic changes in network load, and results in data redundancy and low transmission efficiency during transmission.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a real-time communication and data interaction method for the coordinated operation of energy storage power stations and power grid dispatching, the method specifically including the following steps:

[0007] The transmission energy scale corresponding to the same type of communication data is calculated based on historical data, and the judgment index is obtained by combining the average value. At the same time, it is compared with the preset value to classify and obtain priority communication data and secondary communication data.

[0008] The system determines the real-time load of the communication channel, generates information to reduce or increase transmission speed, and performs linear adjustment processing based on real-time communication data to generate data communication information.

[0009] The real-time communication data after classification is further classified to obtain real-time and periodic data. At the same time, an interaction strategy is formulated, interaction strategy information is generated, and the data is evenly distributed according to its corresponding data capacity to obtain an evenly distributed data stream.

[0010] The evenly divided data stream is converted into a binary stream to obtain a converted evenly divided data stream, and then divided into secondary segments in sequence. Secondary segments with the same sum of binary numbers are marked as segments with the same value.

[0011] The data streams are combined based on the parity of their number of equal-sized data streams to generate a combined package. These combined packages are then combined to generate a recombined data stream.

[0012] As a further aspect of the present invention, the specific method for obtaining the judgment index is as follows:

[0013] Classify historical communication data by type to obtain a set of communication data of the same type, denoted as i, where i = 1, 2, ..., j, and j represents the number of types of communication data of the same type.

[0014] For each type i, count the number of transmissions Ci within the time window T, and obtain the single transmission amount L. Ci According to the formula Calculate the transmission energy scale K i Repeat the calculation for m time windows T to obtain m K values. i Values, and calculate m transmission energy scales K. i The average value is used as the criterion for judging the same type of communication data i.

[0015] As a further aspect of the present invention, the specific method for comparing and classifying the data with preset values ​​to obtain priority communication data and secondary communication data is as follows:

[0016] The obtained judgment index is compared with the preset value, and the specific value of the preset value is set by the operator. The same type of communication data with the judgment index greater than the preset value is classified as priority communication data, and the same type of communication data with the judgment index less than the preset value is classified as secondary communication data.

[0017] As a further aspect of the present invention, the specific method for generating the reduced or increased transmission information is as follows:

[0018] Real-time communication data is acquired and classified into priority communication data and secondary communication data. Then, the real-time load corresponding to the communication channel is acquired and compared with the load threshold.

[0019] If the real-time load is greater than the load threshold, a slowdown transmission message is generated; conversely, if the real-time load is less than the load threshold, a speed-up transmission message is generated.

[0020] As a further aspect of the present invention, the specific method for generating data communication information is as follows:

[0021] Next, a linear adjustment algorithm is selected based on the generated deceleration or acceleration transmission information for adjustment processing. Priority communication processing is performed on the classified priority communication data, and the transmission energy scale is sorted from largest to smallest. Then, the secondary communication data is sorted from largest to smallest according to the transmission energy scale, and data communication information is generated.

[0022] As a further aspect of the present invention, the specific method for obtaining the evenly distributed data stream is as follows:

[0023] Next, the classified real-time communication data is acquired, and the acquired real-time communication data is analyzed a second time according to the real-time nature of the data to obtain real-time data and periodic data. Then, different interaction strategies are formulated for the classified real-time data and periodic data to generate interaction strategy information.

[0024] Meanwhile, all real-time communication data are labeled as n, where n = 1, 2, ..., m, and m represents the number of real-time communication data. The obtained real-time communication data is then divided equally according to the data capacity to obtain equally divided data streams, which are labeled as n1 and n2. Then, interactive encryption processing is performed based on the obtained equally divided data streams.

[0025] As a further aspect of the present invention, the specific method for obtaining segments with the same numerical value is as follows:

[0026] The equally divided data stream is converted to binary to obtain a converted equally divided data stream. The four binary numbers in the converted equally divided data stream are taken in sequence and divided into secondary segments. All converted equally divided data streams are processed in sequence. The sum of the binary numbers of each converted equally divided data stream is calculated. Secondary segments with the same sum of binary numbers are grouped into segments with the same value.

[0027] As a further aspect of the present invention, the specific method for generating the recombined data stream is as follows:

[0028] Different recombination and encryption processes are performed based on the number of equally divided data streams. If the number of equally divided data streams is odd, two groups of segments with the same value are selected in sequence and combined into a combined package. This operation is repeated for all segments with the same value.

[0029] If the number of data streams to be converted to be even is even, select two groups of segments with the same value in even positions and combine them into a package. Repeat this operation for all segments with the same value.

[0030] All bundled packets are merged into a recombined data stream for interactive transmission.

[0031] This invention provides a real-time communication and data interaction method for the coordinated operation of energy storage power stations and power grid dispatching. Compared with existing technologies, it has the following advantages:

[0032] This invention categorizes communication data into priority and secondary communication data, and then further classifies it based on time-series characteristics and business requirements to distinguish between real-time and periodic data. This classification method can more accurately identify the importance and urgency of data, providing a basis for prioritizing the transmission of critical data, optimizing the allocation of communication resources, and improving the response speed and accuracy of power grid dispatching and energy storage power station operation.

[0033] This invention constructs a multi-dimensional load monitoring system that comprehensively considers multiple indicators such as CPU utilization, memory utilization, network bandwidth utilization, and packet loss rate, and utilizes a genetic algorithm to optimize load thresholds. Combined with an improved linear adjustment algorithm, a dynamic adjustment factor is introduced, enabling more flexible and accurate adjustment of data transmission rates based on real-time changes in network load. This avoids network congestion and resource waste, improves data transmission efficiency and network utilization, and ensures the stability and smoothness of data transmission.

[0034] This invention develops refined interaction strategies tailored to the characteristics of real-time and periodic data. Real-time data utilizes independent high-speed channels, low-latency protocols, and priority queues to ensure instant data interaction; periodic data employs intelligent scheduling, batch transmission, and compression technologies to reduce network bandwidth consumption. This approach meets the transmission needs of different data types, improves the relevance and effectiveness of data interaction, and enhances the overall system performance.

[0035] This invention performs binary conversion, segmentation, recombination, and encryption on data. Data integrity is ensured through CRC checksum and data padding; data is recombined according to the sum of binary numbers and encrypted using parity combinations to enhance data security; and metadata headers are added to integrate the data and optimize the transmission structure. This effectively prevents data leakage and tampering, reduces transmission redundancy, improves data transmission efficiency and reliability, and provides a robust data security guarantee for the coordinated operation of energy storage power stations and grid dispatch. Attached Figure Description

[0036] Figure 1 This is a diagram illustrating the steps and methods of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see Figure 1 This application provides a real-time communication and data interaction method for the coordinated operation of energy storage power stations and power grid dispatching. The method specifically includes the following steps:

[0039] Step S1: Obtain historical communication records and corresponding communication data. Classify the communication data according to data type to obtain communication data of the same type. Then, calculate the transmission energy scale corresponding to the communication data of the same type within time T. The specific calculation method is as follows: label the communication data of the same type as i, and i = 1, 2, ..., j, where j represents the number of types of communication data of the same type. At the same time, obtain the number of transmissions corresponding to the data of the same type i within time T, denoted as Ci, and obtain the single transmission amount L corresponding to the number of transmissions Ci. Ci Then according to the formula Calculate the transmission energy scale K corresponding to the same type of communication data i. i Similarly, the transmission energy scale K for m data i of the same type within the same time T is... i Perform calculations and calculate m transmission energy scales K. i The average value is used as the criterion for judging the same type of communication data i.

[0040] The obtained judgment index is compared with the preset value, and the specific value of the preset value is set by the operator. The same type of communication data with the judgment index greater than the preset value is classified as priority communication data, and the same type of communication data with the judgment index less than the preset value is classified as secondary communication data.

[0041] Step S2: Construct a multi-dimensional load monitoring system. In addition to CPU utilization, memory utilization, and port queue length, add monitoring of indicators such as network bandwidth utilization, packet loss rate, and latency jitter. Compare real-time load with dynamic load thresholds. The thresholds are generated by a genetic algorithm combined with historical load data, device performance parameters, and service priorities. A configurable buffer range is also set (e.g., the threshold fluctuates within 5%-10%). When the real-time load exceeds the threshold range:

[0042] If the speed exceeds the upper limit threshold, a speed reduction mechanism is triggered, generating speed reduction transmission information containing information such as the speed reduction ratio and the effective time.

[0043] If the value is less than the lower threshold, the speed-up mechanism is triggered, and speed-up transmission information is generated.

[0044] An improved linear adjustment algorithm is adopted, introducing an adjustment factor α (dynamically selected based on network status and data type, ranging from 0.5 to 1.5). Taking CPU utilization as an example, the adjustment formula is:

[0045] Slow down:

[0046] Acceleration:

[0047] After receiving the adjustment information, the data sending end performs rate adjustment by adjusting the TCP window size, UDP sending frequency, etc., and provides real-time feedback on the adjustment status. After adjustment, the load and transmission quality are continuously monitored. If the expected effect is not achieved, a secondary adjustment is initiated or a nonlinear adjustment algorithm is switched.

[0048] Priority communication data and secondary communication data are sorted from largest to smallest according to their transmission energy, generating priority queues. When transmission energy is the same, priority is further differentiated by factors such as data generation time (newer data has higher priority) and business urgency.

[0049] Prioritize the transmission of high-priority communication data and employ multi-path concurrent transmission and retransmission mechanisms to ensure reliability;

[0050] Secondary communication data dynamically allocates transmission resources based on the remaining bandwidth and load of the channel, supporting batch transmission of data with high latency tolerance.

[0051] Step S3: After acquiring the classified priority and secondary communication data, a dual judgment mechanism based on time series characteristics and business requirements is constructed for secondary analysis. A real-time monitoring module is deployed to evaluate the data update frequency and business response requirements. When the data update interval is less than a set threshold (e.g., 1 second), and the business requires the data to be used for decision-making or control within a very short time (e.g., 500 milliseconds), it is judged as real-time data. For example, real-time fault alarm data of energy storage power station equipment must be processed immediately once it is generated to ensure system safety.

[0052] Historical data pattern matching algorithms are used to analyze the data generation patterns. If the data exhibits stable periodic characteristics, such as power grid load statistics generated every 15 minutes or equipment maintenance reports generated on fixed dates each month, and the periodic fluctuation range is within acceptable error (e.g., ±5%), then it is determined to be periodic data.

[0053] Develop refined interaction strategies tailored to the characteristics of real-time and periodic data;

[0054] Real-time data interaction: Establish an independent high-speed transmission channel and adopt a low-latency communication protocol (such as UDP combined with QUIC) to ensure that data is pushed to the target system immediately after it is generated. Simultaneously, set up a data priority queue so that real-time data can preempt the transmission resources of other data when network resources are scarce, ensuring immediate data interaction. Furthermore, establish a data acknowledgment and retransmission mechanism; if the receiver does not provide acknowledgment information within a specified time (e.g., 1 second), the sender automatically retransmits the data to avoid data loss.

[0055] Periodic Data Interaction: Based on the periodic patterns of data, an intelligent scheduling algorithm is designed. Before the start of a data cycle, the data acquisition and transmission modules are woken up in advance to preprocess and package the data. Batch transmission is adopted, combined with compression techniques (such as the Zstandard algorithm) to reduce the amount of data transmitted and lower network bandwidth usage. For data transmission failures, the failure time and reason are recorded, and the data is prioritized for retransmission in the next cycle. A maximum number of retransmissions is set (e.g., 3 times); exceeding this limit triggers an alarm mechanism.

[0056] All real-time communication data are labeled as n, where n = 1, 2, ..., m, and m represents the number of real-time communication data. The obtained real-time communication data is then divided equally according to the data capacity to obtain equally divided data streams, which are labeled as n1 and n2. Then, interactive encryption processing is performed based on the obtained equally divided data streams.

[0057] Step S4: After obtaining the evenly divided data streams n1 and n2, a standardized binary conversion protocol (such as binary conversion rules corresponding to ASCII, UTF-8, etc.) is used to convert each data element in the data stream into binary format one by one, resulting in a converted evenly divided data stream. During the conversion process, a data type mapping table is established to record the correspondence between the original data types and their binary representations, facilitating subsequent data restoration. The converted binary data stream is then subjected to integrity verification using the CRC (Cyclic Redundancy Check) algorithm to generate a checksum, which is appended to the end of the data stream. If the length of the binary data stream is not divisible by 4, a fixed padding character (such as 0000) is used to padded the end, ensuring the smooth progress of subsequent segmentation operations.

[0058] The process involves sequentially extracting four consecutive binary numbers from the transformed evenly divided data stream as a basic segmentation unit, which serves as the smallest unit for data partitioning. For example, for the transformed evenly divided data stream "1010110011100010...", first extract "1010" as the first segmentation unit, then extract "1100" as the second segmentation unit, and so on. Each segmentation unit acts as a boundary to further divide the transformed evenly divided data stream, resulting in multiple secondary segments. A segmentation index table is established to record the starting position, length, and number of segmentation units contained in each secondary segment within the original evenly divided data stream, facilitating data location and management.

[0059] The same segmentation operation is performed on all equally divided data streams until the secondary segmentation of all data streams is completed. During processing, a progress monitoring module is set up to display the segmentation progress in real time. If there is no progress for an extended period or an abnormal interruption occurs, an error diagnosis and recovery mechanism is automatically triggered. If data truncation occurs during segmentation (e.g., due to network transmission interruption causing incomplete data streams), data integrity is determined based on the checksum. If the data is incomplete, the truncation position is recorded, and a request is sent to the data sender to resend the corresponding portion of data, and the segmentation operation is restarted.

[0060] Iterate through all secondary partitions and use a parallel computing framework (such as Apache Spark) to sum the binary numbers in each partition. To avoid data overflow, select an appropriate data type (such as long integer or high-precision numeric type) based on the size of the secondary partition. Finally, obtain the total binary sum So for each secondary partition, where o = 1, 2, ..., p, where p represents the number of secondary partitions. Establish a hash mapping table, using the total binary sum So as the key, to group secondary partitions with the same sum into a set of partitions with the same value. For each sum value, generate a unique identifier and store the corresponding partitions with the same value in a container named Gu (u = 1, 2, ..., g, where g is the number of different sum values).

[0061] When the number of evenly distributed data streams p is odd:

[0062] Following the data stream numbering order, select the set G of segments with the same numerical value at odd-numbered positions (positions 1, 3, 5, ...). o1 G o2 The data streams from the two sets are combined into a single packet in a specific order (such as the original numbering order). The data within the combined packet is then encrypted using a symmetric encryption algorithm, with the encryption key associated with the data number and transmission time.

[0063] When the number of evenly divided data streams p is even, select the set of segments with the same value at even positions (2nd, 4th, 6th... positions) according to the data stream numbering order and combine them. Similarly, the odd number processing method is used to generate a combined package.

[0064] Deeply integrate all grouped data streams that belong to the same group of transformation and distribution data streams. Sort the grouped data streams according to a preset priority order (such as data importance, transmission timeliness, etc.), and add a header containing metadata such as data source, encryption method, and reassembly order to generate a complete reassembled data stream.

[0065] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching, characterized in that, The method specifically includes the following steps: The transmission energy scale corresponding to the same type of communication data is calculated based on historical data, and the judgment index is obtained by combining the average value. At the same time, it is compared with the preset value to classify and obtain priority communication data and secondary communication data. The system determines the real-time load of the communication channel, generates information to reduce or increase transmission speed, and performs linear adjustment processing based on real-time communication data to generate data communication information. The classified real-time communication data is further classified to obtain real-time and periodic data. Simultaneously, an interaction strategy is formulated, interaction strategy information is generated, and the data is evenly distributed according to its corresponding data capacity to obtain an evenly distributed data stream. The specific processing method is as follows: An improved linear adjustment algorithm is adopted, and an adjustment factor α is introduced. After receiving the adjustment information, the data sending end performs rate adjustment by adjusting the TCP window size and UDP sending frequency, and provides real-time feedback on the adjustment status. A refined interaction strategy is formulated to take into account the characteristics of real-time data and periodic data. Real-time data interaction includes: establishing an independent high-speed transmission channel and adopting a low-latency communication protocol to ensure that data is pushed to the target system immediately after it is generated; setting a data priority queue so that when network resources are scarce, real-time data can preempt the transmission resources of other data to ensure instant data interaction; in addition, establishing a data confirmation and retransmission mechanism so that if the receiver does not provide confirmation information within the specified time, the sender will automatically retransmit the data to avoid data loss. Periodic data interaction: Based on the periodic pattern of data, an intelligent scheduling algorithm is designed to wake up the data acquisition and transmission module in advance before the start time of the data cycle, perform data preprocessing and packaging, adopt batch transmission method, and combine compression technology to reduce the amount of data transmitted and reduce network bandwidth usage. For data that fails to be transmitted, the failure time and reason are recorded, and it is prioritized to be retransmitted in the next cycle. A maximum number of retransmissions is set, and an alarm mechanism is triggered if the number of retransmissions is exceeded. The evenly divided data stream is converted into a binary stream to obtain a converted evenly divided data stream, and then divided into secondary segments in sequence. Secondary segments with the same sum of binary numbers are marked as segments with the same value. The data streams are combined based on the parity of their quantity to generate a combined package. These combined packages are then combined to generate a reconstructed data stream. The specific processing method is as follows: Different recombination and encryption processes are performed based on the number of equally divided data streams. If the number of equally divided data streams is odd, two groups of segments with the same value are selected in sequence and combined into a combined package. This operation is repeated for all segments with the same value. If the number of data streams to be converted to be even is even, select two groups of segments with the same value in even positions and combine them into a package. Repeat this operation for all segments with the same value. All bundled packets are merged into a recombined data stream for interactive transmission.

2. The real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching as described in claim 1, characterized in that, The specific method for obtaining the judgment index is as follows: Classify historical communication data by type to obtain a set of communication data of the same type, denoted as i, where i = 1, 2, ..., j, and j represents the number of types of communication data of the same type. For each type i, count the number of transmissions Ci within the time window T, and obtain the transmission amount L per transmission. Ci According to the formula Calculate the transmission energy scale K i Repeat the calculation for m time windows T to obtain m K values. i Values, and calculate m transmission energy scales K. i The average value is used as the criterion for judging the same type of communication data i.

3. The real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching as described in claim 1, characterized in that, The specific method for comparing and classifying the data with preset values ​​to obtain priority communication data and secondary communication data is as follows: The obtained judgment index is compared with the preset value, and the specific value of the preset value is set by the operator. The same type of communication data with the judgment index greater than the preset value is classified as priority communication data, and the same type of communication data with the judgment index less than the preset value is classified as secondary communication data.

4. The real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching as described in claim 1, characterized in that, The specific method for generating information that reduces or increases transmission speed is as follows: Real-time communication data is acquired and classified into priority communication data and secondary communication data. Then, the real-time load corresponding to the communication channel is acquired and compared with the load threshold. If the real-time load is greater than the load threshold, a slowdown transmission message is generated; conversely, if the real-time load is less than the load threshold, a speed-up transmission message is generated.

5. The real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching as described in claim 1, characterized in that, The specific method for generating data communication information is as follows: Next, a linear adjustment algorithm is selected based on the generated deceleration or acceleration transmission information for adjustment processing. Priority communication processing is performed on the classified priority communication data, and the transmission energy scale is sorted from largest to smallest. Then, the secondary communication data is sorted from largest to smallest according to the transmission energy scale, and data communication information is generated.

6. The real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching according to claim 1, characterized in that, The specific method for obtaining the evenly distributed data stream is as follows: Next, the classified real-time communication data is acquired, and the acquired real-time communication data is analyzed a second time according to the real-time nature of the data to obtain real-time data and periodic data. Then, different interaction strategies are formulated for the classified real-time data and periodic data to generate interaction strategy information. Meanwhile, all real-time communication data are labeled as n, where n = 1, 2, ..., m, and m represents the number of real-time communication data. The obtained real-time communication data is then divided equally according to the data capacity to obtain equally divided data streams, which are labeled as n1 and n2. Then, interactive encryption processing is performed based on the obtained equally divided data streams.

7. The real-time communication and data interaction method for coordinated operation of energy storage power stations and power grid dispatching according to claim 1, characterized in that, The specific method for obtaining segments with the same numerical value is as follows: The equally divided data stream is converted to binary to obtain a converted equally divided data stream. The four binary numbers in the converted equally divided data stream are taken in sequence and divided into secondary segments. All converted equally divided data streams are processed in sequence. The sum of the binary numbers of each converted equally divided data stream is calculated. Secondary segments with the same sum of binary numbers are grouped into segments with the same value.

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