Block chain-based big data statistical equipment and statistical system thereof

Abstract

The invention discloses a block chain-based big data statistical device and statistical system, which comprises a plurality of block chain servers, an intermediate router, a firewall, a big data statistical server, a management terminal and a cloud, and is characterized in that the plurality of block chain servers are in signal connection with the intermediate router; the intermediate router is in signal connection with a big data statistics server through a firewall, the big data statistics server is in signal connection with a cloud server, and the big data statistics server is in signal connection with a plurality of management terminals. According to the invention, the server data of the block chain is sent to the big data statistics server through the cooperation of the intermediate router and the firewall, and the big data statistics server carries out the analysis and statistics processing of the server data of the block chain, and obtains the operation state of the block chain server. According to the invention, the block chain data server can be monitored in real time, and the stability and security of the operation of the block chain server are improved.

Description

Technical Field

[0001] This invention relates to the field of data statistics technology, and in particular to blockchain-based big data statistics equipment and systems. Background Technology

[0002] Blockchain is a chain-like data structure that combines data blocks sequentially in chronological order. It is a distributed ledger that is cryptographically guaranteed to be immutable and unforgeable. The data flow of blockchain is quite large. In order to improve the security of blockchain data flow, big data statistical methods are generally used to statistically process the data in the blockchain.

[0003] A search revealed a blockchain-based data statistics and analysis device disclosed in patent number "CN108646857A". The device includes a housing assembly, a first protective assembly, and a second protective assembly. The housing assembly comprises a main body shell, a switch, a USB interface, a network cable interface, a data interface, a speaker, a display screen, and a handle. The speaker is located on the right side of the front surface of the main body shell and is fixedly connected to it. When the analysis device needs to be carried, the hinges rotate to attach two cover plates to the upper surface of the main body shell. A magnetic sheet then secures the first cover plate to the main body shell, preventing it from opening during movement. Finally, the handle is used to carry the analysis device. During movement, the cover plates effectively protect the display screen, improving its safety. When the main body shell encounters an external impact, the compression of the protective sleeve weakens the impact, reducing its effect on the device.

[0004] The aforementioned data statistics and analysis equipment for blockchain has reduced the impact through its structural design. However, currently, the servers of multiple nodes in the blockchain do not perform data monitoring and processing. When the data of some nodes in the blockchain is tampered with, it can easily lead to the security of the entire blockchain data transmission, affecting the data transmission and access security of the blockchain. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based big data statistical device and its statistical system to solve the problem mentioned in the background art that currently, servers of multiple nodes in the blockchain do not perform data monitoring and processing. When the data of some nodes in the blockchain is tampered with, it can easily lead to the security of the entire blockchain data transmission and affect the data transmission and access security of the blockchain.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-based big data statistics device and its statistics system, comprising multiple blockchain servers, an intermediate router, a firewall, a big data statistics server, management terminals, and a cloud, characterized in that the multiple blockchain servers are signal-connected to the intermediate router, the intermediate router is signal-connected to the big data statistics server through the firewall, the big data statistics server is signal-connected to the cloud server, the big data statistics server is signal-connected to multiple management terminals, and the big data server internally encapsulates the big data statistics system.

[0007] Preferably, the big data statistics server includes a packaged shell, inside which multiple fixing plates are equidistantly fixedly installed. The packaged shell is divided into multiple mounting cavities by the fixing plates. A server body is fixedly installed inside each of the multiple mounting cavities, and the multiple server bodies are electrically connected via data cables. Sleeves are fixedly installed on both sides of the inner walls of each of the multiple mounting cavities. A sleeve rod is sleeved and connected inside each of the multiple sleeves. A return spring is fixedly installed inside each of the multiple sleeves, with one end of each return spring fixedly connected to one end of the sleeve rod. Limiting plates are fixedly provided on both sides of the end of the sleeve rod. Limiting grooves are formed on both sides of the inner walls of the sleeves. The limiting plates and limiting grooves... The groove sliding connection is provided. A clamping plate is fixedly installed at the other end of the sleeve rod. The two clamping plates located in the same mounting cavity are in contact with the two sides of the server body. A cavity is opened at the bottom of the encapsulation shell. A dustproof net is fixedly installed on the front of the cavity. An air pump is fixedly installed inside the cavity. A T-junction is fixedly installed at the air outlet end of the air pump. Air passages are opened on both sides of the inner wall of the encapsulation shell. Multiple air holes communicating with the mounting cavity are opened on the side of the air passages. The two ends of the T-junction are fixedly connected to the air inlet end of the air passage through a conduit. Support legs are fixedly installed at the four corners of the bottom of the encapsulation shell. The bottom ends of the four support legs are all provided with evenly distributed anti-slip textures.

[0008] Preferably, the big data statistics system includes an information acquisition module, an information classification module, an information analysis module, an information processing module, a statistical reporting module, an information storage module, and an information feedback module. The big data statistics system receives blockchain data sent by the blockchain server, classifies and analyzes the data sent by the blockchain server, performs statistical analysis on the analyzed information, stores the statistically analyzed data, and provides timely feedback.

[0009] Preferably, the information acquisition module specifically acquires the data stream transmitted by the router and stores the acquired data stream in a temporary database for later use.

[0010] Preferably, the information classification module specifically involves: using a Poisson model to classify the data in the zero-time database, and then deduplicating the classified data.

[0011] Preferably, the information analysis module specifically includes regression analysis, Bayesian analysis, nearest neighbor algorithm analysis, and artificial neural network analysis.

[0012] Preferably, the regression analysis specifically includes: basic statistical analysis methods for determining the interdependence between variables by analyzing categorical data, assuming that random errors follow a normal distribution with a mean of zero, designing parameters using methods such as least squares and maximum likelihood, and using one or more independent independent variables to fit and predict the dependent variable in the regression analysis; Bayesian analysis can be divided into Naive Bayes (NBC) and Bayesian Network (BN) based on whether the data features satisfy the assumption of independence. NBC assumes that the features are independent, calculates the conditional probability of the sample features, and selects the class with the highest probability as the classification prediction of the unknown sample. BN extends this by acknowledging the existence of correlation between data features and then constructing a network model. Nearest neighbor algorithm analysis: For a sample whose category needs to be known, set an appropriate parameter k, query the category of the k nearest known samples in its feature space, and vote among these k categories. The result with the highest number of votes is the predicted category of the sample. Artificial Neural Network Analysis: After establishing the structural model, appropriate weights and thresholds need to be repeatedly adjusted through training. The final output of the neural network is determined by the activation function, weights, thresholds, and node connection methods. There are many types of ANNs, so different applications can utilize ANNs with different structures.

[0013] Preferably, the information processing module processes the analyzed data through the following steps: Step 1: Data cleaning, which involves filtering out redundant and duplicate data, filling in missing data, and correcting or deleting erroneous data; The second step is data processing, which involves extracting information from data fields, performing calculations, grouping, and transformations.

[0014] Preferably, the statistical report module specifically displays the processed data through data charts and sends the generated data charts to the management terminal.

[0015] Preferably, the information storage module and the information feedback module send the processed data to the cloud database, where the processed data is stored. The information feedback module then uses the result box management terminal to provide feedback on the running status of the connected blockchain database.

[0016] The technical effects and advantages of this invention are as follows: By sending blockchain server data to a big data statistics server through the cooperation of an intermediate router and a firewall, the big data statistics server analyzes and statistically processes the blockchain server data to obtain the operating status of the blockchain server. This enables real-time monitoring of the blockchain data server, thereby improving the stability and security of the blockchain server operation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the statistical device structure of the blockchain-based big data statistical device and its statistical system according to the present invention. Figure 2 This is a schematic diagram of the big data statistics server structure of the blockchain-based big data statistics device and its statistical system according to the present invention. Figure 3 This is a schematic cross-sectional view of the big data statistics server of the blockchain-based big data statistics device and its statistical system according to the present invention. Figure 4 This invention relates to a blockchain-based big data statistical device and its statistical system, specifically a big data statistical server. Figure 3 Enlarged view of a portion of point A in the middle; Figure 5 This is a block diagram of the statistical system of the blockchain-based big data statistical device and its statistical system according to the present invention.

[0018] In the diagram, 1. Encapsulation shell; 2. Server body; 3. Fixing plate; 4. Cavity; 5. Dustproof mesh; 6. Support leg; 7. Air duct; 8. Clamping plate; 9. Sleeve rod; 10. Sleeve; 11. Return spring; 12. Air pump; 13. Air hole; 14. Limiting plate. Detailed Implementation

[0019] 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.

[0020] Example 1: As Figures 1-4 As shown, a blockchain-based big data statistics device and its statistics system include multiple blockchain servers, an intermediate router, a firewall, a big data statistics server, a management terminal, and a cloud. The device is characterized in that the multiple blockchain servers are signal-connected to the intermediate router, the intermediate router is signal-connected to the big data statistics server through the firewall, the big data statistics server is signal-connected to the cloud server, the big data statistics server is signal-connected to multiple management terminals, and the big data server internally encapsulates the big data statistics system.

[0021] The big data statistics server includes a housing 1, inside which multiple fixing plates 3 are fixedly installed at equal intervals. The housing 1 is divided into multiple mounting cavities by the fixing plates 3. Server bodies 2 are fixedly installed inside each of the multiple mounting cavities. The multiple server bodies 2 are electrically connected via data cables. The connection of multiple server bodies 2 improves the computing efficiency of big data statistics. Sleeves 10 are fixedly installed on both sides of the inner wall of each of the multiple mounting cavities. A sleeve rod 9 is sleeved and connected inside each of the multiple sleeves 10. A return spring 11 is fixedly installed inside each of the multiple sleeves 10. One end of the multiple return spring 11 is fixedly connected to one end of the sleeve rod 9. Limiting plates 14 are fixedly provided on both sides of the end of the sleeve rod 9. Limiting grooves are formed on both sides of the inner wall of the sleeve 10. The limiting plates 14 and the limiting grooves are connected to the limiting grooves. The groove slides and the other end of the sleeve rod 9 is fixedly installed with a clamping plate 8. The two clamping plates 8 located in the same mounting cavity are in contact with the two sides of the server body 2. The bottom of the encapsulation shell 1 has a cavity 4. A dustproof net 5 is fixedly installed on the front of the cavity 4. An air pump 12 is fixedly installed inside the cavity 4. A three-way valve is fixedly installed at the air outlet end of the air pump 12. Air passages 7 are opened on both sides of the inner wall of the encapsulation shell 1. Multiple air holes 13 communicating with the mounting cavity are opened on the side of the air passages 7. The two ends of the three-way valve are fixedly connected to the air inlet end of the air passage 7 through a conduit. Support legs 6 are fixedly installed at the four corners of the bottom of the encapsulation shell 1. The bottom of the four support legs 6 is provided with evenly distributed anti-slip textures. By opening the air passages 7 and blowing air through the air pump 12, the heat dissipation efficiency is improved.

[0022] By sending blockchain server data to a big data statistics server through an intermediate router and firewall, the big data statistics server analyzes and processes the blockchain server data to obtain the operating status of the blockchain server. This allows for real-time monitoring of the blockchain data server, improving the stability and security of the blockchain server operation.

[0023] Example 2: Figure 5 As shown, a statistical system for big data statistical devices based on blockchain: The big data statistics system includes modules for information acquisition, information classification, information analysis, information processing, statistical reporting, information storage, and information feedback. The system receives blockchain data sent by the blockchain server, classifies and analyzes the data, performs statistical analysis on the analyzed information, stores the statistically analyzed data, and provides timely feedback.

[0024] The information acquisition module specifically acquires the data stream transmitted by the router and stores the acquired data stream in a temporary database for later use.

[0025] The information classification module specifically uses the Poisson model to classify the data in the zero-time database and then performs deduplication on the classified data.

[0026] The information classification module is specifically classified into frequency domain feature data extraction, which uses Mel frequency cepstral coefficient extraction to emphasize, classify, and window the big data software, and obtain the time domain signal of the frame number. After the time domain signal of the frame number is Fourier transformed, the discrete spectrum can be obtained.

[0027] The information analysis module specifically includes regression analysis, Bayesian analysis, nearest neighbor algorithm analysis, and artificial neural network analysis.

[0028] Regression analysis specifically includes: basic statistical analysis methods for determining the interdependencies between variables by analyzing categorical data; assuming that random errors follow a normal distribution with a mean of zero; designing parameters using methods such as least squares and maximum likelihood; and using one or more independent independent variables to fit and predict the dependent variable. Bayesian analysis can be divided into Naive Bayes (NBC) and Bayesian Network (BN) based on whether the data features satisfy the assumption of independence. NBC assumes that the features are independent, calculates the conditional probability of the sample features, and selects the class with the highest probability as the classification prediction of the unknown sample. BN extends this by acknowledging the existence of correlation between data features and then constructing a network model. Nearest neighbor algorithm analysis: For a sample whose category needs to be known, set an appropriate parameter k, query the category of the k nearest known samples in its feature space, and vote among these k categories. The result with the highest number of votes is the predicted category of the sample. Artificial Neural Network Analysis: After establishing the structural model, appropriate weights and thresholds need to be repeatedly adjusted through training. The final output of the neural network is determined by the activation function, weights, thresholds, and node connection methods. There are many types of ANNs, so different applications can utilize ANNs with different structures.

[0029] Information processing module: Processes the analyzed data through the following steps: Step 1: Data cleaning, which involves filtering out redundant and duplicate data, filling in missing data, and correcting or deleting erroneous data; The specific approach to data cleaning is as follows: Based on functional dependency data cleaning methods, data is obtained from a temporary database. Semantic analysis is performed on fields based on their definitions, attributes, and value ranges to discover hidden relationships between fields. Furthermore, functional dependencies within the dataset are identified. Based on functional dependencies, inconsistencies and hidden noise can be detected. Noise filtering is then performed. Regression methods use a function to fit the data, smoothing it and removing noise. The regressed function value replaces the original data, thus avoiding interference from noise. Regression methods rely on determining data trends; only data exhibiting linear trends are eligible for regression. Therefore, data visualization is often necessary to assess trends and patterns before determining whether regression can be used for noise reduction.

[0030] The second step is data processing, which involves extracting information from data fields, performing calculations, grouping, and transformations.

[0031] The statistical reporting module specifically displays the processed data through data charts and sends the generated data charts to the management terminal.

[0032] Specific statistical methods include: using a divide-and-conquer algorithm to divide the processed data into K subsets, performing statistical analysis on each subset first, and then combining the analysis results of the K subsets.

[0033] The information storage module and the information feedback module send the processed data to the cloud database, where the processed data is stored. The information feedback module provides feedback on the running status of the connected blockchain database through the result box management terminal.

[0034] Contents not described in detail herein are existing technologies known to those skilled in the art. The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art can make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A blockchain-based big data statistics device, comprising multiple blockchain servers, an intermediate router, a firewall, a big data statistics server, a management terminal, and a cloud platform, characterized in that: Multiple blockchain servers are connected to an intermediate router via a signal. The intermediate router is connected to a big data statistics server via a firewall. The big data statistics server is connected to a cloud server via a signal. The big data statistics server is connected to multiple management terminals via a signal. The big data server internally encapsulates a big data statistics system.

2. A blockchain-based big data statistical device, characterized in that: The big data statistics server includes a package shell (1), inside which multiple fixing plates (3) are fixedly installed at equal intervals. The package shell (1) is divided into multiple mounting cavities by the fixing plates (3). Server bodies (2) are fixedly installed inside each of the multiple mounting cavities. The multiple server bodies (2) are electrically connected by data cables. Sleeves (10) are fixedly installed on both sides of the inner wall of each of the multiple mounting cavities. A sleeve rod (9) is sleeved and connected inside each of the multiple sleeves (10). A reset spring (11) is fixedly installed inside each of the multiple sleeves (10). One end of the multiple reset springs (11) is fixedly connected to one end of the sleeve rod (9). Limiting plates (14) are fixedly provided on both sides of the end of the sleeve rod (9). Limiting grooves are opened on both sides of the inner wall of the sleeve (10). The limiting plates (14) and the limiting grooves are connected to each other. The sliding connection is provided. The other end of the sleeve rod (9) is fixedly installed with a clamping plate (8). The two clamping plates (8) located in the same mounting cavity are in contact with the two sides of the server body (2). The bottom of the encapsulation shell (1) is provided with a cavity (4). The front of the cavity (4) is fixedly installed with a dustproof net (5). The cavity (4) is fixedly installed with an air pump (12). The air outlet of the air pump (12) is fixedly installed with a three-way valve. The inner walls of the encapsulation shell (1) are provided with air passages (7) on both sides. The sides of the air passages (7) are provided with multiple air holes (13) communicating with the mounting cavity. The two ends of the three-way valve are fixedly connected to the air inlet of the air passages (7) through a conduit. The four corners of the bottom of the encapsulation shell (1) are fixedly installed with support legs (6). The bottom ends of the four support legs (6) are provided with evenly distributed anti-slip textures.

3. A statistical system for big data statistical equipment based on blockchain, characterized in that, The big data statistics device based on blockchain as described in claim 1 includes an information acquisition module, an information classification module, an information analysis module, an information processing module, a statistical report module, an information storage module, and an information feedback module. The big data statistics system receives blockchain data sent by the blockchain server, classifies and analyzes the data sent by the blockchain server, performs statistical analysis on the analyzed information, stores the statistically analyzed data, and provides timely feedback.

4. The statistical system for blockchain-based big data statistical equipment according to claim 3, characterized in that, The information acquisition module specifically acquires the data stream transmitted by the router and stores the acquired data stream in a temporary database for later use.

5. The statistical system for big data statistical equipment based on blockchain according to claim 3, characterized in that, The information classification module specifically involves: using the Poisson model to classify the data in the zero-time database, and then deduplicating the classified data.

6. The statistical system for big data statistical equipment based on blockchain according to claim 3, characterized in that, The information analysis module specifically includes regression analysis, Bayesian analysis, nearest neighbor algorithm analysis, and artificial neural network analysis.

7. The statistical system for big data statistical equipment based on blockchain according to claim 3, characterized in that, The regression analysis specifically includes: basic statistical analysis methods for determining the interdependencies between variables by analyzing categorical data, assuming that random errors follow a normal distribution with a mean of zero, designing parameters using methods such as least squares and maximum likelihood, and using one or more independent independent variables to fit and predict the dependent variable in the regression analysis. Bayesian analysis can be divided into Naive Bayes (NBC) and Bayesian Network (BN) based on whether the data features satisfy the assumption of independence. NBC assumes that the features are independent, calculates the conditional probability of the sample features, and selects the class with the highest probability as the classification prediction of the unknown sample. BN extends this by acknowledging the existence of correlation between data features and then constructing a network model. Nearest neighbor algorithm analysis: For a sample whose category needs to be known, set an appropriate parameter k, query the category of the k nearest known samples in its feature space, and vote among these k categories. The result with the highest number of votes is the predicted category of the sample. Artificial Neural Network Analysis: After establishing the structural model, appropriate weights and thresholds need to be repeatedly adjusted through training. The final output of the neural network is determined by the activation function, weights, thresholds, and node connection methods. There are many types of ANNs, so different applications can utilize ANNs with different structures.

8. The statistical system for big data statistical equipment based on blockchain according to claim 3, characterized in that, The information processing module processes the analyzed data through the following steps: Step 1: Data cleaning, which involves filtering out redundant and duplicate data, filling in missing data, and correcting or deleting erroneous data; The second step is data processing, which involves extracting information from data fields, performing calculations, grouping, and transformations.

9. The statistical system for big data statistical equipment based on blockchain according to claim 3, characterized in that, Specifically, the statistical reporting module displays the processed data through data charts and sends the generated data charts to the management terminal.

10. The statistical system for blockchain-based big data statistical equipment according to claim 3, characterized in that, The information storage module and information feedback module send the processed data to the cloud database, where the processed data is stored. The information feedback module provides feedback on the running status of the connected blockchain database through the result box management terminal.

Images