A data instruction monitoring method and system for blockchain information transmission

Abstract

The present invention discloses a data instruction monitoring method and system for blockchain information transmission, which relates to the field of data processing technology. The method comprises: receiving a data transmission request from a blockchain user, the request comprising a source node identifier, a data attribute identifier, a storage timestamp identifier and an instruction monitoring data stream; through an instruction parsing channel, performing data configuration according to the above identifiers to generate an instruction target data stream; through an anomaly detection channel, performing an anomaly analysis on the instruction monitoring data stream and the target data stream, and calculating a data stream deviation coefficient; if the deviation coefficient is greater than or equal to a threshold, it is marked as an abnormal instruction data stream; and an error signal is generated according to the abnormal data and sent to a blockchain user end. Thus, the technical effect of improving the efficiency and timeliness of anomaly detection and processing is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data instruction monitoring method and system for blockchain information transmission. Background Art

[0002] With the expansion of blockchain network scale and diversification of applications, problems such as data anomalies and misoperation of instructions during information transmission have gradually emerged, leading to risks such as inefficient data transmission, information loss and even data tampering, affecting the security and stability of blockchain systems.

[0003] The data transmission process in the existing technology usually ensures the security and integrity of data through distributed consensus mechanisms and encryption algorithms, mainly for the consensus process between blockchain nodes. The method of rejecting data transmission by relying on encrypted verification has a long path and low efficiency. The processing of abnormal data flow is mostly delayed, making it difficult to identify and prevent potential security risks in a timely manner. In summary, the existing technology has the technical problems of lack of real-time monitoring of instruction data flow, abnormal detection and delayed processing. Summary of the invention

[0004] The present invention provides a data instruction monitoring method and system for blockchain information transmission to solve the technical problems of lack of real-time monitoring of instruction data streams, anomaly detection and processing lag in the prior art, and achieve the technical effect of improving the efficiency and timeliness of anomaly detection and processing.

[0005] In a first aspect, the present invention provides a data instruction monitoring method for blockchain information transmission, wherein the method comprises:

[0006] Receive a data transmission request input by a blockchain user terminal, wherein the data transmission request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction to monitor a data flow.

[0007] Through the instruction parsing channel, instruction data configuration is performed according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to obtain an instruction target data stream.

[0008] Through the anomaly detection channel, an anomaly analysis is performed on the instruction monitoring data flow and the instruction target data flow to obtain a data flow deviation coefficient.

[0009] When the data flow deviation coefficient is greater than or equal to a data flow deviation coefficient threshold, the instruction monitoring data flow is marked as an abnormal instruction data flow.

[0010] An information transmission error signal is generated according to the abnormal instruction data flow and sent to the blockchain user end.

[0011] In a second aspect, the present invention further provides a data instruction monitoring system for blockchain information transmission, wherein the system comprises:

[0012] A request receiving module, the request receiving module is used to receive a data transmission request input by a blockchain user terminal, wherein the data transmission request includes a source node identifier, a data attribute identifier, a storage timestamp identifier and an instruction monitoring data flow.

[0013] An instruction parsing module is used to configure instruction data through an instruction parsing channel according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to obtain an instruction target data stream.

[0014] The anomaly detection module is used to perform an anomaly analysis on the instruction monitoring data flow and the instruction target data flow through an anomaly detection channel to obtain a data flow deviation coefficient.

[0015] An abnormal calibration module is used to calibrate the instruction monitoring data flow as an abnormal instruction data flow when the data flow deviation coefficient is greater than or equal to a data flow deviation coefficient threshold.

[0016] An abnormal return module is used to generate an information transmission error signal according to the abnormal instruction data flow and send it to the blockchain user end.

[0017] The present invention discloses a data instruction monitoring method and system for blockchain information transmission, including: receiving a data transmission request from a blockchain user end, the request including a source node identifier, a data attribute identifier, a storage timestamp identifier and an instruction monitoring data stream; through an instruction parsing channel, configuring instruction data according to the source node identifier, the data attribute identifier and the storage timestamp identifier, generating an instruction target data stream; through an anomaly detection channel, performing an anomaly analysis on the instruction monitoring data stream and the instruction target data stream, calculating a data stream deviation coefficient; when the data stream deviation coefficient is greater than or equal to a preset deviation coefficient threshold, marking the instruction monitoring data stream as an abnormal instruction data stream; based on the abnormal instruction data stream, generating an information transmission error signal and sending it to the blockchain user end. The data instruction monitoring method and system for blockchain information transmission disclosed by the present invention solve the technical problems of lack of real-time monitoring of instruction data streams, abnormal detection and processing lag, and achieve the technical effect of improving abnormal detection and processing efficiency and timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a flow chart of a data instruction monitoring method for blockchain information transmission according to the present invention;

[0019] Figure 2This is a structural schematic diagram of a data instruction monitoring system for blockchain information transmission according to the present invention.

[0020] Explanation of the accompanying drawings: request receiving module 11, instruction parsing module 12, anomaly detection module 13, anomaly calibration module 14, anomaly return module 15. DETAILED DESCRIPTION

[0021] The technical solution provided in the embodiments of the present invention is to solve the technical problems of the prior art in lacking real-time monitoring of instruction data streams, abnormality detection and delayed processing. The overall idea adopted is as follows:

[0022] First, a data transmission request input by a blockchain user end is received, and the request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction monitoring data stream; then, through an instruction parsing channel, the instruction data is configured according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to generate an instruction target data stream; then, through an anomaly detection channel, an anomaly analysis is performed on the instruction monitoring data stream and the instruction target data stream, and a data stream deviation coefficient is calculated; then, when the data stream deviation coefficient is greater than or equal to a preset threshold, the instruction monitoring data stream is marked as an abnormal instruction data stream; then, an information transmission error signal is generated according to the abnormal instruction data stream; finally, the error signal is sent to the blockchain user end.

[0023] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them. Embodiment 1

[0024] Figure 1 The present invention is a flowchart of a data instruction monitoring method for blockchain information transmission, wherein the method comprises:

[0025] Receive a data transmission request input by a blockchain user terminal, wherein the data transmission request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction to monitor a data flow.

[0026] Specifically, a data transmission request is initiated by a blockchain client and includes application information including a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction to monitor the data flow, which is used to request the transmission of a specific data flow from the source node. In other words, the elements in the data transmission request provide context information and operation instructions for data transmission.

[0027] Specifically, in the above data transmission request, the source node identifier is used to indicate the source node of the data transmission, that is, the node where the data is initially stored or generated, and identifies the unique identity of the source node, such as a user device, IoT device, or sensor node.

[0028] Specifically, data attribute identifiers are used to identify the type, format or other attribute information of the data, thereby ensuring that the data can be processed in the specified format during transmission. At the same time, data attribute identifiers also help determine the priority of data transmission, storage requirements, etc.

[0029] Specifically, the storage timestamp identifier records the storage timestamp of the data at the source node, that is, the time when the data transmission request is generated or recorded. This storage timestamp helps to ensure the timeliness, sequence and traceability of the data in the blockchain, and ensures the immutability of the data in the blockchain and the consistency of the time chain.

[0030] Specifically, the instruction monitoring data flow refers to the specific instruction flow that controls or monitors the data transmission process. For example, the instruction monitoring data flow includes instructions on transmission speed, encryption method, data integrity check, etc. Through the instruction monitoring data flow, the status of data transmission can be obtained to ensure that the data reaches the target node safely and completely.

[0031] Through the instruction parsing channel, instruction data configuration is performed according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to obtain an instruction target data stream.

[0032] Specifically, the instruction parsing channel extracts key data from the source node identifier, data attribute identifier, and storage timestamp identifier based on a predetermined instruction parsing algorithm or instruction parsing rule, and locates the instruction target data stream pointed to by the data transmission request based on the extracted data. Among them, the instruction target data stream is a standardized data stream for data transmission, which is convenient for transmission and verification in the blockchain network.

[0033] In some embodiments, the instruction data is configured according to the source node identifier, the data attribute identifier, and the storage timestamp identifier through the instruction parsing channel to obtain the instruction target data stream, which includes:

[0034] Obtaining data to be stored that has passed blockchain consensus verification, wherein the data to be stored has a node identifier to be stored, an attribute identifier to be stored, and a timestamp identifier to be stored; constructing a to-be-stored instruction data stream of the data to be stored according to the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored; associating and storing the instruction data stream to be stored, the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored, to construct an instruction data stream index library; inputting the source node identifier, the data attribute identifier, and the storage timestamp identifier into the instruction data stream index library to match the initial instruction data stream;

[0035] Through the instruction parsing channel, based on the blockchain client, the initial instruction data stream is configured to obtain the instruction target data stream.

[0036] Specifically, blockchain consensus verification is performed on the data to be stored to ensure the integrity and consistency of the data. The data to be stored includes the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored. The node identifier to be stored indicates the node location where the data to be stored should be stored. The attribute identifier to be stored describes the attribute information of the data to be stored, defines the data type and purpose. The timestamp identifier to be stored records the time when the data is generated or stored, ensuring that the data has a clear order on the timeline.

[0037] Specifically, according to the node, attribute and timestamp information of the data to be stored, the corresponding instruction data stream to be stored is constructed to ensure that the data can be effectively processed and stored. The instruction data stream to be stored is a structured and formatted data stream containing information such as nodes, attributes and timestamps for subsequent transmission and storage operations.

[0038] Optionally, constructing a data stream of instructions to be stored for the data to be stored involves standardizing the format of the data to be stored to ensure that the data stream can be smoothly transmitted between different nodes; according to the requirements of the blockchain, the data stream of instructions to be stored is encrypted to ensure the security of the data during transmission and storage.

[0039] Specifically, the instruction data stream to be stored is associated with its node identifier, attribute identifier and timestamp identifier and stored, and an instruction data stream index library is constructed. The instruction data stream index library is the basis for subsequent data matching and query. Through the instruction data stream index library, the corresponding data stream can be quickly found according to any identifier, thereby improving data processing efficiency. This helps to facilitate the subsequent search and management of data.

[0040] Specifically, an association relationship is established between each instruction data stream to be stored and its node identifier, attribute identifier and timestamp identifier, and stored in the instruction data stream index library. When the instruction data stream to be stored or its node identifier, attribute identifier and timestamp identifier changes, the above-mentioned instruction data stream index library is updated, thereby ensuring the reliability of the instruction data stream index library.

[0041] Furthermore, with the source node identifier, data attribute identifier, and storage timestamp identifier as index constraints, the instruction data stream index library is traversed to perform data stream matching to obtain an initial instruction data stream, wherein the initial instruction data volume is an instruction data stream to be stored whose node identifier to be stored, attribute identifier to be stored, and timestamp identifier to be stored in the instruction data stream index library are consistent with the source node identifier, data attribute identifier, and storage timestamp identifier.

[0042] Furthermore, the instruction parsing channel configures the initial instruction data stream according to the user characteristics of the blockchain user terminal and obtains the instruction target data stream. The instruction parsing channel is used to perform targeted data processing for the blockchain user terminal based on the standardized initial instruction data stream, thereby obtaining the instruction target data stream adapted to the blockchain user terminal, ensuring that the instruction target data stream can be correctly identified and processed.

[0043] Through the above-mentioned method steps, while improving the efficiency and accuracy of instruction data configuration, the adaptability of the generated instruction target data stream is ensured, that is, it meets the specific requirements of the blockchain user end and can be used for transmission and calculation.

[0044] In some implementations, constructing a to-be-stored instruction data stream of the to-be-stored data according to the to-be-stored node identifier, the to-be-stored attribute identifier, and the to-be-stored timestamp identifier includes:

[0045] Perform hash processing on the data to be stored to obtain a data content hash value; perform hash processing based on the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored to obtain a data portrait hash value; perform hash processing based on the data content hash value and the data portrait hash value to obtain a data global hash value; concatenate the data global hash value, the data portrait hash value, and the data content hash value to generate a data ID identifier; construct the instruction data stream to be stored based on the data ID identifier.

[0046] Specifically, first, the data to be stored is hashed to generate a unique data content hash value. The data content hash value is obtained by applying a hash function (such as SHA-256) to the data content, and is used to ensure the integrity and immutability of the data content. Then, hash processing is performed based on the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored to generate a data portrait hash value. The data portrait hash value is used to represent the metadata of the data, which facilitates the identification and classification of the data. Next, the data content hash value and the data portrait hash value are combined and hashed again to obtain the data global hash value. The data global hash value is unique, providing a globally unique identification identity for the data, which helps to enhance data tracking capabilities.

[0047] Specifically, the above-mentioned data global hash value, data portrait hash value and data content hash value are connected in series to generate a corresponding data ID identifier. The data ID identifier is not only unique, but also contains multiple layers of information of the data. By parsing and verifying the multiple layers of information in the data ID identifier, the integrity and originality of the data can be verified, which helps to prevent tampering or the discovery of data errors.

[0048] Further, constructing the instruction data stream to be stored according to the data ID identifier includes:

[0049] According to the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored, a first instruction data stream is constructed; the data ID identifier is embedded into a preset position number of the first instruction data stream to obtain the instruction data stream to be stored; and the data ID identifier and the preset position number are shared on the blockchain.

[0050] Specifically, the instruction data stream to be stored is constructed according to the data ID identifier. First, according to the node identifier to be stored, the attribute identifier to be stored and the timestamp identifier to be stored, a first instruction data stream is constructed based on the preset instruction data stream format. The first instruction data stream contains all necessary descriptions and operation instructions for the data to be stored. Then, according to the preset embedding rules, the data ID identifier is inserted into the preset position number of the first instruction data stream to obtain a complete instruction data stream to be stored, wherein the preset position number is a fixed position for storing the data ID for subsequent quick access and identification.

[0051] Specifically, the data ID identification and the corresponding preset bit number are synchronously shared on the blockchain to ensure that the instruction data stream to be stored can be verified and accessed by other users or nodes in the blockchain network, while ensuring the security and transparency of the data.

[0052] In some embodiments, configuring the initial instruction data stream through the instruction parsing channel based on the blockchain client to obtain the instruction target data stream includes:

[0053] The instruction parsing channel stores an instruction data flow framework, wherein the instruction data flow framework has a data transmission target node number; the blockchain user-side association symbol is embedded into the initial instruction data flow based on the data transmission target node number to obtain the instruction target data flow.

[0054] Specifically, the instruction data flow framework stored in the instruction parsing channel defines the data flow bit number of the data transmission target node, that is, the expression position of the data transmission target node in the data flow. The instruction parsing channel embeds the blockchain user-side association symbol into the position corresponding to the data transmission target node bit number in the initial instruction data flow through the instruction data flow framework, thereby generating the instruction target data flow. Among them, the blockchain user-side association symbol is the unique identification information of the blockchain user-side, and exemplarily includes user identity, node information, user-side address, etc.

[0055] By embedding the blockchain user-side association symbols, it is ensured that the generated data stream can be effectively processed and forwarded in the blockchain network.

[0056] Through the anomaly detection channel, an anomaly analysis is performed on the instruction monitoring data flow and the instruction target data flow to obtain a data flow deviation coefficient.

[0057] Specifically, the instruction monitoring data stream and the instruction target data stream correspond to the original data requested by the user and the target data involved in the transmission process respectively. By comparing the instruction monitoring data stream and the instruction target data stream, the abnormal situations in the transmission process can be identified and the degree of deviation can be quantified through the deviation analysis algorithm.

[0058] In some embodiments, performing an abnormality analysis on the instruction monitoring data stream and the instruction target data stream through an abnormality detection channel to obtain a data stream deviation coefficient includes:

[0059] Obtain a first non-random bit data sequence of the instruction monitoring data stream; obtain a second non-random bit data sequence of the instruction target data stream; and count the proportion of the difference data bit numbers between the first non-random bit data sequence and the second non-random bit data sequence, and set it as the data stream deviation coefficient.

[0060] Specifically, the first non-random bit data sequence and the second non-random bit data sequence are specific representations of the same position data sequence in the instruction monitoring data stream and the instruction target data stream. By comparing the first non-random bit data sequence with the second non-random bit data sequence and counting the number of different data bit numbers between the two, the degree of difference between the instruction monitoring data stream and the instruction target data stream can be quantified to generate a data stream deviation coefficient.

[0061] Specifically, the data bit number interval of the non-random bits corresponding to the first non-random bit data sequence and the second non-random bit data sequence is determined based on the data flow definition and monitoring requirements of the target blockchain, thereby providing good adaptability and portability for the monitoring of data instructions.

[0062] Exemplary. The data stream deviation coefficient is calculated based on the following formula: P=D / N; wherein P is the data stream deviation coefficient, D is the number of difference bit numbers between the first non-random bit data sequence and the second non-random bit data sequence, and N is the total number of bit numbers of the data sequence.

[0063] When the data flow deviation coefficient is greater than or equal to a data flow deviation coefficient threshold, the instruction monitoring data flow is marked as an abnormal instruction data flow.

[0064] Specifically, if the data flow deviation coefficient is greater than or equal to the data flow deviation coefficient threshold, it can be considered that the corresponding instruction monitoring data flow has undergone a significant change / variation in the blockchain transmission, and there may be data security risks, and the instruction monitoring data flow is then marked as an abnormal instruction data flow. Optionally, the data flow deviation coefficient threshold can be a value obtained by statistical analysis of historical abnormal records or an empirical value defined by the target scenario technician.

[0065] An information transmission error signal is generated according to the abnormal instruction data flow and sent to the blockchain user end.

[0066] Optionally, an information transmission error signal is constructed based on the abnormal instruction data stream, and the information transmission error signal includes an error type (used to indicate that the abnormality is caused by data flow deviation), an abnormality description (the value of the deviation coefficient, the affected data bit number, etc.), a timestamp, a source node identifier, and data flow information.

[0067] Specifically, through the blockchain network, the error signal is sent to the blockchain user end in a message format, so that the blockchain user end can start the corresponding abnormal response mechanism, which includes, for example, prompting the user to check the source node, triggering the data retransmission mechanism, and recording the abnormal event for subsequent analysis. Through the above steps, it is ensured that the abnormal situation can be fed back to the relevant user end in a timely manner so that appropriate follow-up measures can be taken.

[0068] Furthermore, the method also includes: when the data flow deviation coefficient is less than the data flow deviation coefficient threshold, allowing the blockchain user terminal to interact with the source node on-chain data.

[0069] Specifically, if the data flow deviation coefficient is less than the data flow deviation coefficient threshold, the corresponding data transmission request can be considered safe and compliant, and the data transmission request is processed normally, allowing the blockchain user end to interact with the source node on the chain.

[0070] In summary, the data instruction monitoring method for blockchain information transmission provided by the present invention has the following technical effects:

[0071] By receiving a data transmission request from a blockchain user end, the request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction monitoring data stream; through an instruction parsing channel, the instruction data is configured according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to generate an instruction target data stream; through an anomaly detection channel, an anomaly analysis is performed on the instruction monitoring data stream and the instruction target data stream to calculate the data stream deviation coefficient; when the data stream deviation coefficient is greater than or equal to the preset deviation coefficient threshold, the instruction monitoring data stream is marked as an abnormal instruction data stream; based on the abnormal instruction data stream, an information transmission error signal is generated and sent to the blockchain user end. Thus, the technical effect of improving the efficiency and timeliness of anomaly detection and processing is achieved. Embodiment 2

[0072] Figure 2 This is a schematic diagram of the structure of a data instruction monitoring system for blockchain information transmission in the present invention. For example, Figure 1 The flowchart of a data instruction monitoring method for blockchain information transmission of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0073] Based on the same concept as the data instruction monitoring method for blockchain information transmission in the embodiment, the present invention also provides a data instruction monitoring system for blockchain information transmission, including:

[0074] The request receiving module 11 is used to receive a data transmission request input by a blockchain user terminal, wherein the data transmission request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction to monitor a data flow.

[0075] The instruction parsing module 12 is used to configure instruction data according to the source node identifier, the data attribute identifier, and the storage timestamp identifier through the instruction parsing channel to obtain the instruction target data stream.

[0076] The anomaly detection module 13 is used to perform an anomaly analysis on the instruction monitoring data flow and the instruction target data flow through an anomaly detection channel to obtain a data flow deviation coefficient.

[0077] The abnormality calibration module 14 is used to calibrate the instruction monitoring data flow as an abnormal instruction data flow when the data flow deviation coefficient is greater than or equal to a data flow deviation coefficient threshold.

[0078] The abnormal return module 15 is used to generate an information transmission error signal according to the abnormal instruction data flow and send it to the blockchain user end.

[0079] In some embodiments, the instruction parsing module 12 includes:

[0080] The to-be-stored data acquisition unit is used to obtain the to-be-stored data verified by the blockchain consensus, wherein the to-be-stored data has a to-be-stored node identifier, a to-be-stored attribute identifier, and a to-be-stored timestamp identifier.

[0081] The to-be-stored instruction data stream construction unit is used to construct the to-be-stored instruction data stream of the to-be-stored data according to the to-be-stored node identifier, the to-be-stored attribute identifier and the to-be-stored timestamp identifier.

[0082] The instruction data stream index library construction unit is used to associate and store the instruction data stream to be stored, the node identifier to be stored, the attribute identifier to be stored and the timestamp identifier to be stored, so as to construct the instruction data stream index library.

[0083] The initial instruction data stream matching unit is used to input the source node identifier, the data attribute identifier, and the storage timestamp identifier into the instruction data stream index library to match the initial instruction data stream.

[0084] An instruction target data stream configuration unit is used to configure the initial instruction data stream through the instruction parsing channel and based on the blockchain client to obtain the instruction target data stream.

[0085] In some implementations, the instruction data stream construction unit to be stored in the instruction parsing module 12 includes:

[0086] The data content hash processing unit is used to perform hash processing on the data to be stored to obtain a data content hash value.

[0087] The data portrait hash processing unit is used to perform hash processing according to the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored to obtain a data portrait hash value.

[0088] The data global hash processing unit is used to perform hash processing according to the data content hash value and the data portrait hash value to obtain the data global hash value.

[0089] A data ID generation unit is used to concatenate the data global hash value, the data portrait hash value and the data content hash value to generate a data ID.

[0090] The instruction data stream construction unit is used to construct the instruction data stream to be stored according to the data ID identifier.

[0091] Furthermore, the execution steps of the instruction data stream construction unit in the instruction parsing module 12 include:

[0092] According to the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored, a first instruction data stream is constructed. The data ID identifier is embedded into a preset position number of the first instruction data stream to obtain the instruction data stream to be stored. And the data ID identifier and the preset position number are shared on the blockchain.

[0093] In some implementations, the execution step of the instruction target data flow configuration unit in the instruction parsing module 12 includes: embedding the blockchain user-side association symbol into the initial instruction data flow based on the data transmission target node bit number to obtain the instruction target data flow. The instruction parsing channel stores an instruction data flow framework, wherein the instruction data flow framework has a data transmission target node bit number.

[0094] In some embodiments, the anomaly detection module 13 includes:

[0095] The first non-random bit data sequence acquisition unit is used to obtain the first non-random bit data sequence of the instruction monitoring data stream.

[0096] The second non-random bit data sequence acquisition unit is used to obtain the second non-random bit data sequence of the instruction target data stream.

[0097] The data stream deviation coefficient statistical unit is used to count the proportion of different data bit numbers between the first non-random bit data sequence and the second non-random bit data sequence, and set it as the data stream deviation coefficient.

[0098] Furthermore, the execution step of the abnormal calibration module 14 also includes: when the data flow deviation coefficient is less than the data flow deviation coefficient threshold, allowing the blockchain user terminal to interact with the source node on the chain data.

[0099] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to a data instruction monitoring system for blockchain information transmission described in embodiment two. For the sake of brevity of the specification, no further elaboration is given here.

[0100] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A data instruction monitoring method for blockchain information transmission, characterized in that: include: Receive a data transmission request input by a blockchain user terminal, wherein the data transmission request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction monitoring data flow; Through the instruction parsing channel, instruction data configuration is performed according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to obtain an instruction target data stream; Through an anomaly detection channel, an anomaly analysis is performed on the instruction monitoring data stream and the instruction target data stream to obtain a data stream deviation coefficient; When the data flow deviation coefficient is greater than or equal to a data flow deviation coefficient threshold, the instruction monitoring data flow is marked as an abnormal instruction data flow; Generate an information transmission error signal according to the abnormal instruction data flow and send it to the blockchain user end; Among them, through the abnormal detection channel, the instruction monitoring data flow and the instruction target data flow are analyzed for abnormalities to obtain the data flow deviation coefficient, including: Obtaining a first non-random bit data sequence of the instruction monitoring data stream; Obtaining a second non-random bit data sequence of the instruction target data stream; The proportion of different data bit numbers between the first non-random bit data sequence and the second non-random bit data sequence is counted and set as the data flow deviation coefficient, wherein the first non-random bit data sequence and the second non-random bit data sequence are specific representations of the same position data sequence in the instruction monitoring data flow and the instruction target data flow, and the corresponding non-random bit data bit number interval is determined based on the data flow definition and monitoring requirements of the target blockchain.

2. A data instruction monitoring method for blockchain information transmission as claimed in claim 1, characterized in that: Also includes: When the data flow deviation coefficient is less than the data flow deviation coefficient threshold, the blockchain client is allowed to interact with the source node on-chain data.

3. A data instruction monitoring method for blockchain information transmission as claimed in claim 1, characterized in that: Through the instruction parsing channel, the instruction data is configured according to the source node identifier, the data attribute identifier, and the storage timestamp identifier to obtain the instruction target data stream, which includes: Obtaining data to be stored that has passed blockchain consensus verification, wherein the data to be stored has a node identifier to be stored, an attribute identifier to be stored, and a timestamp identifier to be stored; Constructing a to-be-stored instruction data stream of the to-be-stored data according to the to-be-stored node identifier, the to-be-stored attribute identifier and the to-be-stored timestamp identifier; The instruction data stream to be stored, the node identifier to be stored, the attribute identifier to be stored and the timestamp identifier to be stored are stored in association with each other to construct an instruction data stream index library; Input the source node identifier, the data attribute identifier, and the storage timestamp identifier into the instruction data stream index library to match the initial instruction data stream; Through the instruction parsing channel, based on the blockchain client, the initial instruction data stream is configured to obtain the instruction target data stream.

4. A data instruction monitoring method for blockchain information transmission as claimed in claim 3, characterized in that: Constructing a to-be-stored instruction data stream of the to-be-stored data according to the to-be-stored node identifier, the to-be-stored attribute identifier and the to-be-stored timestamp identifier, including: Performing hash processing on the data to be stored to obtain a data content hash value; Perform hash processing according to the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored to obtain a data portrait hash value; Perform hash processing according to the data content hash value and the data portrait hash value to obtain a global data hash value; Concatenate the data global hash value, the data portrait hash value and the data content hash value to generate a data ID; The instruction data stream to be stored is constructed according to the data ID identifier.

5. A data instruction monitoring method for blockchain information transmission as claimed in claim 4, characterized in that: Constructing the instruction data stream to be stored according to the data ID identifier includes: Constructing a first instruction data stream according to the node identifier to be stored, the attribute identifier to be stored, and the timestamp identifier to be stored; Embed the data ID identifier into the preset bit number of the first instruction data stream to obtain the instruction data stream to be stored; And the data ID identification and the preset position number are shared on the blockchain.

6. A data instruction monitoring method for blockchain information transmission as claimed in claim 4, characterized in that: Through the instruction parsing channel, based on the blockchain client, the initial instruction data stream is configured to obtain the instruction target data stream, including: The instruction parsing channel stores an instruction data stream framework, wherein the instruction data stream framework has a data transmission target node number; The blockchain user-side association symbol is embedded into the initial instruction data stream based on the data transmission target node number to obtain the instruction target data stream.

7. A data instruction monitoring system for blockchain information transmission, characterized in that: The system is used to execute a data instruction monitoring method for blockchain information transmission according to any one of claims 1 to 6, and the system includes: A request receiving module, the request receiving module is used to receive a data transmission request input by a blockchain user terminal, wherein the data transmission request includes a source node identifier, a data attribute identifier, a storage timestamp identifier, and an instruction monitoring data flow; An instruction parsing module, the instruction parsing module is used to configure instruction data according to the source node identifier, the data attribute identifier, and the storage timestamp identifier through an instruction parsing channel to obtain an instruction target data stream; An anomaly detection module, the anomaly detection module is used to perform an anomaly analysis on the instruction monitoring data flow and the instruction target data flow through an anomaly detection channel to obtain a data flow deviation coefficient; An abnormal calibration module, wherein the abnormal calibration module is used to calibrate the instruction monitoring data flow as an abnormal instruction data flow when the data flow deviation coefficient is greater than or equal to a data flow deviation coefficient threshold; An abnormal return module is used to generate an information transmission error signal according to the abnormal instruction data flow and send it to the blockchain user end.

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