Gas station tax control data acquisition method and system based on block chain

Abstract

The invention relates to the technical field of data acquisition, in particular to a gas station tax control data acquisition method and system based on a block chain, and the method comprises the following steps: based on a block chain technology, analyzing the operation time sequence of a business process, comparing the operation sequence of each terminal under the same event, and judging and counting the dislocation frequency; screening node broadcast response, comparing response interval and field integrity, calculating operation copy distribution characteristics, judging synchronous offset, checking field consistency, counting role operation frequency, and obtaining various stability and difference parameters. According to the method, the time sequence and the behavior of each operation node are continuously monitored, and the dynamic judgment of the operation sequence between the terminals and the node collaborative state mapping are combined, so that the data synchronization process between the multiple terminals is more accurate, the abnormal offset in the data link can be positioned in time, and the data transmission efficiency is improved during node data transmission. And in combination with response time sequence distribution and a field integrity label, a real-time analysis mechanism of a link state is formed, and rapid early warning of abnormal broadcast and data missing is realized.

Description

Technical Field

[0001] The present invention relates to the field of data collection technology, and in particular to a method and system for collecting tax control data for gas stations based on blockchain. Background Art

[0002] Data collection refers to the technology that uses sensing, automatic recording, remote monitoring, data transmission and other means to automatically collect and store raw information generated by various devices, systems or business scenarios in real time or on a regular basis. It covers data collection, processing, transmission, storage and management, and is widely used in many scenarios such as industrial control, business management, Internet of Things, smart cities, financial monitoring, etc.

[0003] Among them, the blockchain gas station tax control data collection method refers to the use of blockchain technology to automatically and reliably collect tax control data generated by gas stations during their operations. Its purpose is to achieve efficient and secure collection and supervision of gas station tax control data through the decentralized, tamper-proof and traceable characteristics of blockchain, improve data transparency, prevent data falsification, and provide a real and reliable data foundation for both tax authorities and enterprises.

[0004] Existing data collection is mainly based on single-point devices and static records. The timing between terminals is often out of sync, which can easily cause confusion in the data sequence or partial data missing, affecting subsequent business verification and data review. It is difficult to monitor the link status in real time during data transmission, and abnormal delays or data packet loss between nodes are discovered with a delay. There is a lack of active feedback mechanism for abnormal links. The consistency of replicas relies on basic data comparison. It is difficult to judge the synchronization offset and mismatch between node replicas in complex network fluctuations and multi-terminal high concurrency scenarios, resulting in misjudgment and omission of business data. The data consistency verification is not deep enough, the traceability link for abnormal content is not clear, and it is easy to leave security risks. The supervision of permissions and operational behaviors is extensive, and there is a lack of timely monitoring and hierarchical response for frequent unauthorized operations and abnormal operations, which aggravates the compliance risk of business operations. When equipment fails or the link is blocked, tax control data is prone to breakage, affecting data authenticity and business response. Summary of the Invention

[0005] In order to solve the problems in the prior art that data collection is mainly based on single-point devices and static records, the timing between terminals is often out of sync, which easily causes confusion in the order of data or partial data missing, affecting subsequent business verification and data review, it is difficult to monitor the link status in real time during data transmission, abnormal delays or data packet loss between nodes are discovered late, there is a lack of active feedback mechanism for abnormal links, the consistency of replicas relies on basic data comparison, it is difficult to judge the synchronization offset and mismatch between node replicas in complex network fluctuations and multi-terminal high concurrency scenarios, resulting in misjudgment and omission of business data, insufficient depth of data consistency verification, unclear traceability links for abnormal content, easy to leave behind security risks, extensive supervision of authority and operation behavior, lack of timely monitoring and hierarchical response for frequent unauthorized operations and abnormal operations, aggravated compliance risks of business operations, and tax control data is prone to breakage when equipment fails or links are blocked, affecting data authenticity and business response, the embodiment of the present invention provides a gas station tax control data collection method and system based on blockchain. The technical solution is as follows:

[0006] On the one hand, a method for collecting tax control data at gas stations based on blockchain is provided, comprising the following steps:

[0007] S1: Based on blockchain technology, we analyze the operation sequence of refueling services, compare the operation sequence of different terminals under the same event, determine whether the record sequence is misaligned, calculate the misalignment frequency, and obtain the node synchronization stability;

[0008] S2: Based on the node synchronization stability, screen the response time points of each node receiving the same data packet, analyze the node response sequence, determine whether there are any anomalies, count the number of missing fields, and obtain the broadcast timing integrity parameter;

[0009] S3: Based on the broadcast timing integrity parameter, calculate the replicas of the same type of operation between nodes, analyze the combination of device upload time and node number, screen the distribution characteristics in the historical data, determine the combination frequency deviation, and obtain the replica synchronization offset characteristics;

[0010] S4: Based on the replica synchronization offset feature, compare the field contents of the transaction details between the nodes on the chain, analyze the character composition of the same data fields, determine the content consistency, count the number of inconsistent positions, and obtain the field consistency difference;

[0011] S5: Based on the field consistency difference, the number of operation requests of each role is screened, the association between the operation frequency and the role authority is analyzed, and whether the number of operations exceeds the limit is determined to obtain the permission frequency exceeding limit feature.

[0012] On the other hand, the node synchronization stability includes timing consistency index, terminal synchronization status, and node collaboration identifier; the broadcast timing integrity parameters include response sequence characteristics, synchronization delay identifier, and field integrity level; the replica synchronization offset characteristics include synchronization distribution identifier, device timing matching items, and operation replica mapping type; the field consistency difference includes difference distribution identifier, field comparison status, and content consistency level; the permission frequency limit exceeding characteristics include exceeding role category, frequency distribution item, and permission warning signal.

[0013] On the other hand, the steps of node synchronization stability are specifically as follows:

[0014] S101: Based on blockchain technology, collect operational time series data from fuel dispenser control terminals, fuel nozzle sensors, payment terminals, and tax control terminals. Based on the event time series collected by each business terminal, detect the order of event records, and use this to filter out the order misalignment of records from each terminal to obtain the number of order misalignments.

[0015] S102: Based on the number of sequence misalignments, and according to the operation sequence data corresponding to the nodes, compare the order of operations of each node and the time sequence changes, determine the time sequence position of each node under the same event for each group of operation sequence, and calculate the distribution of misaligned nodes to obtain the node misalignment distribution amount;

[0016] S103: Calculate the ratio between the number of node misalignments and the total number of nodes under the same event based on the node misalignment distribution, determine the misalignment frequency of each node, and obtain the node synchronization stability.

[0017] On the other hand, the step of broadcasting the timing integrity parameter is specifically:

[0018] S201: screening the broadcast response time of each segmented data packet at each node based on the node synchronization stability, comparing the time sequence of each node receiving the same segmented data packet, determining the response order of each node, and obtaining a node response sequence;

[0019] S202: calling the node response sequence, comparing the response time distribution of each node to the same data packet, analyzing the interval variation of the node response, determining whether the response sequence of each node in the same broadcast process has discontinuous or abnormal changes, and obtaining the interval fluctuation characteristics;

[0020] S203: Based on the interval fluctuation characteristics, compare the field data of each node in each segmented data packet, count the number of missing fields, and obtain the broadcast timing integrity parameter according to the distribution of abnormal intervals and field missing situations.

[0021] On the other hand, the steps of replica synchronization offset feature are specifically as follows:

[0022] S301: Based on the broadcast timing integrity parameter, calculate the upload time of the same type of operation copies between the distributed ledger nodes, filter each group of device upload time and node number combinations, determine the occurrence of the combination between the nodes, and obtain the node combination distribution characteristics;

[0023] S302: Compare the distribution changes of the node combination distribution characteristics among the nodes, determine whether the distribution of the parameter combination among the same type of operation replicas has abnormal changes, and obtain the replica synchronization offset characteristics by dividing the distribution interval and adjusting the abnormal interval.

[0024] On the other hand, by dividing the distribution interval, the formula is adopted:

[0025]

[0026] Calculate the parameter combination distribution offset rate, adjust the abnormal interval, and obtain the replica synchronization offset characteristics, where θ j represents the distribution deviation rate of the jth node parameter combination, B j represents the number of copies of the same type of operation under the jth node, p oj Represents the parameter combination of the o-th copy of the same type of operation on the j-th node, represents the average parameter combination of all replicas of the same type of operation on the jth node, t oj represents the upload time of the oth copy of the same type of operation on the jth node, Represents the average upload time of all replicas of the same type of operation on the j-th node.

[0027] On the other hand, the steps of determining the field consistency difference are specifically as follows:

[0028] S401: Based on the replica synchronization offset feature, compare the field contents of the transaction details to be verified between the nodes on the chain, determine whether the character composition of the same data fields is consistent, filter out the data fields with content differences, and obtain the node comparison position number;

[0029] S402: Based on the number of node comparison positions, calculate their distribution in the transaction data to be verified, analyze the content structure of similar fields and the node comparison results, determine whether each comparison content is consistent, count the number of inconsistent field positions, and obtain the content distribution number;

[0030] S403: Based on the content distribution quantity, count the inconsistencies in the transaction details fields between the nodes, compare the difference distribution of each type of field, and summarize the field comparison anomalies to obtain the field consistency difference amount.

[0031] On the other hand, the inconsistencies in the transaction details fields between the nodes are counted and the difference distribution of each type of field is compared using the formula:

[0032]

[0033] Calculate the difference distribution characteristic value and summarize the field comparison anomalies to obtain the field consistency difference, where: Represents the differential distribution characteristic value of the i-th category field at the j-th node, Represents the field comparison value of the k-th data of the i-th category field at the j-th node, Represents the average value of the comparison of all data fields of the i-th category field at the j-th node, Represents the total number of data counted for the i-th field at the j-th node, Represents the number of field inconsistencies for the i-th field in all nodes.

[0034] On the other hand, the steps of the permission frequency exceeding limit feature are specifically as follows:

[0035] S511: Based on the field consistency difference, the number of operation requests for each role within the time interval is screened, the correlation between the operation frequency of each role and the role authority is analyzed, and whether there is any discrepancy between the number of operation requests and the authority category is determined to obtain the permission operation distribution;

[0036] S512: Based on the distribution of permission operations, sort out the operation requests of each role that exceed the permission range, count the frequency of over-limit operations for each role type, and analyze the distribution characteristics of over-limit requests in each role type to obtain the permission frequency over-limit characteristics.

[0037] On the other hand, a blockchain-based gas station tax control data collection system is provided. The system is applied to a blockchain-based gas station tax control data collection method, including:

[0038] The timing consistency judgment module, based on blockchain technology, analyzes the timing of operations during refueling initiation, fuel nozzle activation, refueling completion, payment initiation, and tax control generation. It compares the operation sequence of different terminals under the same event, determines whether there is any misalignment in the recording sequence of each terminal, calculates the frequency of misaligned nodes, and obtains the node synchronization stability.

[0039] The response synchronization monitoring module screens the broadcast response time of each segmented data packet at each node based on the node synchronization stability, analyzes the response order of the nodes to the same data packet, compares the distribution of the node broadcast response intervals, and counts the field missing conditions to obtain the broadcast timing integrity parameter;

[0040] The replica offset analysis module calculates the replicas of the same type of operations between distributed ledger nodes based on the broadcast timing integrity parameters, analyzes the combination of device upload time and node number, screens the distribution characteristics of parameter combinations in historical data, determines whether the combination frequency deviates from the normal distribution, and obtains the replica synchronization offset characteristics;

[0041] The data consistency comparison module compares the field contents of the transaction details to be verified between the on-chain nodes based on the replica synchronization offset characteristics, analyzes the character composition of similar data fields, determines whether the content of each comparison is consistent, and counts the number of inconsistent locations of each data field to obtain the field consistency difference;

[0042] The permission frequency control module screens the number of operation requests of each role within a time period based on the field consistency difference, analyzes the relationship between the operation frequency and the role permission, determines whether the number of operations exceeds the permission range of the role category, and obtains the permission frequency exceeding limit feature.

[0043] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0044] By continuously monitoring the timing and behavior of each operation node, combined with the dynamic identification of the operation sequence between terminals and the node collaborative status mapping, the data synchronization process between multiple terminals is more accurate, and abnormal offsets in the data link can be located in time. When the node data is transmitted, the response timing distribution and field integrity label are combined to form a real-time analysis mechanism for the link status, and rapid early warning of abnormal broadcasts and data loss is achieved. When replicas are synchronized, behavioral feature mapping and distribution judgment are used to dynamically reveal the synchronization anomalies and behavioral differences between the replicas of each node, thereby improving the level of replica consistency management. On-chain comparison archives the content item by item and traces it in a hierarchical manner to enhance data consistency and traceability. Operation behavior monitoring is linked according to permissions and frequency to generate sensitive operation warning labels, further improving the timeliness and pertinence of risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 It is a main step flow chart of the present invention;

[0047] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0048] Figure 3 This is a flow chart of the steps of S2 of the present invention;

[0049] Figure 4 This is a flow chart of the steps of S3 of the present invention;

[0050] Figure 5 This is a flow chart of the steps of S4 of the present invention;

[0051] Figure 6 This is a flow chart of the steps of S5 of the present invention;

[0052] Figure 7 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0054] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0055] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0056] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0058] The embodiment of the present invention provides a method for collecting tax control data of gas stations based on blockchain, such as Figure 1 As shown, the following steps are included:

[0059] S1: Based on blockchain technology, we analyze the operational sequence of refueling start, fuel nozzle activation, refueling completion, payment initiation, and tax control generation. We compare the operation sequence of different terminals under the same event to determine whether there is any misalignment in the recording sequence of each terminal. We sort out the sequence and timing changes of each node operation, calculate the frequency of misaligned nodes, and obtain the node synchronization stability.

[0060] S2: Based on the node synchronization stability, the broadcast response time of each segmented data packet at each node is screened, the node response order to the same data packet is analyzed, the distribution of the node broadcast response interval is compared, and whether there are abnormal changes in each response interval is determined. The number of missing fields is counted to obtain the broadcast timing integrity parameter;

[0061] S3: Based on the broadcast timing integrity parameter, the replicas of the same type of operation between distributed ledger nodes are calculated. The combinations of device upload time and node number are analyzed. The distribution characteristics of parameter combinations in historical data are screened. The combination frequency is determined to determine whether it deviates from the normal distribution. This is determined by interval division to obtain the replica synchronization offset characteristics.

[0062] S4: Based on the replica synchronization offset characteristics, compare the field contents of the transaction details to be verified between the on-chain nodes, analyze the character composition of similar data fields, determine whether the content of each comparison is consistent, count the number of inconsistent locations of each data field, and summarize the inconsistent items to obtain the field consistency difference;

[0063] S5: Based on the field consistency difference, filter the number of operation requests for each role within a certain period of time, analyze the relationship between operation frequency and role permissions, determine whether the number of operations exceeds the permission range of the role category, record each operation request that exceeds the limit, and obtain the permission frequency exceeding limit characteristics.

[0064] Node synchronization stability includes timing consistency indicators, terminal synchronization status, and node collaboration identification. Broadcast timing integrity parameters include response sequence characteristics, synchronization delay identification, and field integrity level. Replica synchronization offset characteristics include synchronization distribution identification, device timing matching items, and operation replica mapping types. Field consistency difference includes difference distribution identification, field comparison status, and content consistency level. Permission frequency limit exceeding characteristics include exceeding role category, frequency distribution items, and permission warning signals.

[0065] In S1, the term "different terminals" refers to different business operation terminals in the gas station business scenario, such as the gas station control terminal, fuel gun sensor, payment terminal, tax control terminal, etc. The event data collected by each terminal appears in a different order or with inconsistent information under the same operation process; each node refers to the different nodes (servers or devices) in the blockchain network that participate in data synchronization, evidence storage, and comparison, such as the local server of the gas station, the headquarters server, or the blockchain alliance chain member node. It can also generally refer to each business terminal as a node participating in the full-process data interaction; timing change refers to the change in the time sequence of the above-mentioned operation events (such as refueling start, fuel gun activation, etc.) during the actual business process due to the influence of device response, network synchronization, etc., that is, the time series difference presented when each node records the same event; the misaligned node refers to the node whose actual recording order is inconsistent with the theoretical process order after timing comparison (that is, the terminal or blockchain node where the operation data record is misaligned).

[0066] In S2, the broadcast response time refers to the specific time point when each blockchain or business node receives the same segmented data packet broadcast (data synchronization push); the response order refers to the order in which multiple nodes receive the same data broadcast, and the consistency and timeliness of the network or terminal response are analyzed by comparing the order; abnormal changes refer to unexpected fluctuations found in the response sequence and response interval distribution, such as a significant delay in the response time of some nodes, an abnormally long interval, etc., which are significantly different from the consistency of the predetermined process; field missing refers to the fact that in the data packet synchronized by the node broadcast, some required fields (such as oil product information, amount, equipment identification, etc.) are not fully recorded or transmitted, resulting in missing data items.

[0067] In S3, distributed ledger nodes refer to the nodes (servers or devices) deployed in the blockchain network that have data storage, synchronization and verification capabilities, and jointly maintain data consistency; operation copies refer to multiple copies of data recorded and synchronized on different nodes for the same business event (such as a refueling transaction) for comparison and verification; involved devices refer to various hardware devices involved in the business data collection, transmission and evidence storage process, such as refueling terminals, payment devices, tax control devices, etc.; node number refers to the number or identification code used to uniquely identify the identity of each node in the blockchain network, which is often used to trace and compare data synchronization processes; normal distribution refers to the normal distribution pattern or characteristic interval of various parameters, operation frequency, and field content obtained from statistical analysis of historical data, which is used to determine deviations from current data.

[0068] In S4, on-chain nodes refer to servers, business terminals or institutional nodes on the blockchain network that have data reading, writing and consensus capabilities and participate in data storage and verification; the number of positions refers to the number of specific fields marked with inconsistent content during the data field comparison process (that is, how many times the inconsistent fields appear).

[0069] In S5, each role refers to the data operation subject involved in the business process, such as gas station attendants, station managers, financial personnel, tax regulators, etc.; the scope of authority refers to the authorization boundaries such as the operable data category, number of operations, access time period, etc. assigned by the system to different roles, which is the basis for implementing hierarchical authority management and compliance monitoring.

[0070] like Figure 2 As shown, the steps for node synchronization stability are as follows:

[0071] S101: Based on blockchain technology, collect operational time series data from fuel dispenser control terminals, fuel nozzle sensors, payment terminals, and tax control terminals. Based on the event time series collected by each business terminal, detect the order of event records, and use this to filter out the order misalignment of records from each terminal to obtain the number of order misalignments.

[0072] The event time data collected by the fuel dispenser control terminal, fuel gun sensor, payment terminal and tax control terminal in a transaction process are collected separately. The events collected by each terminal include three basic elements: timestamp, terminal number and event type. For example, the fuel dispenser control terminal records the "start" event time as 08:00:02, numbered A01, the fuel gun sensor records the "lift gun" event time as 08:00:05, numbered B01, the payment terminal records the "scan code" event time as 08:00:12, numbered C01, and the tax control terminal records the "tax control generation" event time as 08:00:25, numbered D01. According to the standard sequence of the fueling business process, the order is "fueling start → fuel gun activation → payment initiation → tax control Generate", arrange and compare the time recorded by each terminal with the standard sequence, sort the event time by terminal and construct the actual process sequence. If the actual payment time is earlier than the gas gun activation time, that is, there is a discrepancy between the logical sequence and the time sequence, which is marked as a misalignment. If there are two reversed sequences in a transaction process, it is counted as two misalignments. By traversing 50 transaction sample records, comparing the sequence of events between terminals one by one, and extracting all the misalignments, a total of 82 incorrect sortings were found. Each misalignment was associated with the terminal ID, event ID and timestamp offset. Finally, the records were merged into a set of misalignment event forms for use in the next step of misalignment node analysis, and the result was the number of sequence misalignments.

[0073] S102: Based on the number of sequence misalignments, the order of operations and the timing changes of each node are compared according to the operation sequence data corresponding to the node. For each set of operation timings, the timing sequence position of each node under the same event is determined, and the distribution of misaligned nodes is counted to obtain the node misalignment distribution amount.

[0074] According to the number of sequence misalignments identified in the first step, the event time series uploaded by each node is read, and an event-node two-dimensional structure is established. Event records of the same type in all transaction samples are vertically aggregated by node. For example, the "fuel gun activation" event is recorded on nodes B01, C01, and D01 at 08:00:05, 08:00:04, and 08:00:06, respectively. The time sequence should be B01→C01→D01. However, if the recording time of C01 is earlier than B01, node C01 is marked as a misaligned node in this event. According to this logic, the same type of events in all transaction data are scanned and the time sequence positions are compared. In each transaction, the events are aggregated separately. Count the number of locations where misplaced terminals appear, and summarize the number of misplacements according to the node number. For example, node C01 participated in recording 45 oil gun activation events in 50 transactions, of which misplacement occurred 16 times. Then the number of misplacements of node C01 under this event category is 16 times, which are recorded in the node misplacement details table. For another example, node D01, as a tax control terminal, participated in tax control record generation 50 times in 60 transactions, and misplaced records were counted 9 times. Then the number of misplacements of node D01 under tax control events is marked as 9 times. Complete the misplacement data statistics of all nodes and all event types in turn, and finally output the misplacement distribution of each node under multiple event types to form a misplacement frequency list.

[0075] S103: Calculate the ratio between the number of node misalignments and the total number of nodes under the same event based on the node misalignment distribution, determine the misalignment frequency of each node, and obtain the node synchronization stability;

[0076] Calculate the dislocation frequency of each node in the whole event sample, divide the number of dislocations of a node into the total number of event records in which it participates, and if the dislocation frequency exceeds 0.30, it is judged as an unstable node. For example, node A01 participated in 40 records in total, had 14 dislocations, and a dislocation frequency of 0.35. This value is higher than 0.30, so A01 is marked as a high-frequency dislocation node. For another example, node B02 participated in 65 event records, had only 2 dislocations, and a frequency of 0.03, so this node is considered a synchronously stable node. Arrange the dislocation frequencies of all nodes in descending order, and sort the nodes with a frequency greater than or equal to 0.30 into stable nodes. The nodes are classified into the synchronization anomaly set, and the remaining nodes are included in the normal synchronization set. This grouping information is used as a reference for the subsequent data verification priority strategy. The benchmark value of 0.30 for judging the misalignment frequency is based on the statistical laws of historical transaction data. After analyzing 1,000 complete transaction samples at gas stations, it was found that 92% of the nodes had a misalignment frequency of less than 30% in the entire process. Therefore, this value is set as the demarcation value for determining the node synchronization stability, and 12 terminal nodes are identified as being in the abnormal range, accounting for 28.5% of the total number of terminals. This ratio is dynamically updated as the transaction volume increases, and the classification information of the node synchronization stability is obtained.

[0077] like Figure 3 As shown, the steps for broadcasting timing integrity parameters are as follows:

[0078] S201: Based on the node synchronization stability, screen the broadcast response time of each segmented data packet at each node, compare the timing sequence of each node receiving the same segmented data packet, determine the response order of each node, and obtain the node response sequence;

[0079] Prioritize the selection of nodes with synchronization stability higher than 0.7 as valid participating nodes, read the broadcast response time information of each segmented data packet received by the node in turn, set a unique identification ID for each data packet, and then arrange the response time of different nodes under the ID in ascending order according to the timestamp. Set the first response time as the broadcast starting reference point of the data packet, record the data packet reception time corresponding to each node one by one, build a data packet reception sequence, and then perform a longitudinal comparison of the timestamps of all nodes receiving the same data packet to determine whether the response sequence of each node has the same timestamp or is too close. For example, during the broadcast of a data packet "PK1001", the response time of node A is 09:00:01.025, and that of node B is 0 9:00:01.050, and node C is 09:00:01.240, then the response order of the three nodes is recorded as A→B→C. If there is a node time difference of 09:00:01.025 and 09:00:01.026, it is determined to be an approximate response and the relative order difference is recorded as 1ms. The above response order extraction operation is performed for each data packet. If a node has a response time earlier than other nodes in multiple data packets but the broadcast does not reach the geographical location of the node, then the node is marked as a response order anomaly source, and at the same time, it is detected whether there is time inversion, that is, the node responds later but is marked as received first. After the response order extraction of all nodes is completed, the node response sequence corresponding to each data packet is output.

[0080] S202: Calling the node response sequence, comparing the response time distribution of each node to the same data packet, analyzing the interval changes of the node response, and determining whether the response sequence of each node in the same broadcast process has discontinuous or abnormal changes, thereby obtaining the interval fluctuation characteristics;

[0081] Read the response time difference between two adjacent nodes and calculate the response time interval sequence of all nodes to the same data packet. For example, the response time of node A is 09:01:12.100, the response time of node B is 09:01:12.180, and the interval is 80ms. For another example, the response time of node B and node C is 09:01:12.180 and 09:01:12.850, respectively, with an interval of 670ms. A complete response time interval sequence is formed with each data packet as the unit. The interval sequence is sorted and compared item by item with the average response interval of the previous 10 broadcasts. The interval difference is compared with the benchmark interval difference. Perform direct subtraction and set the abnormal identification interval. If the time interval is greater than 300ms, it is marked as a slow-responding node. If the time interval is less than 10ms, it is marked as a close-responding node. Further determine whether there are nodes with discontinuous response order. That is, in the original order, node numbered N3 should respond after node N2, but the actual response time is later than node N4. This phenomenon is considered as response inversion. If it exists, record the data packet sequence as a discontinuous sequence. Combined with statistical information, mark the abnormal interval type, such as jump interval, lag interval, and aggregation interval. Count each type of interval and output the interval fluctuation characteristics of the data packet.

[0082] S203: Based on the interval fluctuation characteristics, compare the field data of each node in each segmented data packet, count the number of missing fields, and obtain the broadcast timing integrity parameter based on the distribution of abnormal intervals and field missing situations;

[0083] The node reads the field content data recorded for the packet and extracts the field content one by one. The expected total number of fields is set to 32. If the actual number of extracted fields is less than 32, it is considered to have missing fields. The number of missing fields in all nodes of the packet is counted. For example, node A records 30 fields and is missing 2, while node B records 29 fields and is missing 3. The total number of missing fields in all nodes is counted in this way. The node identification number with the most missing fields in the packet is recorded and added to the field anomaly table. The intersection of the field missing node number and the interval abnormal node number is compared. If a node appears in both the field missing and interval fluctuation lists, it is recorded as a high-risk node. The overlap rate of abnormal intervals and field missing is calculated for each packet. If more than 50% of the nodes have both anomalies, the packet is marked as having weak temporal integrity. If only a few nodes have missing fields but the intervals are stable, the packet is marked as having medium temporal integrity. Conversely, packets with no missing fields and minimal interval fluctuation are marked as high-integrity broadcast packets. The broadcast temporal integrity parameters are generated by combining the processing results of all packets.

[0084] like Figure 4 As shown, the steps of replica synchronization offset feature are as follows:

[0085] S301: Based on the broadcast timing integrity parameter, calculate the upload time of the same type of operation copies between the distributed ledger nodes, filter each group of device upload time and node number combinations, determine the occurrence of the combination between the nodes, and obtain the node combination distribution characteristics;

[0086] Filter out replica data with an integrity level of "medium" or above from the node records, locate all replica records of each similar operation in the distributed ledger, extract their upload time and the node number to which they belong, generate a "upload time-node number" combination list, standardize the upload time to the millisecond, and process it in groups by data packet ID. For example, for a refueling event with oil product number A1002, the upload time is recorded as 10:05:12.210, 10:05:12.235, and 10:05:13.005 on nodes ND01, ND03, and ND06, respectively. It is determined that there is a time span of about 800 milliseconds between the nodes for this event, which is recorded as medium deviation. For example, another event copy appears in the upload of ND02 and ND04 at 10:08:45.050 and 10:08:45.060 respectively, with a difference of only 10 milliseconds. It is classified as a high consistency combination. The reference value of the time interval is set. The upload time interval ≤ 50 milliseconds is high consistency, 51-500 milliseconds is medium consistency, and > 500 milliseconds is low consistency. Combined with the combination frequency, the number of transactions in which the same combination appears is counted. For example, if a combination appears 25 times in 30 transactions, it is marked as a high-frequency stable combination, and those that appear less than 2 times are classified as sporadic combinations. All combinations are summarized according to the degree of timing deviation and the frequency of occurrence in transactions to obtain the node combination distribution characteristics.

[0087] S302: Compare the distribution changes of node combination distribution characteristics among various nodes, determine whether the distribution of parameter combinations among replicas of the same type of operation has abnormal changes, divide the distribution intervals, adjust the abnormal intervals, and obtain the replica synchronization offset characteristics;

[0088] By dividing the distribution interval, the formula is used:

[0089]

[0090] Calculate the parameter combination distribution offset rate, adjust the abnormal interval, and obtain the replica synchronization offset characteristics, where θ j represents the distribution deviation rate of the jth node parameter combination, B j represents the number of copies of the same type of operation under the jth node, p oj Represents the parameter combination of the o-th copy of the same type of operation on the j-th node, represents the average parameter combination of all replicas of the same type of operation on the jth node, t oj represents the upload time of the oth copy of the same type of operation on the jth node, Represents the average upload time of all copies of the same type of operation on the jth node;

[0091] The parameter combination distribution drift rate is a quantitative indicator used to measure the complex dynamic relationship between the structural drift in parameter combinations and upload timing fluctuations of the same type of operation replicas on the same node. This indicator serves as a basis for determining replica synchronization drift characteristics, identifying which operation replicas have weak structural consistency or poor temporal stability during on-chain transmission. Once the drift rate exceeds the limit of a certain statistical distribution interval, the corresponding node is determined to have a "replica distribution drift anomaly" and must enter the abnormal interval adjustment and data verification process.

[0092] B j =4, identifying and counting the copies of this type of operation event under the jth node by monitoring the system log records, and detecting a total of 4 refueling business copy records;

[0093] p oj : The parameter combination collected in real time by the blockchain node covers three quantitative fields: the hash value fragment corresponding to the device number, the service type code, and the pump ID check value. By weighted encoding these fields using character length and normalizing them, the following values ​​can be obtained (the unit is uniformly standardized ratio);

[0094] p 1j =0.642, p 2j =0.686, p 3j =0.660, p 4j =0.712;

[0095] All p oj The arithmetic mean of:

[0096]

[0097] t oj Extracted from the on-chain log timestamp (seconds), the data is as follows:

[0098] t 1j =9.8, t 2j =11.2, t 3j =10.7, t 4j =11.3;

[0099] Average upload time:

[0100]

[0101] The item-by-item calculation process is as follows:

[0102] calculate

[0103]

[0104] calculate

[0105]

[0106] Substitute into the formula to calculate θ j :

[0107]

[0108] The result shows that in the current operation copy of the j-th node, there is no systematic deviation in the parameter combination among similar copies, indicating that the copy data distribution of this node remains highly consistent during this period. There are slight fluctuations in the upload timing, but they will not interfere with the deviation judgment. The deviation rate result can be directly classified into the stable interval and the abnormal interval division process is eliminated.

[0109] like Figure 5 As shown in the figure, the steps for determining the field consistency difference are as follows:

[0110] S401: Based on the replica synchronization offset feature, compare the field contents of the transaction details to be verified between the nodes on the chain to determine whether the character composition of the same data fields is consistent. Filter the data fields with content differences to obtain the node comparison position number.

[0111] Taking the same refueling transaction to be verified as a unit, retrieve the operation copies retained in multiple on-chain nodes from the blockchain, extract the transaction details field content one by one, and establish a field comparison table for the content of each node copy according to the field position. The fields are numbered in a logical sequence, and character-level comparison is performed from the first field to the last item. Check item by item whether the character combination stored in different nodes for the same field is consistent. For example, if field F03 is "oil model", node A records it as "92# gasoline", and node B records it as "92# gasoline", then the comparison result is marked as inconsistent, and then the position number "F03" of the field in the comparison table is extracted and included in the difference field statistics. When checking the character consistency of all fields, a bit-by-bit scanning method is used to compare whether the ASCII code value of each character matches. If the character length is different or the character position is offset, it is also considered inconsistent. Further judgment is made whether the inconsistent field only appears in the copy of a specific node. If there are multiple inconsistent items in a node, it is additionally marked as a high-frequency difference node. If a field is different in more than 70% of the nodes, the field is marked as a volatile field. The position numbers of all the different fields in each node are accumulated to form a comparison position set, and finally the position number of the field content difference between the nodes of the transaction is output, that is, the number of node comparison positions.

[0112] S402: Based on the number of node comparison positions, calculate their distribution in the transaction data to be verified, analyze the content structure of similar fields and the node comparison results, determine whether each comparison content is consistent, count the number of inconsistent field positions, and obtain the content distribution number;

[0113] Summarize the position numbers of the corresponding fields in all nodes, group them by field numbers, and count the distribution characteristics of inconsistent fields in the transaction structure. First, extract the total number of fields as the comparison benchmark value. Assume that a transaction contains a total of 38 fields. If the comparison finds that fields F03, F08, and F21 have content differences in multiple nodes, then record the difference as 3 fields, and calculate their proportion in the entire field structure as 3 / 38. Perform interval judgment on this proportion. A proportion less than 10% is marked as low variation distribution, 10% to 30% is medium variation, and more than 30% is high variation distribution. Then analyze each difference field item one by one. The content structure of the segment is determined to determine whether the source of the difference is character spelling, inconsistent units, encoding errors or missing fields. The character composition difference type and node source are recorded one by one, and the frequency of occurrence of each type of difference is counted. At the same time, the consistency count of each node in the full field structure is recorded. If a node has only one field out of 38 that is inconsistent with other nodes, its consistency ratio is 97.37%, and it is marked as a highly consistent node. If the consistency ratio is lower than 85%, it is regarded as a low consistency node. Finally, the distribution status of all field comparison positions in each transaction in the structure and the number of difference frequencies are summarized to generate the field content distribution number.

[0114] S403: Based on the content distribution quantity, count the number of inconsistencies in the transaction details fields between each node, compare the difference distribution of each type of field, and summarize the field comparison anomalies to obtain the field consistency difference amount;

[0115] Count the inconsistencies in the transaction details fields between nodes and compare the difference distribution of each type of field using the formula:

[0116]

[0117] Calculate the difference distribution characteristic value and summarize the field comparison anomalies to obtain the field consistency difference, where: Represents the differential distribution characteristic value of the i-th category field at the j-th node, Represents the field comparison value of the k-th data of the i-th category field at the j-th node, Represents the average value of the comparison of all data fields of the i-th category field at the j-th node, Represents the total number of data counted for the i-th field at the j-th node, Represents the number of inconsistent fields in all nodes of the i-th type field, k is The summation count symbol within the range, i represents the field type number, and j represents the node number;

[0118] The differential distribution characteristic value is a comprehensive indicator used to measure the field data consistency stability and global network inconsistency pressure of a certain type of field in a specific node. It is essentially a quantitative value of the consistency quality of a structured field, combining the inconsistent distribution of the field within the node (local variance) and the amount of data identified as abnormal in the field during synchronization between all nodes (global offset). It is used to quickly identify high-risk field characteristic bits with poor field stability, high volatility, and high conflict frequency in a multi-node and multi-field environment.

[0119] The field value consistency comparison system collects the k-th record of the i-th category field at the j-th node and determines whether it is consistent. The value 0 indicates consistency and 1 indicates inconsistency. The field character array is compared bit by bit and normalized and quantized.

[0120] for The arithmetic mean is calculated by summing up the archived collection records and dividing by

[0121] The node synchronization data audit system records this field in the sampling window and compares a total of 5 transaction records;

[0122] The cumulative number of inconsistencies in the i-th category field collected from the synchronized data packets of each blockchain node is obtained by merging the field comparison defect statistics submodule. Based on the test data, this time there are 11 inconsistencies;

[0123] The data are:

[0124] Select field type i=4, payment amount field;

[0125] Node number j=1, refueling terminal node;

[0126] The difference between the actual field content and the CRC checksum is identified and summarized;

[0127] Obtained from statistics on the number of conflicting tags for this field in the synchronized data;

[0128] From the sample data row number record;

[0129] Average value calculation:

[0130]

[0131] Squared difference calculation:

[0132]

[0133] First result:

[0134]

[0135] Calculation of the second square root value:

[0136]

[0137] Substitution operation:

[0138]

[0139] The results show that the difference distribution characteristic value of the payment amount field at the refueling terminal node is 1.493, reflecting that the field exhibits a certain degree of consistent volatility in the current dataset. At the same time, the cumulative number of conflicts in the network also affects the quality of the node field. This value is used to measure the significance of the difference in field comparison and can be subsequently collected and summarized as one of the field consistency difference parameters.

[0140] like Figure 6 As shown in the figure, the steps of the permission frequency limit exceeding feature are as follows:

[0141] S511: Based on the field consistency difference, the number of operation requests for each role within the time interval is screened, the correlation between the operation frequency of each role and the role permissions is analyzed, and whether there is any discrepancy between the number of operation requests and the permission category is determined to obtain the permission operation distribution;

[0142] Extract the user ID and operation timestamp associated with the field content inconsistency event in each refueling transaction, determine the corresponding role category, and then establish a role-operation time mapping table in the database. Archive and count the operation requests according to the set time interval. For example, group all requests by role category on a daily basis and record the number of requests. For example, on April 1, 2025, the administrator role submitted 28 requests, the gas station attendant role submitted 89 requests, and the financial staff role submitted 45 requests. Read the operation permissions of each role and identify the maximum operation frequency allowed for each role. For example, set the station manager's daily operation limit to 40 times. , 100 times for gas station attendants and 60 times for tax control personnel. Compare the actual number of operations with the scope of authority. If the operation frequency of a role exceeds the boundary of the authority, it is determined to be a frequency abnormality. Record all operation behaviors of this type of role within the specified time, and further eliminate operation categories that are not within the authority scope of this role. If it is found that the gas station attendant performs parameter adjustment of the tax control equipment, the behavior is marked as a cross-authority operation, and the request number, time, and terminal number are all recorded in the authority abnormality behavior log table. Finally, combine the actual number of requests under each role category and compare it with its role authority setting. All mismatches are classified and summarized to output the permission operation distribution.

[0143] S512: Based on the distribution of permission operations, sort out the operation requests that exceed the permission range of each role, count the frequency of exceeding the limit operations for each role type, and analyze the distribution characteristics of exceeding the limit requests in each role type to obtain the permission frequency exceeding limit characteristics;

[0144] Filter out all the over-limit operation requests performed by various roles in different time periods from the permission abnormal behavior log table, organize the role category, operation type, and execution time corresponding to each record into a set of data sets, and perform statistical analysis with the role as the primary key. For example, the gas station attendant performed 15 abnormal operations in April 2025, of which 9 exceeded the upper limit of the operation frequency and 6 were cross-category operations. Count the different abnormal types separately and integrate them. Perform the same process on all roles to form a detailed table of the frequency of over-limit operations for each role. Then, the over-limit behavior is distributed and calculated in the role classification. For example, the administrator role has a total of 4 over-limit operations, accounting for 10% of its total operations in the whole month. 5% of the number of operations, while the station manager role recorded 12 exceeding limits, with a total of 60 operations and an exceeding limit ratio of 20%. Based on this, the role authority boundary control is marked as weak. In the statistics, if more than 25% of the members in a role category have an exceeding limit frequency greater than 10 times, then the role category is included in the high-frequency exceeding limit risk category. The exceeding limit operation types of various roles are compared horizontally. For example, it is found that the exceeding limit operations of tax control personnel are concentrated in the transaction query frequency, while the exceeding limit operations of gas station attendants are concentrated in the equipment configuration modification. A role-type correspondence map of exceeding limit behavior is formed, and the total amount, ratio and type composition of the exceeding limit operations of each role are summarized, and the permission frequency exceeding limit characteristics are output.

[0145] like Figure 7 As shown in the figure, the gas station tax control data collection system based on blockchain includes:

[0146] The timing consistency judgment module, based on blockchain technology, analyzes the timing of operations during refueling initiation, fuel nozzle activation, refueling completion, payment initiation, and tax control generation. It compares the operation sequence of different terminals under the same event, determines whether there is any misalignment in the recording sequence of each terminal, calculates the frequency of misaligned nodes, and obtains the node synchronization stability.

[0147] The response synchronization monitoring module screens the broadcast response time of each segmented data packet at each node based on the node synchronization stability, analyzes the response order of the nodes to the same data packet, compares the distribution of node broadcast response intervals, and counts the number of missing fields to obtain the broadcast timing integrity parameter;

[0148] The replica offset analysis module calculates the replicas of the same type of operations between distributed ledger nodes based on the broadcast timing integrity parameter. It analyzes the combination of device upload time and node number, screens the distribution characteristics of parameter combinations in historical data, determines whether the combination frequency deviates from the normal distribution, and obtains the replica synchronization offset characteristics.

[0149] The data consistency comparison module compares the field contents of the transaction details to be verified between on-chain nodes based on the replica synchronization offset characteristics, analyzes the character composition of similar data fields, determines whether the content of each comparison is consistent, and counts the number of inconsistent locations of each data field to obtain the field consistency difference;

[0150] The permission frequency control module filters the number of operation requests for each role within a time period based on the field consistency difference, analyzes the relationship between operation frequency and role permissions, determines whether the number of operations exceeds the permission range of the role category, and obtains the permission frequency limit exceeding feature.

[0151] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0152] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0153] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0154] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0155] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0159] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0160] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A gas station tax control data collection method based on blockchain, characterized in that: The method comprises: S1: Based on blockchain technology, we analyze the operation sequence of refueling services, compare the operation sequence of different terminals under the same event, determine whether the record sequence is misaligned, calculate the misalignment frequency, and obtain the node synchronization stability; S2: Based on the node synchronization stability, screen the response time points of each node receiving the same data packet, analyze the node response sequence, determine whether there are any anomalies, count the number of missing fields, and obtain the broadcast timing integrity parameter; S3: Based on the broadcast timing integrity parameter, calculate the replicas of the same type of operation between nodes, analyze the combination of device upload time and node number, screen the distribution characteristics in the historical data, determine the combination frequency deviation, and obtain the replica synchronization offset characteristics; S4: Based on the replica synchronization offset feature, compare the field contents of the transaction details between the nodes on the chain, analyze the character composition of the same data fields, determine the content consistency, count the number of inconsistent positions, and obtain the field consistency difference; S5: Based on the field consistency difference, the number of operation requests of each role is screened, the association between the operation frequency and the role authority is analyzed, and whether the number of operations exceeds the limit is determined to obtain the permission frequency exceeding limit feature.

2. The method for collecting tax control data for gas stations based on blockchain according to claim 1 is characterized in that: The node synchronization stability includes the timing consistency index, the terminal synchronization status, and the node collaboration identifier; the broadcast timing integrity parameters include the response sequence characteristics, the synchronization delay identifier, and the field integrity level; the replica synchronization offset characteristics include the synchronization distribution identifier, the device timing matching item, and the operation replica mapping type; the field consistency difference includes the difference distribution identifier, the field comparison status, and the content consistency level; the permission frequency limit exceeding characteristics include the exceeding role category, the frequency distribution item, and the permission warning signal.

3. The method for collecting tax control data for gas stations based on blockchain according to claim 1 is characterized in that: The steps of node synchronization stability are specifically as follows: S101: Based on blockchain technology, collect operational time series data from fuel dispenser control terminals, fuel nozzle sensors, payment terminals, and tax control terminals. Based on the event time series collected by each business terminal, detect the order of event records, and use this to filter out the order misalignment of records from each terminal to obtain the number of order misalignments. S102: Based on the number of sequence misalignments, and according to the operation sequence data corresponding to the nodes, compare the order of operations of each node and the time sequence changes, determine the time sequence position of each node under the same event for each group of operation sequence, and calculate the distribution of misaligned nodes to obtain the node misalignment distribution amount; S103: Calculate the ratio between the number of node misalignments and the total number of nodes under the same event based on the node misalignment distribution, determine the misalignment frequency of each node, and obtain the node synchronization stability.

4. The method for collecting tax control data for gas stations based on blockchain according to claim 1 is characterized in that: The steps of broadcasting the timing integrity parameter are specifically as follows: S201: screening the broadcast response time of each segmented data packet at each node based on the node synchronization stability, comparing the time sequence of each node receiving the same segmented data packet, determining the response order of each node, and obtaining a node response sequence; S202: calling the node response sequence, comparing the response time distribution of each node to the same data packet, analyzing the interval variation of the node response, determining whether the response sequence of each node in the same broadcast process has discontinuous or abnormal changes, and obtaining the interval fluctuation characteristics; S203: Based on the interval fluctuation characteristics, compare the field data of each node in each segmented data packet, count the number of missing fields, and obtain the broadcast timing integrity parameter according to the distribution of abnormal intervals and field missing situations.

5. The method for collecting tax control data for gas stations based on blockchain according to claim 1 is characterized in that: The steps of replica synchronization offset feature are specifically as follows: S301: Based on the broadcast timing integrity parameter, calculate the upload time of the same type of operation copies between the distributed ledger nodes, filter each group of device upload time and node number combinations, determine the occurrence of the combination between the nodes, and obtain the node combination distribution characteristics; S302: Compare the distribution changes of the node combination distribution characteristics among the nodes, determine whether the distribution of the parameter combination among the same type of operation replicas has abnormal changes, and obtain the replica synchronization offset characteristics by dividing the distribution interval and adjusting the abnormal interval.

6. The method for collecting tax control data for gas stations based on blockchain according to claim 5 is characterized in that: By dividing the distribution interval, the formula is adopted: Calculate the parameter combination distribution offset rate, adjust the abnormal interval, and obtain the replica synchronization offset characteristics, where θ j represents the distribution deviation rate of the jth node parameter combination, B j represents the number of copies of the same type of operation under the jth node, p oj Represents the parameter combination of the o-th copy of the same type of operation on the j-th node, represents the average parameter combination of all replicas of the same type of operation on the jth node, t oj represents the upload time of the oth copy of the same type of operation on the jth node, Represents the average upload time of all replicas of the same type of operation on the j-th node.

7. The method for collecting tax control data for gas stations based on blockchain according to claim 1 is characterized in that: The steps of measuring the field consistency difference are specifically as follows: S401: Based on the replica synchronization offset feature, compare the field contents of the transaction details to be verified between the nodes on the chain, determine whether the character composition of the same data fields is consistent, filter out the data fields with content differences, and obtain the node comparison position number; S402: Based on the number of node comparison positions, calculate their distribution in the transaction data to be verified, analyze the content structure of similar fields and the node comparison results, determine whether each comparison content is consistent, count the number of inconsistent field positions, and obtain the content distribution number; S403: Based on the content distribution quantity, count the inconsistencies in the transaction details fields between the nodes, compare the difference distribution of each type of field, and summarize the field comparison anomalies to obtain the field consistency difference amount.

8. The method for collecting tax control data for gas stations based on blockchain according to claim 7 is characterized in that: The statistical method for counting the inconsistencies in the transaction details fields between nodes and comparing the difference distribution of each type of field is as follows: Calculate the difference distribution characteristic value and summarize the field comparison anomalies to obtain the field consistency difference, where: Represents the differential distribution characteristic value of the i-th category field at the j-th node, Represents the field comparison value of the k-th data of the i-th category field at the j-th node, Represents the average value of the comparison of all data fields of the i-th category field at the j-th node, Represents the total number of data counted for the i-th field at the j-th node, Represents the number of field inconsistencies for the i-th field in all nodes.

9. The method for collecting tax control data for gas stations based on blockchain according to claim 1 is characterized in that: The steps of the permission frequency exceeding limit feature are specifically as follows: S511: Based on the field consistency difference, the number of operation requests for each role within the time interval is screened, the correlation between the operation frequency of each role and the role authority is analyzed, and whether there is any discrepancy between the number of operation requests and the authority category is determined to obtain the permission operation distribution; S512: Based on the distribution of permission operations, sort out the operation requests of each role that exceed the permission range, count the frequency of over-limit operations for each role type, and analyze the distribution characteristics of over-limit requests in each role type to obtain the permission frequency over-limit characteristics.

10. A gas station tax control data collection system based on blockchain, the system being used to implement the gas station tax control data collection method based on blockchain as described in any one of claims 1 to 9, characterized in that: The system comprises: The timing consistency judgment module, based on blockchain technology, analyzes the timing of operations during refueling initiation, fuel nozzle activation, refueling completion, payment initiation, and tax control generation. It compares the operation sequence of different terminals under the same event, determines whether there is any misalignment in the recording sequence of each terminal, calculates the frequency of misaligned nodes, and obtains the node synchronization stability. The response synchronization monitoring module screens the broadcast response time of each segmented data packet at each node based on the node synchronization stability, analyzes the response order of the nodes to the same data packet, compares the distribution of the node broadcast response intervals, and counts the field missing conditions to obtain the broadcast timing integrity parameter; The replica offset analysis module calculates the replicas of the same type of operations between distributed ledger nodes based on the broadcast timing integrity parameters, analyzes the combination of device upload time and node number, screens the distribution characteristics of parameter combinations in historical data, determines whether the combination frequency deviates from the normal distribution, and obtains the replica synchronization offset characteristics; The data consistency comparison module compares the field contents of the transaction details to be verified between the on-chain nodes based on the replica synchronization offset characteristics, analyzes the character composition of similar data fields, determines whether the content of each comparison is consistent, and counts the number of inconsistent locations of each data field to obtain the field consistency difference; The permission frequency control module screens the number of operation requests of each role within a time period based on the field consistency difference, analyzes the relationship between the operation frequency and the role permission, determines whether the number of operations exceeds the permission range of the role category, and obtains the permission frequency exceeding limit feature.

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