A blockchain-based petroleum resin production whole-process traceability system and method

Abstract

The application relates to the field of data processing, in particular to a blockchain-based oil resin production whole-process tracing system and method. The method comprises the following steps: acquiring any data sequence of any sub-process in the oil resin production process; acquiring the maximum value point of the data sequence, dividing the data sequence according to the minimum value point of the data sequence to obtain a sub-sequence; calculating the fluctuation effectiveness of each sub-sequence; calculating the overall fluctuation factor of the data sequence; sorting the sub-sequences in descending order according to the fluctuation effectiveness to obtain a sub-sequence sorting result, and determining a fluctuation significant interval; calculating the sensitive activation degree of each fluctuation significant interval; acquiring the local fluctuation factor of each fluctuation significant interval, and calculating the monitoring effectiveness of each moment in the data sequence; and determining the data of the moment in the data sequence as important data when the monitoring effectiveness is greater than a preset effectiveness threshold. The application can improve the tracing effect of the whole process of oil resin production.

Description

Technical Field

[0001] This invention relates to the field of data processing, and specifically to a blockchain-based traceability system and method for the entire production process of petroleum resin. Background Technology

[0002] The core challenge in tracing the origins of petroleum resin production lies in its complex raw materials, sensitive processes, and meticulous quality control. Typically, manufacturers choose to use blockchain to securely store process monitoring data. Alternatively, they may utilize blockchain's on-chain and off-chain capabilities to transmit or publish this data between companies. However, in chemical production, such as petroleum resin production, process monitoring data is extremely valuable to each company. Furthermore, due to the high cost of blockchain implementation, companies often choose to minimize data transmission or publication, resulting in incomplete data access for other companies.

[0003] When using traditional methods to store process monitoring data using blockchain, if all data collected at high frequency is uploaded to the blockchain during the data uploading process, the uploaded data will lack significance, and the data characteristics of the production process will be diluted by massive amounts of data. This will result in poor readability when analyzing the data, and thus lead to poor traceability of the entire petroleum resin production process. Summary of the Invention

[0004] This invention provides a blockchain-based traceability system and method for the entire petroleum resin production process to solve existing problems.

[0005] The present invention provides a blockchain-based method for tracing the entire production process of petroleum resin, employing the following technical solution:

[0006] One embodiment of the present invention provides a blockchain-based method for tracing the entire production process of petroleum resin, the method comprising the following steps:

[0007] Obtain any data sequence from any sub-process in the petroleum resin production process;

[0008] Find the maximum points of the data sequence, and divide the data sequence into subsequences based on the minimum points;

[0009] The volatility effectiveness of each subsequence is calculated based on the mean of the maximum points, the mean of the data in each subsequence, the number of maximum points, and the length of each group sequence.

[0010] Calculate the mean and standard deviation of volatility efficiency for each subsequence, and calculate the overall volatility factor of the data series based on the volatility efficiency of each subsequence and the mean and standard deviation of volatility efficiency for each subsequence.

[0011] The subsequences are sorted in descending order according to their volatility effectiveness to obtain the subsequence sorting results, and the significant volatility intervals are determined based on the subsequence sorting results.

[0012] The sensitivity activation of each significant fluctuation interval is calculated based on the fluctuation effectiveness of each subsequence in each significant fluctuation interval, the number of subsequences in each significant fluctuation interval, the time length of each significant fluctuation interval, and the time length of the data sequence.

[0013] Obtain the local volatility factor for each significant volatility interval, and calculate the monitoring effectiveness at each time point in the data sequence based on the overall volatility factor, the local volatility factor for each significant volatility interval, and the sensitivity activation.

[0014] Data in the data sequence at times when the monitoring validity exceeds a preset validity threshold is identified as important data and uploaded to the blockchain for storage.

[0015] Optionally, the volatility validity of each subsequence is calculated based on the mean of the maxima, the mean of the data in each subsequence, the number of maxima, and the length of each group sequence. Specifically, this includes:

[0016] The ratio of the mean of the maximum point to the mean of the data in the p-th subsequence is determined as the magnitude of the data change in the p-th subsequence.

[0017] The ratio of twice the number of maxima to the length of the p-th subsequence is determined as the data change frequency of the p-th subsequence;

[0018] The fluctuation validity of the p-th subsequence is determined by multiplying the magnitude of the data change of the p-th subsequence by the frequency of the data change of the p-th subsequence.

[0019] Obtain the volatility validity of each subsequence.

[0020] Optionally, the overall volatility factor of the data series is calculated based on the volatility efficiency of each subsequence, the mean volatility efficiency of the subsequence, and the standard deviation of volatility efficiency. Specifically, this includes:

[0021] The difference between the volatility efficiency of the p-th subsequence and the mean volatility efficiency of the subsequences is defined as the volatility deviation of the p-th subsequence.

[0022] The degree of volatility anomaly of the p-th subsequence is obtained by raising the fourth power to the ratio of the volatility deviation of the p-th subsequence to the standard deviation of the volatility effectiveness of the subsequence.

[0023] Obtain the degree of fluctuation anomaly for each subsequence;

[0024] The mean of the fluctuation anomalies of the subsequences is determined as the overall fluctuation factor of the data series.

[0025] Optionally, significant fluctuation intervals are determined based on the subsequence sorting results, specifically including:

[0026] The first subsequence in the sorted subsequence result is determined as the first subsequence;

[0027] Sort the subsequences in chronological order to obtain the original subsequences;

[0028] The position of the first subsequence within the original subsequence is determined as the initial interval;

[0029] Obtain the subsequence in the original subsequence that is adjacent to the initial interval and whose time precedes the first subsequence, and obtain the first subsequence on the left;

[0030] When the fluctuation validity of the first sequence on the left is less than that of the initial interval, the first sequence on the left and the initial interval are merged to obtain the first interval on the left. The subsequence that is adjacent to the Nth interval on the left and whose time is before the Nth interval on the left is obtained to obtain the (N+1)th sequence on the left. When the fluctuation validity of the first sequence on the left is greater than or equal to that of the initial interval, the initial interval is determined as the first interval on the left.

[0031] When the volatility validity of the (N+1)th sequence on the left is less than the volatility validity of the Nth sequence on the left in the Nth interval on the left, the (N+1)th sequence on the left and the Nth interval on the left are merged to obtain the (N+1)th interval on the left. When the volatility validity of the (N+1)th sequence on the left is greater than or equal to the volatility validity of the Nth sequence on the left in the Nth interval on the left, the merging stops and the Nth interval on the left is obtained, where N is a positive integer.

[0032] The first interval on the left, or the (N+1)th interval on the left, or the Nth interval on the left, is defined as the left interval;

[0033] Get the right-hand interval;

[0034] The left and right intervals are merged, and duplicate subsequences are removed to obtain the first significant fluctuation interval of the first subsequence.

[0035] Determine the Zth significant fluctuation interval, and define the interval from the first significant fluctuation interval to the Zth significant fluctuation interval as the significant fluctuation interval.

[0036] Optionally, the right-hand interval can be obtained, specifically including:

[0037] Obtain the subsequence in the original subsequence that is adjacent to the initial interval and whose time is after the first subsequence, and get the first subsequence on the right;

[0038] When the fluctuation validity of the first right sequence is less than that of the initial interval, the first right sequence and the initial interval are merged to obtain the first right interval. The subsequence that is adjacent to the Mth right interval and whose time is after the Mth right interval is obtained from the original subsequence to obtain the (M+1)th right sequence. When the fluctuation validity of the first right sequence is greater than or equal to that of the initial interval, the empty interval is determined as the first right interval.

[0039] When the volatility validity of the (M+1)th sequence on the right is less than the volatility validity of the Mth sequence on the right in the Mth interval on the right, the (M+1)th sequence on the right and the Mth interval on the right are merged to obtain the (M+1)th interval on the right. When the volatility validity of the (M+1)th sequence on the right is greater than or equal to the volatility validity of the Mth sequence on the right in the Mth interval on the right, the merging stops and the Mth interval on the right is obtained, where M is a positive integer.

[0040] The first interval on the right, or the (M+1)th interval on the right, or the Mth interval on the right, is defined as the right interval.

[0041] Optionally, the significant interval of the Z-th fluctuation is determined, specifically including:

[0042] From the original subsequence and the subsequence sorting results, remove the subsequence in the first significant fluctuation interval to obtain the first original subsequence and the first subsequence sorting results, respectively.

[0043] In the sorting result of the first subsequence, the first subsequence is determined as the second subsequence;

[0044] Determine the left and right intervals of the second subsequence from the first original subsequence;

[0045] The left and right intervals of the second subsequence are merged, and the duplicate subsequences are removed to obtain the second significant fluctuation interval of the second subsequence.

[0046] From the first original subsequence and the sorting result of the first subsequence, remove the subsequence in the second significant fluctuation interval to obtain the second original subsequence and the sorting result of the second subsequence, respectively.

[0047] In the sorting result of the second subsequence, the first subsequence is determined as the third subsequence;

[0048] Determine the third significant fluctuation interval of the third subsequence from the second original subsequence;

[0049] From the original subsequence A and the sorting results of subsequence A, remove the subsequence in the significant fluctuation interval of subsequence A+1, and obtain the original subsequence A+1 and the sorting results of subsequence A+1, respectively.

[0050] In the sorting result of the (A+1)th subsequence, the first subsequence is determined as the (A+2)th subsequence;

[0051] Determine the A+2 significant fluctuation interval of the A+2 subsequence from the sorting results of the A+1 subsequence;

[0052] From the sorting results of the (A+1)th original subsequence and the (A+2)th subsequence, remove the subsequence in the (A+2)th significant fluctuation interval to obtain the (A+2)th original subsequence and the (A+2)th subsequence sorting results. When both the (A+2)th original subsequence and the (A+2)th subsequence sorting results are empty sequences, stop the iteration and determine the (A+2)th significant fluctuation interval as the Zth significant fluctuation interval, where A is a positive integer greater than 1.

[0053] Optionally, the sensitivity activation of each significant fluctuation interval is calculated based on the fluctuation effectiveness of each subsequence within each significant fluctuation interval, the number of subsequences within each significant fluctuation interval, the time length of each significant fluctuation interval, and the time length of the data sequence. Specifically, this includes:

[0054] The difference between the volatility efficiency of the b-th subsequence and the volatility efficiency of the starting sequence within the a-th significant volatility interval is obtained to obtain the volatility efficiency difference, where the starting sequence is the subsequence with the largest volatility efficiency in the a-th significant volatility interval.

[0055] Obtain the volatility efficiency difference of each subsequence within the a-th significant volatility interval;

[0056] The mean of the fluctuation efficiency difference of the subsequence in the a-th fluctuation significant interval is determined as the perceptual sensitivity of the a-th fluctuation significant interval.

[0057] The ratio of the time length of the a-th significant fluctuation interval to the time length of the data sequence is determined as the data dilution degree of the time length of the a-th significant fluctuation interval.

[0058] The product of the perceptual sensitivity of the a-th significant fluctuation interval and the data dilution degree of the time length of the a-th significant fluctuation interval is determined as the sensitivity activation degree of the a-th significant fluctuation interval.

[0059] Obtain the sensitivity activation for each significant fluctuation interval.

[0060] Optionally, the local volatility factor for each significant volatility interval is obtained, specifically including:

[0061] Obtain the mean and standard deviation of volatility efficiency of the subsequence within the c-th significant volatility interval;

[0062] The difference between the volatility efficiency of the d-th subsequence in the c-th significant volatility interval and the mean volatility efficiency of the subsequences in the c-th significant volatility interval is defined as the volatility deviation of the d-th subsequence in the c-th significant volatility interval.

[0063] The fourth power is the ratio of the fluctuation deviation of the d-th subsequence in the c-th significant fluctuation interval to the standard deviation of the fluctuation effectiveness of the subsequence in the c-th significant fluctuation interval. This gives the degree of fluctuation anomaly of the d-th subsequence in the c-th significant fluctuation interval.

[0064] Obtain the degree of fluctuation anomaly of each subsequence within the c-th significant fluctuation interval;

[0065] The mean value of the fluctuation anomaly of the subsequence in the c-th significant fluctuation interval is determined as the local fluctuation factor of the c-th significant fluctuation interval.

[0066] Obtain the local volatility factor for each significant volatility interval.

[0067] Optionally, the monitoring effectiveness at each time point in the data sequence is calculated based on the overall volatility factor, the local volatility factor of each significant volatility interval, and the sensitivity activation, specifically including:

[0068] The significant fluctuation interval at time e in the data sequence is defined as the target significant fluctuation interval.

[0069] The significant fluctuation interval that is adjacent to the significant fluctuation interval of the target interval and is earlier than the significant fluctuation interval of the target interval is determined as the previous significant fluctuation interval of the target interval.

[0070] The significant fluctuation interval that is earlier than the target significant fluctuation interval and has the largest time difference with the target significant fluctuation interval is determined as the initial target significant fluctuation interval;

[0071] Obtain the mean of the local volatility factor within the significant volatility range;

[0072] The ratio of the mean local volatility factor to the overall volatility factor within a significant volatility range is defined as the monitored change.

[0073] The difference between the sensitivity activation degree of the target fluctuation significant interval and the sensitivity activation degree of the previous target fluctuation significant interval is determined as the sensitivity activation degree difference.

[0074] The ratio of the difference in sensitive activation to the sensitive activation in the significant fluctuation range of the initial target is determined as the abnormal climb rate.

[0075] The product of the monitored change and the abnormal rise is determined as the monitoring effectiveness at time e.

[0076] Obtain the monitoring effectiveness at each time point in the data sequence.

[0077] This invention proposes a blockchain-based traceability system for the entire petroleum resin production process, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the blockchain-based traceability method for the entire petroleum resin production process.

[0078] The beneficial effects of the technical solution of the present invention are:

[0079] In this embodiment of the invention, by evaluating the sensitivity of each information source to data changes based on the fluctuation effectiveness at different time scales for information sources belonging to different processes, the effective representative time periods in the production process can be accurately screened out, enabling more accurate extraction of key value data in the petroleum resin production process and improving the traceability effect of the entire petroleum resin production process. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 A flowchart illustrating a blockchain-based traceability method for the entire production process of petroleum resin, provided as an embodiment of the present invention.

[0082] Figure 2 This is a structural diagram of a blockchain-based traceability system for the entire production process of petroleum resin, provided as an embodiment of the present invention. Detailed Implementation

[0083] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a blockchain-based traceability method for the entire petroleum resin production process proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0084] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0085] The following description, in conjunction with the accompanying drawings, details a specific scheme for a blockchain-based traceability method for the entire petroleum resin production process provided by this invention.

[0086] This invention provides a blockchain-based traceability system and method for the entire petroleum resin production process. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a blockchain-based traceability method for the entire petroleum resin production process, according to an embodiment of the present invention. The method includes the following steps:

[0087] S101. Obtain any data sequence of any sub-process in the petroleum resin production process.

[0088] In this embodiment, the monitoring data of the entire process of petroleum resin production is analyzed. Petroleum resin is a thermoplastic resin produced by a series of processes such as polymerization and distillation from the C5 and C9 fractions generated by petroleum cracking. Therefore, in monitoring the entire production process, the monitoring of the production process is of utmost importance for ensuring product quality.

[0089] Therefore, high-frequency sensors are used to monitor reaction parameters during the production process, and the data of various attributes obtained by the high-frequency sensors are recorded. While the high acquisition frequency allows for the capture of detailed data on the petroleum resin production process, the extremely high acquisition frequency blurs the distinction between different production reaction states, resulting in insufficient differentiation between these states and consequently, different fluctuation characteristics are extracted at different stages of the reaction.

[0090] The production process of petroleum resin generally includes the following steps: raw material distillation or extraction - catalyst selection - polymerization reaction - catalyst removal - resin refining - additive formulation - molding. The actual production process of petroleum resin is divided into many sub-processes, and within each sub-process, information sources may appear that are only present in the current sub-process.

[0091] Each step in the process can be considered a sub-process within the petroleum resin production process. Within each sub-process, sensors collect various data sequences. For example, the distillation temperature during the raw material distillation separation process, and the pH value of the pickling solution during the raw material impurity removal process, only exhibit corresponding anomalies within the current distillation separation sub-process. Therefore, any sub-process is denoted as 's', and any data sequence within 's' is denoted as 'P' (such as temperature, humidity, pH, etc.). Any data sequence denoted as 'P' can also be referred to as any information source.

[0092] S102. Obtain the maximum points of the data sequence, and divide the data sequence into subsequences based on the minimum points of the data sequence.

[0093] In this embodiment, for the current information source P, the specific method for extracting its data sequence during the execution of the current subprocess s and obtaining the maximum value point of the data sequence can be as follows:

[0094] For the current information source P, the maximum point in the sequence is extracted by AMPD (multi-scale peak search), and the information source P is axially symmetric through the time axis. The maximum point of the axially symmetric sequence is also found by AMPD. The found maximum point is recorded as the minimum point of the original sequence.

[0095] Therefore, the sequence can be divided according to two adjacent minimum points, thus obtaining multiple subsequences p.

[0096] S103. Calculate the volatility effectiveness of each subsequence based on the mean of the maximum points, the mean of the data in each subsequence, the number of maximum points, and the length of each group sequence.

[0097] In this embodiment, the volatility effectiveness of each subsequence is calculated based on the mean of the maximum points, the mean of the data in each subsequence, the number of maximum points, and the length of each group sequence. Specifically, this includes:

[0098] The ratio of the mean of the maximum point to the mean of the data in the p-th subsequence is determined as the magnitude of the data change in the p-th subsequence.

[0099] The ratio of twice the number of maxima to the length of the p-th subsequence is determined as the data change frequency of the p-th subsequence;

[0100] The fluctuation validity of the p-th subsequence is determined by multiplying the magnitude of the data change of the p-th subsequence by the frequency of the data change of the p-th subsequence.

[0101] Obtain the volatility validity of each subsequence.

[0102] For example, the formula for calculating the volatility effectiveness of each subsequence, based on the mean of the maxima, the mean of the data in each subsequence, the number of maxima, and the length of each group sequence, can be:

[0103]

[0104] in, Indicates the first The volatility efficiency of individual subsequences This represents the mean of the maximum points in the data sequence. Indicates the first The mean of data in each subsequence, Indicates the first The magnitude of data change in each subsequence This indicates the number of local maxima in the data sequence. Indicates the first The length of each subsequence Indicates the first The frequency of data changes in each subsequence.

[0105] In the formula, From the perspective of numerical changes in the data source, data fluctuations are extracted to determine the magnitude of data changes occurring in the current production sub-process. Furthermore, Indicates the first The more extreme values ​​there are in a subsequence, the more significant the peak change position, indicating that the data fluctuation in the sequence p is more obvious in the current subprocess s.

[0106] According to the above formula, the fluctuation validity can be calculated for all subsequences p that are split from the data source P in the current subprocess s.

[0107] S104. Calculate the mean and standard deviation of volatility efficiency for each subsequence, and calculate the overall volatility factor of the data series based on the volatility efficiency of each subsequence, the mean and standard deviation of volatility efficiency for each subsequence.

[0108] In this embodiment, the overall volatility factor of the data series is calculated based on the volatility efficiency of each subsequence, the mean volatility efficiency of the subsequence, and the standard deviation of volatility efficiency. Specifically, this includes:

[0109] The difference between the volatility efficiency of the p-th subsequence and the mean volatility efficiency of the subsequences is defined as the volatility deviation of the p-th subsequence.

[0110] The degree of volatility anomaly of the p-th subsequence is obtained by raising the fourth power to the ratio of the volatility deviation of the p-th subsequence to the standard deviation of the volatility effectiveness of the subsequence.

[0111] Obtain the degree of fluctuation anomaly for each subsequence;

[0112] The mean of the fluctuation anomalies of the subsequences is determined as the overall fluctuation factor of the data series.

[0113] For example, the fluctuation effectiveness y calculated from the different subsequences p extracted from a single information source illustrates the changing characteristics of an influencing factor in the current production process. Based on this, it is also necessary to accurately determine whether the data source P of the current sub-process s is more sensitive to changes in the various influencing factors during production, i.e., the magnitude of the influence of the influencing factor represented by the current data source P on the sub-process.

[0114] First, it is necessary to calculate the overall fluctuation factor of the current information source P based on the different subsequences p that are split from a single information source. .

[0115] Therefore, based on the volatility efficiency of each subsequence, the mean volatility efficiency of the subsequence, and the standard deviation of volatility efficiency, the overall volatility factor of the data series can be calculated using the following formula:

[0116]

[0117] in, The overall volatility factor of the data series. This indicates the number of subsequences in the data sequence. Indicates the first The volatility efficiency of individual subsequences This represents the mean volatility efficiency of a subsequence in a data sequence P. It represents the standard deviation of the volatility efficiency of a subsequence in a data sequence P.

[0118] In the formula, This represents the fluctuation deviation of the p-th subsequence, used to express the deviation between the influencing factors represented by each subsequence in the production process and the overall influencing factors. It is expressed as the standard deviation of the fluctuation effectiveness of all subsequences. By measuring the significance of the differences and amplifying the numerical difference by raising it to the fourth power, we can obtain the degree of anomaly of different subsequences in the information source during the production steps.

[0119] S105. Sort the subsequences in descending order according to their fluctuation effectiveness to obtain the subsequence sorting results, and determine the significant fluctuation intervals based on the subsequence sorting results.

[0120] In this embodiment, determining the significant fluctuation interval based on the subsequence sorting results specifically includes:

[0121] The first subsequence in the sorted subsequence result is determined as the first subsequence;

[0122] Sort the subsequences in chronological order to obtain the original subsequences;

[0123] The position of the first subsequence within the original subsequence is determined as the initial interval;

[0124] Obtain the subsequence in the original subsequence that is adjacent to the initial interval and whose time precedes the first subsequence, and obtain the first subsequence on the left;

[0125] When the fluctuation validity of the first sequence on the left is less than that of the initial interval, the first sequence on the left and the initial interval are merged to obtain the first interval on the left. The subsequence that is adjacent to the Nth interval on the left and whose time is before the Nth interval on the left is obtained to obtain the (N+1)th sequence on the left. When the fluctuation validity of the first sequence on the left is greater than or equal to that of the initial interval, the initial interval is determined as the first interval on the left.

[0126] When the volatility validity of the (N+1)th sequence on the left is less than the volatility validity of the Nth sequence on the left in the Nth interval on the left, the (N+1)th sequence on the left and the Nth interval on the left are merged to obtain the (N+1)th interval on the left. When the volatility validity of the (N+1)th sequence on the left is greater than or equal to the volatility validity of the Nth sequence on the left in the Nth interval on the left, the merging stops and the Nth interval on the left is obtained, where N is a positive integer.

[0127] The first interval on the left, or the (N+1)th interval on the left, or the Nth interval on the left, is defined as the left interval;

[0128] Get the right-hand interval;

[0129] The left and right intervals are merged, and duplicate subsequences are removed to obtain the first significant fluctuation interval of the first subsequence.

[0130] Determine the Zth significant fluctuation interval, and define the interval from the first significant fluctuation interval to the Zth significant fluctuation interval as the significant fluctuation interval.

[0131] Obtain the right-hand interval, specifically including:

[0132] Obtain the subsequence in the original subsequence that is adjacent to the initial interval and whose time is after the first subsequence, and get the first subsequence on the right;

[0133] When the fluctuation validity of the first right sequence is less than that of the initial interval, the first right sequence and the initial interval are merged to obtain the first right interval. The subsequence that is adjacent to the Mth right interval and whose time is after the Mth right interval is obtained from the original subsequence to obtain the (M+1)th right sequence. When the fluctuation validity of the first right sequence is greater than or equal to that of the initial interval, the empty interval is determined as the first right interval.

[0134] When the volatility validity of the (M+1)th sequence on the right is less than the volatility validity of the Mth sequence on the right in the Mth interval on the right, the (M+1)th sequence on the right and the Mth interval on the right are merged to obtain the (M+1)th interval on the right. When the volatility validity of the (M+1)th sequence on the right is greater than or equal to the volatility validity of the Mth sequence on the right in the Mth interval on the right, the merging stops and the Mth interval on the right is obtained, where M is a positive integer.

[0135] The first interval on the right, or the (M+1)th interval on the right, or the Mth interval on the right, is defined as the right interval.

[0136] Determine the significant interval of the Z-th fluctuation, specifically including:

[0137] From the original subsequence and the subsequence sorting results, remove the subsequence in the first significant fluctuation interval to obtain the first original subsequence and the first subsequence sorting results, respectively.

[0138] In the sorting result of the first subsequence, the first subsequence is determined as the second subsequence;

[0139] Determine the left and right intervals of the second subsequence from the first original subsequence;

[0140] The left and right intervals of the second subsequence are merged, and the duplicate subsequences are removed to obtain the second significant fluctuation interval of the second subsequence.

[0141] From the first original subsequence and the sorting result of the first subsequence, remove the subsequence in the second significant fluctuation interval to obtain the second original subsequence and the sorting result of the second subsequence, respectively.

[0142] In the sorting result of the second subsequence, the first subsequence is determined as the third subsequence;

[0143] Determine the third significant fluctuation interval of the third subsequence from the second original subsequence;

[0144] From the original subsequence A and the sorting results of subsequence A, remove the subsequence in the significant fluctuation interval of subsequence A+1, and obtain the original subsequence A+1 and the sorting results of subsequence A+1, respectively.

[0145] In the sorting result of the (A+1)th subsequence, the first subsequence is determined as the (A+2)th subsequence;

[0146] Determine the A+2 significant fluctuation interval of the A+2 subsequence from the sorting results of the A+1 subsequence;

[0147] From the sorting results of the (A+1)th original subsequence and the (A+2)th subsequence, remove the subsequence in the (A+2)th significant fluctuation interval to obtain the (A+2)th original subsequence and the (A+2)th subsequence sorting results. When both the (A+2)th original subsequence and the (A+2)th subsequence sorting results are empty sequences, stop the iteration and determine the (A+2)th significant fluctuation interval as the Zth significant fluctuation interval, where A is a positive integer greater than 1.

[0148] For example, the purpose of this step is to intelligently identify the time intervals (referred to as "significant fluctuation intervals") from continuous sensor time-series data that exhibit the most significant fluctuations and best represent abnormal states in the process, thereby achieving selective data uploading to the blockchain. Through a mechanism of "priority sorting and dynamic attribution," it is ensured that the identification results cover the most important abnormal events while avoiding overlap between intervals.

[0149] Wherein, subsequence p is a short-term data segment obtained by dividing continuous time series data through extreme points. Fluctuation effectiveness. This is an indicator used to quantify the severity and significance of fluctuations in a subsequence. The higher the value, the more prominent the anomalous behavior of the subsequence. The significant fluctuation interval P' is a time period representing a complete anomalous event, consisting of one or more subsequences that are continuous in time and have high fluctuation validity.

[0150] In one specific embodiment, in the sub-process(s) of "raw material deimpurification", there is an information source (P) of "pickling solution pH value". The complete time series data of this sub-process was monitored and segmented into 10 consecutive subsequences (p1 to p10).

[0151] Fluctuation effectiveness of each subsequence ( The calculation has been completed, and the original subsequence is: p1(y=0.3)-p2(y=1.5)-p3(y=0.8)-p4(y=2.0)-p5(y=0.6)-p6(y=1.8)-p7(y=0.9)-p8(y=1.7)-p9(y=0.5)-p10(y=1.0).

[0152] First, sort by fluctuation effectiveness and determine the processing priority: sort all subsequences according to... Sort the values ​​in descending order to get the following subsequence sorting results: p4(y=2.0), p6(y=1.8), p8(y=1.7), p2(y=1.5), p10(y=1.0), p7(y=0.9), p3(y=0.8), p5(y=0.6), p9(y=0.5), p1(y=0.3).

[0153] The second step involves identifying the first subsequence p4 in the subsequence sorting results as the first subsequence, as it has the highest volatility validity, representing the most dramatic and abnormal pH fluctuations within this time period. The position of p4 within the original subsequence is determined as the initial interval. From the original subsequence, p4's left neighbor (the first left-hand sequence) p3 (y=0.8) on the time axis is examined. Since 0.8 < 2.0, it's a "downhill" trend, so p3 is included in the interval, resulting in the first left-hand interval. Next, p3's left neighbor (the second left-hand sequence) p2 (y=1.5) is examined. Since 1.5 > 0.8, it's an "uphill" trend, so the expansion to the left stops. Then, p4's right neighbor (the first right-hand sequence) p5 (y=0.6) is examined. Since 0.6 < 2.0, it's a "downhill" trend, so p5 is included in the interval, resulting in the first right-hand interval. Finally, p5's right neighbor p6 (y=1.8) is examined. Since 1.8 > 0.6, it's an "uphill" trend, so the expansion to the right stops.

[0154] Therefore, by merging the left and right intervals, the first significant fluctuation interval of the first subsequence is: [p3, p4, p5].

[0155] Since p3, p4, and p5 have been included in the first significant fluctuation interval, the subsequences in the first significant fluctuation interval are removed from the original subsequences and the subsequence sorting results to obtain the first original subsequences and the first subsequence sorting results, which are: p1(y=0.3)-p2(y=1.5)-p6(y=1.8)-p7(y=0.9)-p8(y=1.7)-p9(y=0.5)-p10(y=1.0) and p6(y=1.8), p8(y=1.7), p2(y=1.5), p10(y=1.0), p7(y=0.9), p9(y=0.5), p1(y=0.3).

[0156] In the sorting results of the first subsequence, the second subsequence is p6. Therefore, the significant fluctuation interval corresponding to p6 is obtained as follows:

[0157] The left neighbor of p6 is p5, but p5 already belongs to the first significant fluctuation interval, so we immediately stop expanding to the left; we check the right neighbor of p6, p7 (y=0.9). Since 0.9 < 1.8, it is a "downhill" interval, so we include p7 in the interval. Then we check the right neighbor of p7, p8 (y=1.7). Since 1.7 > 0.9, it is an "uphill" interval, so we stop expanding to the right. Therefore, the second significant fluctuation interval of the second subsequence is [p6, p7].

[0158] Since p6 and p7 are included in the second significant fluctuation interval, the subsequences in the second significant fluctuation interval are removed from the first original subsequence and the first subsequence sorting results to obtain the second original subsequence and the second subsequence sorting results, which are: p1(y=0.3)-p2(y=1.5)-p8(y=1.7)-p9(y=0.5)-p10(y=1.0) and p8(y=1.7), p2(y=1.5), p10(y=1.0), p9(y=0.5), p1(y=0.3).

[0159] In the sorting results of the second subsequence, the third subsequence is p8. Therefore, the significant fluctuation interval corresponding to p8 is obtained as follows:

[0160] The left neighbor of p8 is p7, which already belongs to the second significant fluctuation interval, so we stop expanding to the left. We then check the right neighbor of p8, p9 (y=0.5). Since 0.5 < 1.7, it's a "downhill" interval, so we include p9 in the interval. Next, we check the right neighbor of p9, p10 (y=1.0). Since 1.0 > 0.5, it's an "uphill" interval, so we stop expanding to the right. Therefore, the third significant fluctuation interval of the third subsequence is [p8, p9].

[0161] Since p8 and p9 are included in the third significant fluctuation interval, they are removed from the second original subsequence and the second subsequence sorting results. The resulting third original subsequence and third subsequence sorting results are: p1(y=0.3)-p2(y=1.5)-p10(y=1.0) and p2(y=1.5), p10(y=1.0), p1(y=0.3), respectively.

[0162] In the sorting results of the third subsequence, the fourth subsequence is p2. Therefore, the significant fluctuation interval corresponding to p2 is obtained as follows:

[0163] Check the left neighbor of p2, p1 (y=0.3). Since 0.3<1.5, it belongs to the "downhill" range, so p1 is included in the interval; the right neighbor of p2 is p3, which has already been assigned to the first significant fluctuation interval, so we stop expanding to the right. Therefore, the fourth significant fluctuation interval of the obtained fourth subsequence is [p1, p2].

[0164] Ultimately, only p10 remains as a single, significant fifth fluctuation interval.

[0165] Thus, the first to fifth significant fluctuation intervals are determined as the significant fluctuation intervals based on the subsequence sorting results.

[0166] S106. Calculate the sensitivity activation of each significant fluctuation interval based on the fluctuation effectiveness of each subsequence in each significant fluctuation interval, the number of subsequences in each significant fluctuation interval, the time length of each significant fluctuation interval, and the time length of the data sequence.

[0167] In this embodiment, the sensitivity activation of each significant fluctuation interval is calculated based on the fluctuation effectiveness of each subsequence in each significant fluctuation interval, the number of subsequences in each significant fluctuation interval, the time length of each significant fluctuation interval, and the time length of the data sequence. Specifically, this includes:

[0168] The difference between the volatility efficiency of the b-th subsequence and the volatility efficiency of the starting sequence within the a-th significant volatility interval is obtained to obtain the volatility efficiency difference, where the starting sequence is the subsequence with the largest volatility efficiency in the a-th significant volatility interval.

[0169] Obtain the volatility efficiency difference of each subsequence within the a-th significant volatility interval;

[0170] The mean of the fluctuation efficiency difference of the subsequence in the a-th fluctuation significant interval is determined as the perceptual sensitivity of the a-th fluctuation significant interval.

[0171] The ratio of the time length of the a-th significant fluctuation interval to the time length of the data sequence is determined as the data dilution degree of the time length of the a-th significant fluctuation interval.

[0172] The product of the perceptual sensitivity of the a-th significant fluctuation interval and the data dilution degree of the time length of the a-th significant fluctuation interval is determined as the sensitivity activation degree of the a-th significant fluctuation interval.

[0173] Obtain the sensitivity activation for each significant fluctuation interval.

[0174] For example, the sensitivity activation of each significant fluctuation interval is calculated based on the fluctuation effectiveness of each subsequence in each significant fluctuation interval, the number of subsequences in each significant fluctuation interval, the time length of each significant fluctuation interval, and the time length of the data sequence. The calculation formula can be as follows:

[0175]

[0176] in, Indicates the significant fluctuation range Sensitive activation level, Indicates the significant fluctuation range The maximum value of the wave efficiency of neutron sequences. Indicates the significant fluctuation range The Middle The volatility efficiency of individual subsequences Indicates the significant fluctuation range The number of neutron sequences, Indicates the significant fluctuation range The length of time, Indicates the time length of the data sequence.

[0177] In the formula, It shows a set of significant changes in a single data source within a currently extracted subprocess s, representing the sensitivity of the fluctuation factors in the current subprocess to changes in the monitored parameters. This indicates whether the sensitivity level will be diluted by a longer interval length, that is, the degree of data dilution.

[0178] Therefore, the sensitivity of activation can be obtained for each significant fluctuation range.

[0179] S107. Obtain the local fluctuation factor for each significant fluctuation interval, and calculate the monitoring effectiveness at each moment in the data sequence based on the overall fluctuation factor, the local fluctuation factor for each significant fluctuation interval, and the sensitivity activation.

[0180] Obtain the local volatility factor for each significant volatility interval, specifically including:

[0181] Obtain the mean and standard deviation of volatility efficiency of the subsequence within the c-th significant volatility interval;

[0182] The difference between the volatility efficiency of the d-th subsequence in the c-th significant volatility interval and the mean volatility efficiency of the subsequences in the c-th significant volatility interval is defined as the volatility deviation of the d-th subsequence in the c-th significant volatility interval.

[0183] The fourth power is the ratio of the fluctuation deviation of the d-th subsequence in the c-th significant fluctuation interval to the standard deviation of the fluctuation effectiveness of the subsequence in the c-th significant fluctuation interval. This gives the degree of fluctuation anomaly of the d-th subsequence in the c-th significant fluctuation interval.

[0184] Obtain the degree of fluctuation anomaly of each subsequence within the c-th significant fluctuation interval;

[0185] The mean value of the fluctuation anomaly of the subsequence in the c-th significant fluctuation interval is determined as the local fluctuation factor of the c-th significant fluctuation interval.

[0186] Obtain the local volatility factor for each significant volatility interval.

[0187] Based on the overall volatility factor, the local volatility factor of each significant volatility interval, and the sensitivity activation, the monitoring effectiveness at each time point in the data sequence is calculated, specifically including:

[0188] The significant fluctuation interval at time e in the data sequence is defined as the target significant fluctuation interval.

[0189] The significant fluctuation interval that is adjacent to the significant fluctuation interval of the target interval and is earlier than the significant fluctuation interval of the target interval is determined as the previous significant fluctuation interval of the target interval.

[0190] The significant fluctuation interval that is earlier than the target significant fluctuation interval and has the largest time difference with the target significant fluctuation interval is determined as the initial target significant fluctuation interval;

[0191] Obtain the mean of the local volatility factor within the significant volatility range;

[0192] The ratio of the mean local volatility factor to the overall volatility factor within a significant volatility range is defined as the monitored change.

[0193] The difference between the sensitivity activation degree of the target fluctuation significant interval and the sensitivity activation degree of the previous target fluctuation significant interval is determined as the sensitivity activation degree difference.

[0194] The ratio of the difference in sensitive activation to the sensitive activation in the significant fluctuation range of the initial target is determined as the abnormal climb rate.

[0195] The product of the monitored change and the abnormal rise is determined as the monitoring effectiveness at time e.

[0196] Obtain the monitoring effectiveness at each time point in the data sequence.

[0197] For example, blockchain needs to record information in the production process to track and trace the production process. However, the production environment is constantly changing. For instance, the impurity content of each batch is different during the distillation process, and the heat accumulation caused by the exothermic polymerization during the reaction process leads to constant changes in the collected data.

[0198] Therefore, different impact scenarios need to be represented by fluctuation characteristics at different time scales to accurately reflect the degree of impact of the current anomaly on the current process. For example, a fixed-size anomaly feature window cannot fully reflect the specific fluctuations within a long range of significant information fluctuations. Therefore, it is necessary to accurately determine the collaborative changes between data sources to extract specific time periods from the current sub-process s, thereby accurately assessing the specific data change ranges within sub-process s that represent its execution process. This allows for the evaluation of the process quality represented by these specific data change ranges, ensuring the security of the original monitoring data while reducing the risk of biased judgments about process execution effects due to massive uploads of fine-grained data, thus minimizing the effectiveness of process traceability.

[0199] Therefore, for each significant fluctuation interval, the local volatility factor can be calculated using the formula for calculating the overall volatility factor of the reference data sequence. Each significant fluctuation interval is considered as a data sequence, and the subsequences within each significant fluctuation interval are considered as data sequences within the data sequence, thus calculating the local volatility factor for each significant fluctuation interval.

[0200] Based on the overall volatility factor, the local volatility factor of each significant volatility interval, and the sensitivity activation, the monitoring effectiveness at each time point in the data sequence is calculated. The calculation formula can be:

[0201]

[0202] in, This indicates the monitoring effectiveness of a single data source in subroutine s at time t. This represents the sensitivity activation level within the significant range of the target fluctuation at time t. This represents the sensitivity activation of the significant range of the target fluctuation at time t. This indicates the sensitivity of activation within the range of significant fluctuations in the initial target. This represents the mean of the local volatility factor within a significant volatility range. This represents the overall volatility factor.

[0203] In the formula, The monitored change in the current data source reflects whether the fluctuation of the current data source is relatively significant. That is, during the execution of the current process s, the fluctuation of a single data source is more conducive to reflecting the change in the monitored quantity at the current moment in the current environment. The abnormal rise rate reflects the actual situation of the abnormal rise of the current t in the significant fluctuation range relative to the first occurrence of the significant fluctuation range.

[0204] Therefore, the monitoring effectiveness of a single data source at each time step within the current subroutine can be calculated. The same calculation method can be used to calculate the monitoring effectiveness of each data source at each time step for the other data sources within the subroutine. For the entire subroutine, the calculation method for the monitoring effectiveness of all data sources at each time step can be as follows:

[0205]

[0206] in, This represents the monitoring effectiveness of all data sources in subroutine s at time t. This indicates the number of data sources in the subroutine.

[0207] S108. Data in the data sequence at times when the monitoring validity exceeds the preset validity threshold is identified as important data and uploaded to the blockchain for storage.

[0208] In this embodiment, the monitoring effectiveness can first be normalized and compared with a preset effectiveness threshold to determine whether each moment's data is important. Next, adjacent marked important data can be merged to obtain the final important data, which is then marked and uploaded to the blockchain.

[0209] Optionally, the preset validity threshold can be set according to actual needs, and no specific numerical limit is imposed here. In a preferred embodiment, it can be 0.6.

[0210] Blockchain technology itself achieves tamper-proof and distributed storage functions through decentralization. Through a chain architecture, it stores data from various parties in the supply chain, such as suppliers, processors, regulatory agencies, and purchasers, on the chain. Based on the immutable nature of blockchain, it ensures that the data stored by multiple parties will not be modified by third parties, thus preventing errors in the storage results.

[0211] The management system verifies the user's account permissions and the compliance of the published information. The verified information is hashed using the SHA-256 algorithm to generate corresponding hash values ​​for each field in the metadata. The system then records the marked blocks, their corresponding hash values, and the hash values ​​of the parent blocks of the current block, thus completing the process of putting the corresponding important data on the blockchain.

[0212] In summary, in this embodiment of the invention, by evaluating the sensitivity of each information source to data changes based on the fluctuation effectiveness at different time scales for information sources belonging to different processes, the effective representative time periods in the production process can be accurately selected, enabling more accurate extraction of key value data in the petroleum resin production process and improving the traceability effect of the entire petroleum resin production process.

[0213] This invention also proposes a blockchain-based traceability system for the entire petroleum resin production process. Please refer to [link / reference]. Figure 2 The diagram shows a structural diagram of a blockchain-based traceability system for the entire production process of petroleum resin, provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a data processing module 102, and a data uploading module 103.

[0214] The data acquisition module 101 is used to acquire any data sequence of any sub-process in the petroleum resin production process;

[0215] The data processing module 102 is used to acquire the maximum points of the data sequence and divide the data sequence into subsequences based on the minimum points; calculate the volatility effectiveness of each subsequence based on the mean of the maximum points, the mean of the data in each subsequence, the number of maximum points, and the length of each group sequence; calculate the mean and standard deviation of the volatility effectiveness of the subsequences, and calculate the overall volatility factor of the data sequence based on the volatility effectiveness of each subsequence, the mean and standard deviation of the volatility effectiveness; sort the subsequences in descending order according to volatility effectiveness to obtain the subsequence sorting result, and determine the significant volatility intervals based on the subsequence sorting result; calculate the sensitivity activation of each significant volatility interval based on the volatility effectiveness of each subsequence in each significant volatility interval, the number of subsequences in each significant volatility interval, the time length of each significant volatility interval, and the time length of the data sequence; acquire the local volatility factor of each significant volatility interval, and calculate the monitoring effectiveness at each time point in the data sequence based on the overall volatility factor, the local volatility factor of each significant volatility interval, and the sensitivity activation.

[0216] The data upload module 103 is used to identify data in the data sequence at times when the monitoring validity is greater than a preset validity threshold as important data, and upload the important data to the blockchain for storage.

[0217] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the blockchain-based petroleum resin production full-process traceability system and the blockchain-based petroleum resin production full-process traceability method embodiment provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0218] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0219] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0220] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A blockchain-based method for tracing the entire production process of petroleum resin, characterized in that, include: Acquire any data sequence of any subprocess in the petroleum resin production process, wherein the data sequence is a monitoring data sequence that reflects the state of the petroleum resin production process, collected by a high-frequency sensor, and the monitoring data sequence includes at least one of temperature, pH value or humidity. Find the maximum points of the data sequence, and divide the data sequence into subsequences based on the minimum points; The volatility effectiveness of each subsequence is calculated based on the mean of the maximum points, the mean of the data in each subsequence, the number of maximum points, and the length of each group sequence. Calculate the mean and standard deviation of volatility efficiency for each subsequence, and calculate the overall volatility factor of the data series based on the volatility efficiency of each subsequence and the mean and standard deviation of volatility efficiency for each subsequence. The subsequences are sorted in descending order according to their volatility effectiveness to obtain the subsequence sorting results; The significant fluctuation intervals are determined based on the subsequence sorting results, specifically including: The first subsequence in the sorted subsequence result is determined as the first subsequence; Sort the subsequences in chronological order to obtain the original subsequences; The position of the first subsequence within the original subsequence is determined as the initial interval; Obtain the subsequence in the original subsequence that is adjacent to the initial interval and whose time precedes the first subsequence, and obtain the first subsequence on the left; When the fluctuation validity of the first sequence on the left is less than that of the initial interval, the first sequence on the left and the initial interval are merged to obtain the first interval on the left. The subsequence that is adjacent to the Nth interval on the left and whose time is before the Nth interval on the left is obtained to obtain the (N+1)th sequence on the left. When the fluctuation validity of the first sequence on the left is greater than or equal to that of the initial interval, the initial interval is determined as the first interval on the left. When the volatility validity of the (N+1)th sequence on the left is less than the volatility validity of the Nth sequence on the left in the Nth interval on the left, the (N+1)th sequence on the left and the Nth interval on the left are merged to obtain the (N+1)th interval on the left. When the volatility validity of the (N+1)th sequence on the left is greater than or equal to the volatility validity of the Nth sequence on the left in the Nth interval on the left, the merging stops and the Nth interval on the left is obtained, where N is a positive integer. The first interval on the left, or the (N+1)th interval on the left, or the Nth interval on the left, is defined as the left interval; Get the right-hand interval; The left and right intervals are merged, and duplicate subsequences are removed to obtain the first significant fluctuation interval of the first subsequence. Determine the Zth significant fluctuation interval, and define the interval from the first significant fluctuation interval to the Zth significant fluctuation interval as the significant fluctuation interval; Based on the volatility validity of each subsequence within each significant volatility interval, the number of subsequences within each significant volatility interval, the time length of each significant volatility interval, and the time length of the data sequence, the sensitivity activation of each significant volatility interval is calculated, specifically including: The difference between the volatility efficiency of the b-th subsequence and the volatility efficiency of the starting sequence within the a-th significant volatility interval is obtained to obtain the volatility efficiency difference, where the starting sequence is the subsequence with the largest volatility efficiency in the a-th significant volatility interval. Obtain the volatility efficiency difference of each subsequence within the a-th significant volatility interval; The mean of the fluctuation efficiency difference of the subsequence in the a-th fluctuation significant interval is determined as the perceptual sensitivity of the a-th fluctuation significant interval. The ratio of the time length of the a-th significant fluctuation interval to the time length of the data sequence is determined as the data dilution degree of the time length of the a-th significant fluctuation interval. The product of the perceptual sensitivity of the a-th significant fluctuation interval and the data dilution degree of the time length of the a-th significant fluctuation interval is determined as the sensitivity activation degree of the a-th significant fluctuation interval. Obtain the sensitivity of activation for each significant fluctuation interval; Obtain the local volatility factor for each significant volatility interval, specifically including: Obtain the mean and standard deviation of volatility efficiency of the subsequence within the c-th significant volatility interval; The difference between the volatility efficiency of the d-th subsequence in the c-th significant volatility interval and the mean volatility efficiency of the subsequences in the c-th significant volatility interval is defined as the volatility deviation of the d-th subsequence in the c-th significant volatility interval. The fourth power is the ratio of the fluctuation deviation of the d-th subsequence in the c-th significant fluctuation interval to the standard deviation of the fluctuation effectiveness of the subsequence in the c-th significant fluctuation interval. This gives the degree of fluctuation abnormality of the d-th subsequence in the c-th significant fluctuation interval. Obtain the degree of fluctuation anomaly of each subsequence within the c-th significant fluctuation interval; The mean value of the fluctuation anomaly of the subsequence in the c-th significant fluctuation interval is determined as the local fluctuation factor of the c-th significant fluctuation interval. Obtain the local volatility factor for each significant volatility interval; The monitoring effectiveness at each moment in the data sequence is calculated based on the overall volatility factor, the local volatility factor of each significant volatility interval, and the sensitivity activation. Data in the data sequence at times when the monitoring validity exceeds the preset validity threshold is identified as important data and uploaded to the blockchain for storage. This important data is used for blockchain traceability of the entire petroleum resin production process.

2. The method for tracing the entire production process of petroleum resin based on blockchain according to claim 1, characterized in that, The calculation of the volatility effectiveness of each subsequence based on the mean of the maximum points, the mean of the data in each subsequence, the number of maximum points, and the length of each group sequence specifically includes: The ratio of the mean of the maximum point to the mean of the data in the p-th subsequence is determined as the magnitude of the data change in the p-th subsequence. The ratio of twice the number of maxima to the length of the p-th subsequence is determined as the data change frequency of the p-th subsequence; The fluctuation validity of the p-th subsequence is determined by multiplying the magnitude of the data change of the p-th subsequence by the frequency of the data change of the p-th subsequence. Obtain the volatility validity of each subsequence.

3. The method for tracing the entire production process of petroleum resin based on blockchain according to claim 1, characterized in that, The calculation of the overall volatility factor of the data series based on the volatility efficiency of each subsequence, the mean volatility efficiency of the subsequence, and the standard deviation of volatility efficiency specifically includes: The difference between the volatility efficiency of the p-th subsequence and the mean volatility efficiency of the subsequences is defined as the volatility deviation of the p-th subsequence. The degree of volatility anomaly of the p-th subsequence is obtained by raising the fourth power to the ratio of the volatility deviation of the p-th subsequence to the standard deviation of the volatility effectiveness of the subsequence. Obtain the degree of fluctuation anomaly for each subsequence; The mean of the fluctuation anomalies of the subsequences is determined as the overall fluctuation factor of the data series.

4. The method for tracing the entire production process of petroleum resin based on blockchain according to claim 1, characterized in that, The acquisition of the right-hand interval specifically includes: Obtain the subsequence in the original subsequence that is adjacent to the initial interval and whose time is after the first subsequence, and get the first subsequence on the right; When the fluctuation validity of the first right sequence is less than that of the initial interval, the first right sequence and the initial interval are merged to obtain the first right interval. The subsequence that is adjacent to the Mth right interval and whose time is after the Mth right interval is obtained from the original subsequence to obtain the (M+1)th right sequence. When the fluctuation validity of the first right sequence is greater than or equal to that of the initial interval, the empty interval is determined as the first right interval. When the volatility validity of the (M+1)th sequence on the right is less than the volatility validity of the Mth sequence on the right in the Mth interval on the right, the (M+1)th sequence on the right and the Mth interval on the right are merged to obtain the (M+1)th interval on the right. When the volatility validity of the (M+1)th sequence on the right is greater than or equal to the volatility validity of the Mth sequence on the right in the Mth interval on the right, the merging stops and the Mth interval on the right is obtained, where M is a positive integer. The first interval on the right, or the (M+1)th interval on the right, or the Mth interval on the right, is defined as the right interval.

5. The method for tracing the entire production process of petroleum resin based on blockchain according to claim 1, characterized in that, The determination of the significant interval of the Zth fluctuation specifically includes: From the original subsequence and the sorted subsequence results, remove the subsequence in the first significant fluctuation interval to obtain the first original subsequence and the sorted first subsequence results, respectively. In the sorting result of the first subsequence, the first subsequence is determined as the second subsequence; Determine the left and right intervals of the second subsequence from the first original subsequence; The left and right intervals of the second subsequence are merged, and the repeated subsequences are removed to obtain the second significant fluctuation interval of the second subsequence. From the first original subsequence and the sorting result of the first subsequence, remove the subsequence in the second significant fluctuation interval to obtain the second original subsequence and the sorting result of the second subsequence, respectively. In the sorting result of the second subsequence, the first subsequence is determined as the third subsequence; Determine the third significant fluctuation interval of the third subsequence from the second original subsequence; From the original subsequence A and the sorting results of subsequence A, remove the subsequence in the (A+1)th significant fluctuation interval to obtain the original subsequence A+1 and the sorting results of subsequence A+1, respectively. In the sorting result of the (A+1)th subsequence, the first subsequence is determined as the (A+2)th subsequence; Determine the A+2 significant fluctuation interval of the A+2 subsequence from the sorting results of the A+1 subsequence; From the sorting results of the (A+1)th original subsequence and the (A+2)th subsequence, remove the subsequence in the (A+2)th significant fluctuation interval to obtain the (A+2)th original subsequence and the (A+2)th subsequence sorting results. When both the (A+2)th original subsequence and the (A+2)th subsequence sorting results are empty sequences, stop the iteration and determine the (A+2)th significant fluctuation interval as the Zth significant fluctuation interval, where A is a positive integer greater than 1.

6. The method for tracing the entire production process of petroleum resin based on blockchain according to claim 1, characterized in that, The calculation of the monitoring effectiveness at each moment in the data sequence based on the overall volatility factor, the local volatility factor of each significant volatility interval, and the sensitivity activation degree specifically includes: The significant fluctuation interval at time e in the data sequence is defined as the target significant fluctuation interval. The significant fluctuation interval that is adjacent to the target significant fluctuation interval and is earlier than the target significant fluctuation interval is determined as the previous target significant fluctuation interval. The significant fluctuation interval that is earlier than the target significant fluctuation interval and has the largest time difference with the target significant fluctuation interval is determined as the initial target significant fluctuation interval; Obtain the mean of the local volatility factor within the significant volatility range; The ratio of the mean local volatility factor to the overall volatility factor within a significant volatility range is defined as the monitored change. The difference between the sensitivity activation degree of the target fluctuation significant interval and the sensitivity activation degree of the previous target fluctuation significant interval is determined as the sensitivity activation degree difference. The ratio of the difference in sensitive activation to the sensitive activation in the significant fluctuation range of the initial target is determined as the abnormal climb rate. The product of the monitored change and the abnormal rise is determined as the monitoring effectiveness at time e. Obtain the monitoring effectiveness at each moment in the data sequence.

7. A blockchain-based traceability system for the entire petroleum resin production process, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the blockchain-based petroleum resin production process traceability method as described in any one of claims 1-6.

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