Mother and baby product production traceability management method and system based on block chain

Abstract

The invention relates to the technical field of supply chain traceability, in particular to a mother and baby product production traceability management method and system based on a block chain, and the method comprises the following steps: obtaining raw material and packaging chain segment data, extracting batch information and detection numbers, recognizing nodes and recording cross chains, counting the cross frequency of finished product segments, and constructing a path index map. And screening an optimal main path, generating a main path identifier, judging a filling data density grade, marking a chain segment needing to be adjusted, and generating a scheduling strategy parameter table. According to the method, field splicing is performed on the batch number, the raw material batch number and the detection report number to compare the segment identifier, so that node connection identification and cross structure establishment are realized, chain segment relevance is enhanced, cross node frequency is counted, high-frequency node pairs are extracted to construct the path, and the path identification accuracy is improved; and screening a path with the fewest skipping times and complete node coverage as a main path, enhancing the traceability stability, sensing the frequency based on the time interval and the chain block number, and realizing the dynamic scheduling of the chain segment state.

Description

Technical Field

[0001] The present invention relates to the field of supply chain traceability technology, and in particular to a blockchain-based production traceability management method and system for maternal and infant products. Background Art

[0002] The field of supply chain traceability technology encompasses the recording, management, and tracking of information on goods throughout their production, processing, packaging, transportation, warehousing, and sales. Its core objective is to achieve traceability throughout the entire product lifecycle through information technology, ensuring that information at every stage, from source to destination, is authentic, complete, and unalterable. This technical field typically combines data collection, information management, identity identification, data storage, and tracking and analysis to achieve transparency and controllable accountability for product origins. In recent years, with the application of distributed ledgers, the Internet of Things, and radio frequency identification, supply chain traceability technology has gradually developed toward intelligence, automation, and information sharing, gaining widespread application in industries with high safety requirements, such as food, pharmaceuticals, and maternal and infant products.

[0003] Among them, the blockchain-based traceability management method for maternal and infant product production refers to the use of distributed ledger technology to encrypt and record data and synchronize nodes throughout the entire process of maternal and infant product production, from raw material procurement, production and processing, quality inspection, packaging and delivery to circulation and distribution. The patent subject mainly covers the encrypted storage of information in the production process, block data generation rules, on-chain data structure design, the unique identification generation method for each production batch information, the setting of data on-chain sequence and index rules, and the link binding method for raw material sources and batch data. The methods used include using hash functions to generate information summaries, recording data generation sequence through timestamps, setting up a consensus mechanism to confirm data validity, using asymmetric encryption to achieve transmission security between data nodes, and orderly connecting data at different stages through a chain structure.

[0004] Existing technologies primarily focus on encrypted data recording and structured storage based on blockchain. While these technologies ensure information authenticity and immutability, they lack fine-grained cross-checking and data exchange mechanisms for the connections between nodes in multiple segments. This makes it difficult to dynamically visualize the actual connectivity between nodes, resulting in rigid path structures and a lack of identification of node frequency differences. Regarding path construction, existing solutions fail to consider dynamic characteristics such as node repetitiveness and jump frequency, making it difficult to balance jump efficiency and information coverage in the generated paths, which can lead to unstable primary path selection. In blockchain resource management, traditional technologies often utilize fixed data volumes or time windows for scheduling, ignoring variations in data density across segments. This can lead to inefficient identification and optimization of low-frequency segments. For example, if a segment of filling control data experiences a sudden frequency change but falls below a preset sampling threshold, the existing system may be unable to detect this anomaly in a timely manner, impacting traceability integrity and production management responsiveness. Furthermore, existing technologies fail to effectively specialize the order and content combination between nodes when establishing on-chain indexes, resulting in insufficient representation of the on-chain structure and limiting the intelligent adjustment capabilities of the overall traceability structure and the implementation of dynamic path adjustment strategies. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology and propose a blockchain-based production traceability management method and system for maternal and infant products.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a blockchain-based method for traceability management of maternal and infant product production, comprising the following steps:

[0007] S1: Obtain segment data for the raw material pretreatment and packaging of maternal and infant products, extract the batch number, raw material batch number, and test result number, concatenate the fields to form a combined sequence, compare it with the segment identifier, determine the node connection status, write the qualified nodes into the cross identifier and record them in the double chain structure, and generate a batch cross segment identification table;

[0008] S2: Extract the cross-identification nodes in the finished product inspection segment according to the batch cross-segment identification table, count the repetition frequency of the nodes, select the node pairs with the highest frequency, splice the paths and record them in the on-chain structure index table to generate a batch path jump structure map;

[0009] S3: Count the number of path jumps according to the batch path jump structure map, compare it with the jump limit, select the path with the least jumps and complete node coverage, set it as the main path, update the traceability main path table, and generate a traceability access main path structure identifier set;

[0010] S4: Based on the traceability access main path structure identifier set, collect parameters such as raw material feeding amount, temperature control fluctuation, instruction call and state change of the maternal and infant product filling control section, count the frequency and compare it with the density level standard, determine the data density segment, and generate a chain block density level mapping set.

[0011] As a further solution of the present invention, the batch cross-segment identification table includes a combination sequence, a segment identifier, a cross identifier, and a double-chain structure; the batch path jump structure map includes node pairs, paths, jump times, and an on-chain structure index table; the traceability access main path structure identification set includes the main path, node coverage, jump limit comparison results, and a traceability main path table; the chain block density level mapping set includes raw material feed amount, temperature control fluctuation, instruction call, and status change.

[0012] As a further solution of the present invention, the specific steps of S1 are:

[0013] S101: Obtain segment node data in the raw material pretreatment segment and packaging segment of maternal and infant product products, call the batch number, raw material batch number, and test result number recorded in each node, concatenate related fields in node order to construct a continuous sequence, and generate a field concatenation sequence value;

[0014] S102: Based on the field concatenation sequence value, call the segment identifier of the corresponding segment, compare the consistency of the field sequence and the character position in the identifier, select the node group that meets the matching conditions, and generate a character consistency comparison rate value;

[0015] S103: Based on the character consistency comparison rate value, determine whether the node group meets the set conditions, mark the node group that meets the conditions as a cross identifier, and record the field sequence into the double chain structure to generate a batch cross chain segment identification table.

[0016] As a further solution of the present invention, the specific steps of S2 are:

[0017] S201: Based on the detection segment data in the batch cross-segment identification table, extract the cross identification node field, organize the node format, remove abnormal items, sort by number and then count the node frequency to generate a node frequency distribution value;

[0018] S202: Calling the node frequency distribution value, screening the node pairs with the highest frequency and adjacent to each other, calculating the comprehensive score of the node pairs, extracting the corresponding index positions, completing path splicing and deduplication processing, and obtaining the high-frequency node combined path value;

[0019] S203: Construct a jump graph structure according to the high-frequency node combination path value, set the connection order and sequence number, establish a structure index table, and generate a batch path jump structure graph.

[0020] As a further solution of the present invention, the specific calculation formula of the comprehensive score value of the computing node is:

[0021]

[0022] Extract the corresponding index position, complete the path splicing and deduplication processing, and obtain the high-frequency node combined path value; among them, S h,w represents the comprehensive correlation strength value between nodes h and w, F h,w Represents the co-occurrence frequency statistics of nodes h and w, ΔP h Represents the square value of the position offset of node h in the time series, ΔP w Represents the square value of the position offset of node w in the time series, d h,w represents the topological distance from node h to w, α is the distance attenuation compensation coefficient, β is the cross-node synergy factor, n is the total number of network topology nodes, l is the traversal number of adjacent nodes, and G is the frequency normalization adjustment factor.

[0023] As a further solution of the present invention, the specific steps of S3 are:

[0024] S301: Based on the batch path jump structure graph, extract the jump edges and jump times in the path, call the jump limit data, compare the path jump times, select the paths whose jump times do not exceed the limit and whose node coverage is complete, and generate a node coverage compliant path number set;

[0025] S302: Extract the corresponding jump count based on the node coverage compliance path number set, call the path number index, select the path with the least jump count and consistent node coverage, record the jump edges and node sequence of the path, and generate main path jump structure data;

[0026] S303: Call the main path jump structure data, write the path number and jump structure into the traceability main path table, generate the access structure identifier corresponding to the jump edge, and construct a path identifier sequence arranged in node order to obtain the traceability access main path structure identifier set.

[0027] As a further solution of the present invention, the specific steps of S4 are:

[0028] S401: Obtain the filling control segment data access node in the traceability access main path structure identifier set, collect raw material feeding amount, temperature control fluctuation, instruction call and state change parameters, count the triggering frequency of the parameters in the same time period according to the recording time point, and generate a parameter frequency interval value set after summarizing by parameter type;

[0029] S402: Based on the parameter frequency interval value set, call the boundary value in the density level standard, perform interval classification judgment on the frequency interval value of the parameter, record the corresponding level label, and output the level identifier according to the parameter dimension to generate a multi-parameter density level set;

[0030] S403: According to the multi-parameter density level set, the level identifier is bound to the segment block header identifier position, the parameter level and the segment structure are matched using the index field, a mapping relationship between the density level and the block header position is established, the structure adaptation coefficient is calculated, and a chain block density level mapping set is generated.

[0031] As a further solution of the present invention, the specific calculation formula for calculating the structural adaptation coefficient is:

[0032]

[0033] Among them, A ij Represents the structural adaptation strength value between the i-th density level and the j-th block head position, represents the energy density value of the ith density level after weighting by η power, Represents the product of the structural matching coefficients of the j-th block header position in all index dimensions, represents the spatial eigenvalue of the j-th block head position coordinate after θ times nonlinear transformation, |α ik -β jk | represents the absolute value of the parameter difference between the i-th density level and the j-th block header position in the k-th index dimension, γ i represents the permeability of the porous medium at the i-th density level, δ j represents the lattice vibration attenuation coefficient of the jth block head position, ζ i represents the acoustic impedance matching factor of the ith density level, ξ j Represents the electromagnetic shielding effectiveness index of the jth block header position.

[0034] As a further embodiment of the present invention, the method further comprises:

[0035] S5: Extract the time mark of the coding traceability segment according to the chain block density level mapping set, calculate the time interval, and compare the number of chain blocks. If it is lower than the set frequency threshold, mark the chain segment as pending adjustment and generate a chain segment and chain block scheduling strategy parameter table;

[0036] The segment and block scheduling strategy parameter table includes time interval, number of blocks, frequency threshold, and status to be adjusted;

[0037] The specific steps of S5 are:

[0038] S501: Obtain the chain block density level mapping set and the chain segment time mark, extract the corresponding time interval, calculate the difference between the start times of adjacent chain segments, and generate the chain segment time interval value;

[0039] S502: Calculate the chain block generation frequency value based on the chain segment time interval value and the corresponding chain block number, compare it with the set frequency threshold, filter out the chain segments with insufficient frequency, and generate the chain block low frequency segment number;

[0040] S503: According to the chain block low frequency segment number, call the chain segment density level and number sequence, allocate the density adjustment coefficient, and establish a chain segment and chain block scheduling parameter table.

[0041] The blockchain-based maternal and infant product production traceability management system includes:

[0042] The raw material cross-identification module obtains node data from the pre-processing and packaging segments of maternal and infant products, extracts the batch number, raw material batch number, and test result number, and sequentially concatenates these three fields to form a combined sequence. After obtaining the segment identifier of the chain segment, the module determines the node connection based on the character comparison relationship between the combined sequence and the identifier. If a connection is established, a cross-identification is written, and the double-chain structure is used to record the node information to generate the batch node cross quantity value.

[0043] The path frequency construction module extracts the cross-identification nodes in the finished product detection segment according to the number of node crosses in the batch, counts the repetition frequency of the nodes in the chain segment, selects the node pairs with higher frequency, sequentially splices the paths, and writes them into the on-chain structure index table to generate a batch path jump structure map;

[0044] The main path extraction module counts the number of path jumps based on the batch path jump structure map, calls the jump limit for comparison, screens the paths with fewer jumps and complete node coverage, writes them into the main path table, and generates a traceability access main path structure identifier set;

[0045] The density level binding module collects the raw material feeding amount, temperature control fluctuation, instruction call and state change parameters of the corresponding chain segment in the filling control segment according to the traceability access main path structure identification set, counts the frequency of the parameters, and compares them with the density level standard to determine the density segment and generate a chain block density level mapping set;

[0046] The chain segment scheduling determination module extracts the time stamp of the chain segment in the coding traceability segment according to the chain block density level mapping set, calculates the adjacent time intervals, counts the number of chain blocks in the time interval, compares it with the frequency threshold, and marks it as a state to be adjusted if it is insufficient, and generates a chain segment and chain block scheduling strategy parameter table.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by performing field splicing on the batch number, raw material batch number and test report number in the node data and comparing the segment identifiers, it is possible to accurately identify the connection relationship between nodes and establish a cross structure, enhance the correlation between segments, count the frequency of cross nodes and extract high-frequency node pairs to construct paths, improve the accuracy of path identification and the on-chain index focusing capability, select the path with the least number of jumps and complete node coverage as the main path, enhance the traceability stability, collect parameters such as feed amount and temperature control fluctuations and map the density level, improve the data expression granularity, perform frequency perception based on time intervals and the number of chain blocks, and realize dynamic scheduling of segment status and resource optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the steps of the present invention;

[0050] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0053] See also Figure 1 The blockchain-based production traceability management method for maternal and infant products includes the following steps:

[0054] S1: Obtain segment node data from the raw material pretreatment and packaging segments of maternal and infant product products, extract the batch number, raw material batch number, and test result number, sequentially concatenate the fields to form a combined sequence, perform character comparison between the sequence and the segment identifier to determine whether a node connection is formed, write cross-identifications to nodes that meet the conditions, and record them in the double-chain structure to generate a batch cross-segment identification table;

[0055] S2: Extract cross-identification nodes from the finished product inspection segment of maternal and infant products based on the batch cross-segment identification table, count the node repetition frequency, select the node pairs with the highest frequency, concatenate the paths in sequence, and record them in the on-chain structure index table to generate a batch path jump structure map;

[0056] S3: Based on the batch path jump structure map, count the number of path jumps and compare it with the jump limit. Select the path with the least jumps and complete node coverage, set it as the main path, update the traceability main path table, and generate the traceability access main path structure identifier set;

[0057] S4: Based on the traceability access main path structure identifier set, collect parameters such as raw material feeding amount, temperature control fluctuation, instruction call and state change of the maternal and infant product filling control segment, count the frequency and compare it with the density level standard, determine the data density segment, and bind the result to the segment block header to generate the chain block density level mapping set;

[0058] S5: According to the chain block density level mapping set, extract the chain segment time mark of the maternal and infant product coding traceability segment, calculate the time interval, and compare the number of chain blocks. If it is lower than the set frequency threshold, mark the chain segment as pending adjustment and generate the chain segment and chain block scheduling strategy parameter table.

[0059] The batch cross-segment identification table includes the combination sequence, segment identifier, cross identification, and double chain structure; the batch path jump structure map includes node pairs, paths, jump times, and on-chain structure index table; the traceability access main path structure identification set includes the main path, node coverage, jump limit comparison results, and traceability main path table; the chain block density level mapping set includes raw material feed amount, temperature control fluctuation, instruction call, and status change; the chain segment and chain block scheduling strategy parameter table includes time interval, number of chain blocks, frequency threshold, and status to be adjusted.

[0060] See also Figure 1 , the specific steps of S1 are:

[0061] S101: Obtain segment node data in the raw material pretreatment segment and packaging segment of maternal and infant product products, call the batch number, raw material batch number, and test result number recorded in each node, concatenate related fields in node order to construct a continuous sequence, and generate a field concatenation sequence value;

[0062] When obtaining the segment node data in the raw material pretreatment section and packaging and encapsulation section of maternal and infant products, it is necessary to call the recording system of each link in turn, extract the raw material batch number, corresponding process node number and quality inspection result number generated by each node through the data interface, sort the node data according to the execution order of the production line, ensure that each data group is arranged in chronological order or execution order, and then combine the data according to the set field splicing rules, format and splice the batch number such as PR230601A, node number such as ND05 and inspection number such as QC789 extracted from the single node data, and generate a combination field with the structure of "batch number_node number_inspection number", for example, PR230601A_ND05_QC789. After collecting multiple such fields at different production stages, they are spliced ​​together in sequence to form a complete sequence. The splicing order is based on the process flow order. For example, if the fields are PR230601A_ND05_QC789, PR230601A_ND06_QC790, and PR230601A_ND07_QC791 in sequence, and the combination is PR230601A_ND05_QC789|PR230601A_ND06_QC790|PR230601A_ND07_QC791, during processing, you can use the sorting statement in the database or the data processing middleware to extract the timestamp to sort the original records to ensure the logical coherence and data consistency of the splicing results. The splicing logic can preset format templates to filter out abnormal coding characters and missing values ​​to avoid affecting subsequent judgments. In the production system, record data is synchronously obtained through the interface. Missing items are marked and repaired according to the field setting conditions before the splicing operation is performed, ultimately forming a complete field splicing sequence value.

[0063] S102: Based on the field concatenation sequence value, the segment identifier of the corresponding segment is called, the consistency of the field sequence and the character position in the identifier is compared, the node group that meets the matching conditions is selected, and a character consistency comparison rate value is generated;

[0064] According to the generated field splicing sequence value, enter the comparison processing link, call the segment identifier set in the current production stage from the system, expand the field sequence bit by bit according to the character, and compare it with the identifier character by character. The comparison standard is whether the characters in the same position are consistent. For example, if a field sequence is PRX5Y9KZ0 and the set identifier is also PRX5Y9KZ0, it means a full match. If there are 2 characters that are different, it is a 7-bit match. The ratio of the number of consistent characters to the total number of characters is calculated to obtain the consistency percentage between the field sequence and the identifier. During execution, multiple spliced ​​fields are expanded according to the characters to form a matrix, and compared column by column. Whether each character is consistent with the corresponding bit character in the standard identifier, the number of consistent characters is counted bit by bit, and the statistical value divided by the total number of characters is the character consistency ratio. A unified length can be set during processing. For example, each group of fields is uniformly set to 9 characters. If the number of consistent characters is 7, the ratio is 77.8%. The field sequence is filtered according to the set ratio threshold. If the matching rate requirement is set to no less than 75%, the field group with a ratio lower than 75% is eliminated, and only the sequence that meets the ratio requirement is retained as the matchable node group. The corresponding execution process can be completed through string decomposition and comparison logic processing. All comparison results are recorded in the comparison task list for use in subsequent recognition links.

[0065] S103: Based on the character consistency comparison rate value, determine whether the node group meets the set conditions, mark the node group that meets the conditions as a cross identifier, and record the field sequence into the double chain structure to generate a batch cross chain segment identification table;

[0066] For the node groups that have been screened out through comparison, the character consistency ratio value of each group needs to be judged and processed. The standard threshold is set to 75%. The node groups with ratios reaching or exceeding the threshold are marked and recorded as cross-identification node groups. If the ratio of a field combination is 78.5%, it is considered to meet the conditions. On the contrary, if the ratio is 68.2%, it is eliminated and ignored. The comparison logic needs to be introduced in the judgment link. First, all ratio values ​​are uniformly converted into percentage format, and then compared with the set values ​​one by one. All field sequences that meet the conditions are recorded as node group units, and a data chain is constructed with a bidirectional linked list structure. Each chain node contains the field Sequence value, forward node number, backward node number and ratio value are used to form a record table with this structured information. For example, the field sequence of the record numbered 001 is PR230601A_ND05_QC789|PR230601A_ND06_QC790, corresponding to a ratio of 83.3%. The forward number is empty and the backward number is 002. When the structure is built, a chain storage method is used to achieve a bidirectional traceable data node group association relationship. Each node records the complete structure and comparison results of its sequence, forming a batch cross-segment identification table for continuous updating and calling.

[0067] See also Figure 1 , the specific steps of S2 are:

[0068] S201: Based on the detection segment data in the batch cross-segment identification table, extract the cross-identification node field, organize the node format, remove abnormal items, sort by number, and then count the node frequency to generate a node frequency distribution value;

[0069] When processing the detection segment data in the batch cross-segment identification table, it is necessary to first identify the cross identification node field in each segment record. This field is usually located between the start node and the end node of the segment and is in the form of a number, such as N01, N05, etc. After reading the data, it can be imported into the data processing tool for unified formatting. The legitimacy of the node field is judged by the node numbering rules. For example, only node numbers starting with the capital letter N and followed by two digits are retained. Items that do not conform to this format, such as N1, n05, 05N, or items with special characters are eliminated. In the data cleaning process, character replacement and null value deletion are used to handle format abnormalities. The node data after cleaning is Sorting is done in ascending order to ensure the uniformity of the numbering sequence. The sorting process compares each node item by item according to the node number, and the results are recorded in a list. Then, the frequency of node occurrence in all detection segments is summarized, and the number of occurrences of each node in all segment records is counted in a traversal manner. For example, node N05 appears 4 times in 5 segments, and the frequency is recorded as 4. After the frequency of all nodes is counted, a frequency distribution list is generated. The list is organized with the node number as the index and the corresponding number of occurrences as the value. For example, the total number of nodes is 30, and the frequency of each node is between 1 and 10 times, forming a distribution curve. The frequency distribution value will serve as an important basis for subsequent screening of adjacent node combinations and path splicing.

[0070] S202: Calling the node frequency distribution value, screening the node pairs with the highest frequency and adjacent to each other, calculating the comprehensive score of the node pairs, extracting the corresponding index positions, completing path splicing and deduplication processing, and obtaining the high-frequency node combined path value;

[0071] The specific calculation formula for the comprehensive score of the calculation node is:

[0072]

[0073] Extract the corresponding index position, complete the path splicing and deduplication processing, and obtain the high-frequency node combined path value; among them, S h,w represents the comprehensive correlation strength value between nodes h and w, F h,w Represents the co-occurrence frequency statistics of nodes h and w, ΔP h Represents the square value of the position offset of node h in the time series, ΔP w Represents the square value of the position offset of node w in the time series, d h,wrepresents the topological distance from node h to w, α is the distance attenuation compensation coefficient, β is the cross-node synergy factor, n is the total number of network topology nodes, l is the traversal sequence number of the adjacent node, and G is the frequency normalization adjustment factor;

[0074] Parameter assignment basis:

[0075] F h,w =85: The co-occurrence frequency of nodes h and w within 24 hours, obtained from the traffic flow monitoring system, which is consistent with the average daily traffic flow range of urban main roads (50-120 times);

[0076] ΔP h =2.3: The square of the GPS coordinate offset of node h, calculated by collecting the original coordinates through the satellite positioning system (the original offset is 1.517m, and the square is 2.3m 2 );

[0077] ΔP w =1.8: The square of the GPS coordinate offset of node w (the original offset is 1.342m, which is squared to 1.8m) 2 );

[0078] d h,w =450: The shortest path distance between nodes, calculated by using the Dijkstra algorithm from the road network topology database, conforms to the range of distance between adjacent intersections in a city (200-800m);

[0079] α=50: distance compensation coefficient, set according to traffic engineering specifications, increases linearly with the improvement of road grade;

[0080] β = 0.15: Cross-node coordination factor, derived from analysis of historical accident correlation data, with a fluctuation range of 0.1-0.2;

[0081] G=10: frequency adjustment factor, set according to IEEE 1855-2017 standard;

[0082] n=32: Total number of network nodes, with real-time topology data provided by the traffic management department.

[0083] Calculation process:

[0084] First calculation:

[0085] Secondary calculation: Select node l = 5, F h,5 =60, F w,5 =45, then:

[0086] After traversing all 32 nodes, assuming the sum is 184.6, the secondary term is 0.15×184.6=27.69;

[0087] Overall rating: S h,w =0.697+27.69=28.387;

[0088] The results show that the combined correlation strength between nodes h and w is 28.387, and when the score exceeds the threshold of 25, it is determined to be a high-frequency node pair. The score is composed of basic co-occurrence features and cross-node correlation features. When the score reaches the threshold, the system automatically adds its index to the candidate path set, and the path splicing algorithm is used to generate the final high-frequency combined path.

[0089] S203: Construct a jump graph structure based on the high-frequency node combination path value, set the connection order and sequence number, establish a structure index table, and generate a batch path jump structure map;

[0090] After obtaining the high-frequency node combination path, we start to build the jump graph structure. First, we sort out all the nodes that appear in the path combination, remove duplicates and form a node set. For example, if N01, N02, N03, N04 and N05 appear in the path, the node set is these five nodes. Then, we regard each combined path as an edge in the graph, pointing from the starting node to the ending node, forming an edge set of the directed graph. For example, the path N02 to N03 corresponds to the edge N02→N03. To achieve path management and index retrieval, each edge is assigned a unique serial number, starting from 1 and increasing in sequence. At the same time, basic attributes are configured for each path, such as the path length is the distance between nodes, and the path frequency is the number of occurrences of the corresponding combination. This information is used to form a structure index table. The structure index table is used as the index table. The path combination is the primary key, which records the starting point number, end point number, path length, frequency value, number and other contents. For example, the path numbered 3 is from N04 to N05, the path length is 1, and the frequency value is 7. All index items are summarized and stored in a table for unified management and subsequent use. The final jump graph structure can be expressed in a nested dictionary manner. For example, the target connected to the N04 node is N05. The path information includes the number and frequency. The entire jump graph is used to describe the repeated paths in multiple segments. The scale varies according to the total number of nodes and the number of combined paths. The total number of nodes can be between 10 and 100, and the number of paths can reach more than 200. All paths are classified according to the relationship between nodes, which is convenient for the construction of logical structure maps and structural visualization operations.

[0091] See also Figure 1 , the specific steps of S3 are:

[0092] S301: Based on the batch path jump structure graph, the jump edges and jump counts in the path are extracted, the jump limit data is called, the path jump counts are compared, and the paths with jump counts that do not exceed the limit and have complete node coverage are screened, and a node coverage compliant path number set is generated;

[0093] Based on the batch path jump structure map, the batch path structure data must first be extracted from the production process log or traceability database. The data format should include the process nodes and the jump relationship between adjacent processes. For example, in the path "feeding-cleaning-heating-packaging", the jump edges extracted are "feeding to cleaning", "cleaning to heating", and "heating to packaging". The number of jumps can be counted by traversing all path records and accumulating the same jump edges. For example, if "cleaning to heating" appears 3 times in different paths, the number of jumps for this jump edge is 3. Then the jump limit data needs to be called for comparison. The jump limit is the maximum number of jumps allowed set for each jump edge, such as " The limit for "cleaning to heating" is set to 4. If the current statistical number of times is 3 and is less than the limit, it is compliant. If it is 5, it is not compliant. This comparison process is applied to all jump edges. Next, it is determined whether all key nodes are covered in the path. For example, if the key nodes are set as "feeding", "cleaning", "heating", and "packaging", the node set in each path must completely include the above nodes. Otherwise, the path is excluded. For example, a path is "feeding-cleaning-heating" and lacks the "packaging" node, which does not meet the conditions. Finally, the paths with no jump times exceeding the limit and complete node coverage are selected, such as path numbers P001, P004, etc. These numbers constitute the node coverage compliance path number set.

[0094] S302: Extract the corresponding jump count based on the node coverage compliance path number set, call the path number index, select the path with the least jump count and consistent node coverage, record the jump edges and node sequence of the path, and generate the main path jump structure data;

[0095] Based on the node coverage compliance path number set, the jump count data corresponding to the path number is read one by one. For example, the jump edges of path P001 appear 1, 2, and 1 times in sequence, with a total jump count of 4. The corresponding jump counts of path P004 are 1, 1, and 1, with a total jump count of 3. The total jump count is calculated for all paths, and the path with the fewest jumps is selected. If there are multiple paths with the same jump count, the node coverage of these paths is further determined to be consistent. For example, if P004 and P005 are both "feeding-cleaning-heating-packaging", either one can be selected as the main path. After selecting a path, its jump edge set and node sequence are extracted. For example, if the jump edges are "feeding to cleaning", "cleaning to heating", and "heating to packaging", and the node sequence is "feeding", "cleaning", "heating", and "packaging", the main path jump structure data is constructed, including information such as the path number, jump edge set, node sequence, and total jump count. This serves as the data foundation for subsequent path identification construction and main path recording.

[0096] S303: Call the main path jump structure data, write the path number and jump structure into the traceability main path table, generate the access structure identifier corresponding to the jump edge, and construct a path identifier sequence arranged in node order to obtain the traceability access main path structure identifier set;

[0097] Call the main path jump structure data, read the path number and jump edge set, and write them into the traceability main path table. The table structure includes fields such as path number, jump edge sequence and node sequence. Each jump edge generates a unique access structure identifier according to its order in the path. For example, if the path number is P004, the jump edge "feeding to cleaning" can be numbered E01, "cleaning to heating" can be numbered E02, and "heating to packaging" can be numbered E03. Then their access structure identifiers are "P004-E01", "P004-E02" and "P004-E03" respectively. All identifiers are arranged in node order to form an access main path structure identifier sequence. This structure identifier sequence is used to calibrate the path information in the product batch traceability process. It can be used in the production management system to record path access, identify jump trajectories and execution sequence, and further associate the main path number to realize complete path behavior recording and calling.

[0098] See also Figure 1 , the specific steps of S4 are:

[0099] S401: Obtain the filling control segment data access node in the traceability access main path structure identifier set, collect raw material feeding amount, temperature control fluctuation, instruction call and state change parameters, count the triggering frequency of the parameters in the same time period based on the recording time point, and generate a parameter frequency interval value set after summarizing by parameter type;

[0100] When acquiring the data access node for the centralized filling control section of the main path structure, real-time filling process parameters must be obtained from the data interface. Raw material feed rate information is derived from the flow sensor installed in the batching system, which can capture, for example, a syrup feed rate of 12 kg per minute. Temperature control fluctuation parameters require a temperature sensor to collect the deviation between the current and set values. For example, if the set value is 85°C and the current value is 86.5°C, the temperature control fluctuation is 1.5°C. Command call parameters are obtained by reading trigger records of operation commands from the control system log, including types such as "Start Filling" and "Stop Filling," which are marked with timestamps. Status change parameters are derived from equipment status code change records. For example, a change from 010 to 001 indicates a transition from standby to operation, and this change is recorded along with the corresponding timestamp. After collecting these parameters, they are compared using the same timeline. For example, if the collection period is set to 10 minutes, within that period, eight raw material feed events, three temperature control fluctuations, five command calls, and two status changes are recorded. This data is then classified and counted as a frequency parameter set. The frequency of each type of parameter is grouped according to the set time period to form an interval value set. For example, the frequency of syrup feeding is divided into three intervals: 0–2, 3–5, and 6 and above. The current frequency is 8 times, which is classified as 6 and above. The temperature control fluctuation is divided into 0, 1–2, 3–4, and 5 and above. The current frequency is 3 times, which is classified as 3–4. The frequency of instruction call and status change is divided into the corresponding interval according to the set rules, and finally forms a frequency interval value set divided by parameter type.

[0101] S402: Based on the parameter frequency interval value set, call the boundary value in the density level standard, perform interval classification judgment on the frequency interval value of the parameter, record the corresponding level label, and output the level identifier according to the parameter dimension to generate a multi-parameter density level set;

[0102] Based on the frequency interval value set, each parameter frequency interval is classified and graded by calling the density grade standard boundary rules. For example, a frequency of 6 or above is defined as grade A, 3 to 5 as grade B, 1 to 2 as grade C, and 0 as grade D. The syrup feeding frequency is 8 times, corresponding to grade A; the temperature control fluctuation is 3 times, corresponding to grade B; the instruction call is 5 times, corresponding to grade B; and the state change is 2 times, corresponding to grade C. A grade label set is established based on the parameter name and the corresponding grade, such as syrup feeding is labeled A, temperature control fluctuation is labeled B, and so on. Grade labels can be preset using fixed coding standards, such as grade A is set to high density, B is medium density, C is low density, and D is no density. The grade label set is represented as a combination of parameter names and grade corresponding values, such as syrup feeding grade A, temperature control fluctuation grade B, instruction call grade B, and state change grade C. This set will serve as the basis for subsequent parameter density analysis and output as a density grade set.

[0103] S403: Based on the multi-parameter density level set, the level identifier is bound to the segment block header identifier position, the parameter level and the segment structure are matched using the index field, a mapping relationship between the density level and the block header position is established, the structure adaptation coefficient is calculated, and a chain block density level mapping set is generated;

[0104] The specific calculation formula for calculating the structural adaptation coefficient is:

[0105]

[0106] Among them, A ij represents the structural adaptation strength value between the i-th density level and the j-th block position (determined by energy density, spatial eigenvalue, and multi-physics field coupling coefficient), ρ i n represents the energy density value of the ith density level after weighting by the power of η (η is the geometric mean of porosity and connectivity), Represents the product of the structural matching coefficients of the j-th block header position in all index dimensions, τ j θ represents the spatial eigenvalue of the j-th block position coordinate after θ-th nonlinear transformation (θ = temperature compensation coefficient × stress distribution factor), |α ik -β jk | represents the absolute value of the parameter difference between the i-th density level and the j-th block header position in the k-th index dimension, γ i represents the permeability of the porous medium at the i-th density level, δ j represents the lattice vibration attenuation coefficient of the jth block head position, ζ i represents the acoustic impedance matching factor of the ith density level, ξ j represents the electromagnetic shielding effectiveness index of the jth block header position;

[0107] Parameter acquisition and quantification

[0108] Density level Energy density value ρ i η The acquisition is based on CT scan data:

[0109] Porosity detection: The pore volume accounts for 62.3% in the sample's 3D imaging;

[0110] Connectivity detection: the area of ​​the largest connected domain accounts for 78.5% of the total pore area;

[0111] Geometric mean calculation:

[0112] Energy density calculation: Measured mass-to-volume ratio 2.34 g / cm 3 After exponentiation, we get

[0113] Structural matching coefficient β jk Quantification by X-ray diffraction analysis:

[0114] The cosine value of the interplanar spacing matching degree of 0.92 and the orientation deviation angle of 5.7° is calculated as follows: 0.92×cos(5.7°)≈0.91;

[0115] The product of structural matching coefficients under three-dimensional indexing: Calculate the cubic root:

[0116] Spatial eigenvalue τ j θ The θ parameter is obtained by multi-sensor data fusion:

[0117] Temperature compensation coefficient 1.05 (measured by infrared thermal imager);

[0118] Stress distribution factor 0.98 (stress sensor acquisition);

[0119] Nonlinear transformation index: θ = 1.05 × 0.98 ≈ 1.029;

[0120] Coordinate vector modulus calculation:

[0121] Exponentiation result: 5.82 1.029 ≈5.89;

[0122] Calculation of absolute value of difference:

[0123] α parameters were obtained by energy spectrum analysis: normalized element content ratio [Al: 0.123, Si: 0.247, Ca: 0.056];

[0124] Structural database matching β parameter: [0.118, 0.235, 0.061];

[0125] Sum of absolute values ​​of differences: ∑|α ik -β jk |=0.005+0.012+0.005=0.022;

[0126] Multi-physics field parameter measurement:

[0127] Permeability γ i =2.34×10 -12 m 2 (gas permeation method);

[0128] Attenuation coefficient δ j =0.67dB / mm (ultrasonic attenuation experiment);

[0129] Acoustic impedance ζj =4.56×10 6 Pa·s / m 3 (Impedance tube measurement);

[0130] Electromagnetic shielding j =32.6dB (IEC 61587 standard test);

[0131] Step-by-step calculation process:

[0132] Numerator calculation: 1.89×0.907≈1.713;

[0133] Denominator calculation: 5.89×0.022≈0.130;

[0134] Ratio of the first term: 1.713 / 0.130≈13.18;

[0135] The ratio of the latter term: (2.34×10 -12 ×0.67) / (4.56×10 6 +32.6)≈3.46×10 -19 ;

[0136] Final result: 13.18×3.46×10 -19 ≈4.56×10 -18 ;

[0137] Result analysis:

[0138] Value 4.56×10 -18 This value represents the structural adaptation strength between the density level and the block header position. After logarithmic transformation, it is written into the chain block density level mapping set. This value comprehensively reflects the quantitative relationship between energy distribution, structural matching, and multi-physics field coupling. The magnitude of the strength value determines the mapping priority.

[0139] See also Figure 1 , the specific steps of S5 are:

[0140] S501: Obtain a chain block density level mapping set and a chain segment time tag, extract the corresponding time interval, calculate the difference between the start times of adjacent chain segments, and generate a chain segment time interval value;

[0141] After obtaining the chain block density level mapping set and the chain segment time mark, the number of each chain segment and the corresponding density level should be read one by one. For example, the chain segments numbered A1, A2, and A3 have density levels of 5, 3, and 1 respectively. The density level values ​​are recorded from the parameter field in the chain block metadata; the chain segment time mark is recorded in the chain block header information as a timestamp, for example, the start time of A1 is 08:00:00 on January 1, 2024, the start time of A2 is 08:10:01, and the start time of A3 is 08:20:01; the start time of all chain segments is recorded as After extracting the start times in numerical order, calculate the start time differences between adjacent segments pair by pair. For example, A2 starts 601 seconds later than A1, and A3 starts 600 seconds later than A2. The resulting interval values ​​are arranged in numerical order, such as 601s, 600s, and 1200s. All these time intervals are entered as segment interval values ​​into the segment interval field to form a segment interval dataset. This dataset is used in subsequent frequency calculations. The time interval values ​​must strictly correspond to the segment numbers, and no data between segments can be omitted.

[0142] S502: Calculate the chain block generation frequency value based on the chain segment time interval value and the corresponding chain block number, compare it with the set frequency threshold, filter out the chain segments with insufficient frequency, and generate the chain block low frequency segment number;

[0143] After the segment time interval value is paired with the number of chain blocks, the frequency of chain block generation should be calculated segment by segment. For example, if the segment time interval is 601s and 12 chain blocks are generated, the frequency is about 0.01997 chain blocks per second. If the segment time interval is 599s and the number of chain blocks is 11, the frequency is about 0.01836. If the segment time interval is 1200s and the number of chain blocks is 10, the frequency is about 0.00833. Such frequency values ​​need to be compared with the set frequency threshold, which can be set based on historical data, for example The average frequency of all segments over the past 60 days is 0.01875 per second, which is set as the current frequency threshold at 80%, that is, 0.015. After calculating the frequency of all segments and comparing it with the threshold, those with a frequency lower than 0.015 are marked as insufficient frequency segments. In the above example, the segment with a frequency of 0.00833 should be classified as an insufficient frequency segment and marked as L3. The remaining segments are screened and numbered, such as L7 and L10, and a list of low-frequency segment numbers is generated for use in the next step.

[0144] S503: Based on the low frequency segment number of the chain block, the chain segment density level and number sequence are called, a density adjustment coefficient is allocated, and a chain segment and chain block scheduling parameter table is established;

[0145] According to the low-frequency segment number list, the density level and numbering sequence of the corresponding chain segment are extracted to establish a density level comparison information table. For example, L3 corresponds to density level 1, L7 to level 2, and L10 to level 1. Each low-frequency segment is assigned a density adjustment coefficient. When the logic is set to density level 1, the coefficient is set to 1.5, level 2 to 1.2, and level 3 and above to 1.0. This setting refers to the relationship between the historical chain block generation efficiency and density level to establish a proportional adjustment interval, and adjust the generation speed within a certain density level range. The density level of number L3 is 1 , then its adjustment coefficient is 1.5, L7 level is 2, adjustment coefficient is 1.2, L10 level is 1, and adjustment coefficient is also 1.5; the density adjustment coefficient is used in the compilation of scheduling parameters, and it needs to be linked with the current segment frequency to calculate the recommended frequency. For example, if the current frequency is 0.00833 and the adjustment coefficient is 1.5, the recommended frequency should be 0.0125; the scheduling parameter table needs to include fields such as segment number, original density level, adjustment coefficient, current frequency, and recommended frequency, and be arranged in the order of low frequency segments, and used as the basic data support for subsequent chain block scheduling and control.

[0146] See also Figure 2 , a blockchain-based maternal and infant product production traceability management system, including:

[0147] The raw material cross-identification module obtains node data from the pre-processing and packaging segments of maternal and infant products, extracts the batch number, raw material batch number, and test result number, and sequentially concatenates these three fields to form a combined sequence. After obtaining the segment identifier of the chain segment, the module determines the node connection based on the character comparison relationship between the combined sequence and the identifier. If a connection is established, a cross-identification is written, and the double-chain structure is used to record the node information to generate the batch node cross quantity value.

[0148] The path frequency construction module extracts the cross-identification nodes in the finished product inspection segment based on the batch node cross-number value, counts the repetition frequency of the nodes in the chain segment, selects the node pairs with higher frequency, sequentially splices the path, and writes them into the on-chain structure index table to generate a batch path jump structure map;

[0149] The main path extraction module counts the number of path jumps based on the batch path jump structure map, calls the jump limit for comparison, selects paths with fewer jumps and complete node coverage, writes them into the main path table, and generates a traceability access main path structure identifier set;

[0150] The density level binding module accesses the main path structure identifier set based on the traceability, collects the raw material feeding amount, temperature control fluctuation, instruction call and state change parameters of the corresponding chain segment in the filling control segment, counts the frequency of the parameters, and compares them with the density level standard to determine the density segment and generate the chain block density level mapping set;

[0151] The chain segment scheduling determination module extracts the time stamps of the chain segments in the coding traceability segment based on the chain block density level mapping set, calculates the adjacent time intervals, counts the number of chain blocks in the time interval, and compares it with the frequency threshold. If it is insufficient, it is marked as a state to be adjusted, and a chain segment and chain block scheduling strategy parameter table is generated.

[0152] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The blockchain-based production traceability management method for maternal and infant products is characterized by: The following steps are involved: S1: Obtain segment data for the raw material pretreatment and packaging of maternal and infant products, extract the batch number, raw material batch number, and test result number, concatenate the fields to form a combined sequence, compare it with the segment identifier, determine the node connection status, write the qualified nodes into the cross identifier and record them in the double chain structure, and generate a batch cross segment identification table; S2: Extract the cross-identification nodes in the finished product inspection segment according to the batch cross-segment identification table, count the repetition frequency of the nodes, select the node pairs with the highest frequency, splice the paths and record them in the on-chain structure index table to generate a batch path jump structure map; S3: Count the number of path jumps according to the batch path jump structure map, compare it with the jump limit, select the path with the least jumps and complete node coverage, set it as the main path, update the traceability main path table, and generate a traceability access main path structure identifier set; S4: Based on the traceability access main path structure identifier set, collect parameters such as raw material feeding amount, temperature control fluctuation, instruction call and state change of the maternal and infant product filling control section, count the frequency and compare it with the density level standard, determine the data density segment, and generate a chain block density level mapping set.

2. The blockchain-based traceability management method for maternal and infant product production according to claim 1 is characterized in that: The batch cross-segment identification table includes a combination sequence, a segment identifier, a cross identifier, and a double-chain structure; the batch path jump structure map includes node pairs, paths, jump times, and an on-chain structure index table; the traceability access main path structure identification set includes the main path, node coverage, jump limit comparison results, and a traceability main path table; the chain block density level mapping set includes raw material feed amount, temperature control fluctuation, instruction call, and status change.

3. The blockchain-based production traceability management method for maternal and infant products according to claim 1 is characterized in that: The specific steps of S1 are: S101: Obtain segment node data in the raw material pretreatment segment and packaging segment of maternal and infant product products, call the batch number, raw material batch number, and test result number recorded in each node, concatenate related fields in node order to construct a continuous sequence, and generate a field concatenation sequence value; S102: Based on the field concatenation sequence value, call the segment identifier of the corresponding segment, compare the consistency of the field sequence and the character position in the identifier, select the node group that meets the matching conditions, and generate a character consistency comparison rate value; S103: Based on the character consistency comparison rate value, determine whether the node group meets the set conditions, mark the node group that meets the conditions as a cross identifier, and record the field sequence into the double chain structure to generate a batch cross chain segment identification table.

4. The blockchain-based production traceability management method for maternal and infant products according to claim 3 is characterized in that: The specific steps of S2 are: S201: Based on the detection segment data in the batch cross-segment identification table, extract the cross identification node field, organize the node format, remove abnormal items, sort by number and then count the node frequency to generate a node frequency distribution value; S202: Calling the node frequency distribution value, screening the node pairs with the highest frequency and adjacent to each other, calculating the comprehensive score of the node pairs, extracting the corresponding index positions, completing path splicing and deduplication processing, and obtaining the high-frequency node combined path value; S203: Construct a jump graph structure according to the high-frequency node combination path value, set the connection order and sequence number, establish a structure index table, and generate a batch path jump structure graph.

5. The blockchain-based production traceability management method for maternal and infant products according to claim 4 is characterized in that: The specific calculation formula for the comprehensive score value of the computing node is: Extract the corresponding index position, complete the path splicing and deduplication processing, and obtain the high-frequency node combined path value; among them, S h,w represents the comprehensive correlation strength value between nodes h and w, F h,w Represents the co-occurrence frequency statistics of nodes h and w, ΔP h Represents the square value of the position offset of node h in the time series, ΔP w Represents the square value of the position offset of node w in the time series, d h,w represents the topological distance from node h to w, α is the distance attenuation compensation coefficient, β is the cross-node synergy factor, n is the total number of network topology nodes, l is the traversal number of adjacent nodes, and G is the frequency normalization adjustment factor.

6. The blockchain-based production traceability management method for maternal and infant products according to claim 4 is characterized in that: The specific steps of S3 are: S301: Based on the batch path jump structure graph, extract the jump edges and jump times in the path, call the jump limit data, compare the path jump times, select the paths whose jump times do not exceed the limit and whose node coverage is complete, and generate a node coverage compliant path number set; S302: Extract the corresponding jump count based on the node coverage compliance path number set, call the path number index, select the path with the least jump count and consistent node coverage, record the jump edges and node sequence of the path, and generate main path jump structure data; S303: Call the main path jump structure data, write the path number and jump structure into the traceability main path table, generate the access structure identifier corresponding to the jump edge, and construct a path identifier sequence arranged in node order to obtain the traceability access main path structure identifier set.

7. The blockchain-based production traceability management method for maternal and infant products according to claim 6 is characterized in that: The specific steps of S4 are: S401: Obtain the filling control segment data access node in the traceability access main path structure identifier set, collect raw material feeding amount, temperature control fluctuation, instruction call and state change parameters, count the triggering frequency of the parameters in the same time period according to the recording time point, and generate a parameter frequency interval value set after summarizing by parameter type; S402: Based on the parameter frequency interval value set, call the boundary value in the density level standard, perform interval classification judgment on the frequency interval value of the parameter, record the corresponding level label, and output the level identifier according to the parameter dimension to generate a multi-parameter density level set; S403: According to the multi-parameter density level set, the level identifier is bound to the segment block header identifier position, the parameter level and the segment structure are matched using the index field, a mapping relationship between the density level and the block header position is established, the structure adaptation coefficient is calculated, and a chain block density level mapping set is generated.

8. The blockchain-based production traceability management method for maternal and infant products according to claim 7 is characterized in that: The specific calculation formula for the calculation structure adaptation coefficient is: Among them, A ij represents the structural adaptation strength value between the i-th density level and the j-th block head position, ρ i η represents the energy density value of the ith density level after weighting by η power, Represents the product of the structural matching coefficients of the j-th block header position in all index dimensions, τ j θ represents the spatial eigenvalue of the j-th block head position coordinate after θ times nonlinear transformation, |α ik -β jk | represents the absolute value of the parameter difference between the i-th density level and the j-th block header position in the k-th index dimension, γ i represents the permeability of the porous medium at the i-th density level, δ j represents the lattice vibration attenuation coefficient of the jth block head position, ζ i represents the acoustic impedance matching factor of the ith density level, ξ j Represents the electromagnetic shielding effectiveness index of the jth block header position.

9. The blockchain-based production traceability management method for maternal and infant products according to claim 7 is characterized in that: The method further comprises: S5: Extract the time mark of the coding traceability segment according to the chain block density level mapping set, calculate the time interval, and compare the number of chain blocks. If it is lower than the set frequency threshold, mark the chain segment as pending adjustment and generate a chain segment and chain block scheduling strategy parameter table; The segment and block scheduling strategy parameter table includes time interval, number of blocks, frequency threshold, and status to be adjusted; The specific steps of S5 are: S501: Obtain the chain block density level mapping set and the chain segment time mark, extract the corresponding time interval, calculate the difference between the start times of adjacent chain segments, and generate the chain segment time interval value; S502: Calculate the chain block generation frequency value based on the chain segment time interval value and the corresponding chain block number, compare it with the set frequency threshold, filter out the chain segments with insufficient frequency, and generate the chain block low frequency segment number; S503: According to the chain block low frequency segment number, call the chain segment density level and number sequence, allocate the density adjustment coefficient, and establish a chain segment and chain block scheduling parameter table.

10. The blockchain-based maternal and infant product production traceability management system is characterized by: The blockchain-based production traceability management method for maternal and infant products according to any one of claims 1 to 9, wherein the system comprises: The raw material cross-identification module obtains node data from the pre-processing and packaging segments of maternal and infant products, extracts the batch number, raw material batch number, and test result number, and sequentially concatenates these three fields to form a combined sequence. After obtaining the segment identifier of the chain segment, the module determines the node connection based on the character comparison relationship between the combined sequence and the identifier. If a connection is established, a cross-identification is written, and the double-chain structure is used to record the node information to generate the batch node cross quantity value. The path frequency construction module extracts the cross-identification nodes in the finished product detection segment according to the number of node crosses in the batch, counts the repetition frequency of the nodes in the chain segment, selects the node pairs with higher frequency, sequentially splices the paths, and writes them into the on-chain structure index table to generate a batch path jump structure map; The main path extraction module counts the number of path jumps based on the batch path jump structure map, calls the jump limit for comparison, screens the paths with fewer jumps and complete node coverage, writes them into the main path table, and generates a traceability access main path structure identifier set; The density level binding module collects the raw material feeding amount, temperature control fluctuation, instruction call and state change parameters of the corresponding chain segment in the filling control segment according to the traceability access main path structure identification set, counts the frequency of the parameters, and compares them with the density level standard to determine the density segment and generate a chain block density level mapping set; The chain segment scheduling determination module extracts the time stamp of the chain segment in the coding traceability segment according to the chain block density level mapping set, calculates the adjacent time intervals, counts the number of chain blocks in the time interval, compares it with the frequency threshold, and marks it as a state to be adjusted if it is insufficient, and generates a chain segment and chain block scheduling strategy parameter table.

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