Blockchain-based construction engineering consultation data credible evidence method

Abstract

This invention discloses a blockchain-based method for credible data storage in construction engineering consulting, applicable to the reliable retention of acceptance records for concealed works before pouring in cast-in-place concrete structure construction. The method determines the evaluation trigger time at the pouring release trigger time, calculates the idle time based on the time difference between the data collection time and the evaluation trigger time, and introduces a minimum operation time threshold to zero out evidence entries with insufficient idle time. For evidence entries meeting the threshold, an asymmetric attenuation constraint is applied according to the structural topology category of the acceptance criteria to calculate the evidence confidence level. Then, the key point confidence level is obtained according to a preset aggregation rule, outputting a release or postponement decision and a minimum supplementary data collection list. The evaluation trigger time, key point confidence level, and decision result, among other frozen fields, are written into a data storage digest, and a data storage digest summary value is generated and written to the blockchain to form an on-chain data storage record, thereby supporting multi-party reconciliation and non-repudiation verification.

Description

Technical Field

[0001] This invention relates to the field of engineering information management and data storage technology, and more specifically, to a blockchain-based method for trusted storage of architectural engineering consulting data. Background Technology

[0002] Engineering information management and data storage technologies are commonly used in concrete structure construction. During this process, prior to pouring, it is typically necessary to inspect and record concealed works such as the arrangement and connection of reinforcing bars, the thickness of the protective layer, and the location and elevation of embedded parts. Conventional digital methods often archive acceptance records at the document or attachment level, calculating summary values ​​or storing them on a blockchain to prove that the content has not been tampered with afterward. This approach reduces the risk of post-construction tampering and improves the traceability and reliability of record keeping.

[0003] The existing technology has the following shortcomings: On the one hand, when there is a time interval between the collection of evidence items and the triggering time for pouring, existing practices typically lack a calculable evaluation of the time difference between the collection time and the release triggering time, and also lack a distinguishable processing of differences in structural topology between different acceptance criteria. The consequence is that expired evidence may still be accepted, and reconciliation is difficult when concealed objects cannot be directly verified after pouring. The difficulty lies in the fact that document or attachment-level solidification can only prove that the content has not been tampered with, and it is difficult to reflect whether the evidence items are still consistent with the site condition at the release triggering time. On the other hand, when evidence collection is close to the pouring release triggering time or the collection process is too short, there is a risk of re-photographing or staging, making the evidence items unsuitable for direct, highly credible acceptance. The consequence is an increased risk of erroneous release, or the forced expansion of manual review and re-collection scope leading to uncontrollable efficiency. The difficulty lies in the fact that relying solely on archived content and its solidification results makes it difficult to provide a reviewable and executable blocking conclusion and a minimum scope of re-collection at the release triggering time.

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a blockchain-based trusted evidence storage method for construction engineering consulting data, in order to solve the problems raised in the background art. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: Blockchain-based trusted data storage methods for construction engineering consulting include: S101, Obtain the acceptance checklist and obtain the corresponding evidence item for each acceptance checklist, wherein the evidence item includes at least the evidence type, collection time, and evidence content summary; and mark the acceptance checklist as node-type checklist or rod-type checklist based on the structural topology. S102, respond to the pouring release trigger event to determine the evaluation trigger time, and calculate the idle time as the difference between the evaluation trigger time and the collection time; when the idle time is less than the minimum operation time, the evidence confidence of the corresponding evidence item is set to zero; otherwise, the idle time is input into the decay function according to the topology category of the acceptance key point to obtain the evidence confidence; whereby, for any same idle time, the evidence confidence of the node type key point is not higher than the evidence confidence of the rod type key point; S103, for each acceptance point, aggregate the evidence confidence level corresponding to the acceptance point according to the preset aggregation rule to obtain the point confidence level; when the point confidence level of any node-type point is lower than the node-type point threshold, generate a decision to postpone release and output the minimum supplementary collection list, otherwise generate a release decision; generate a storage summary, write the evaluation trigger time, the point confidence level of each acceptance point and the pouring release decision as frozen fields into the storage summary, and generate a storage summary summary value for the storage summary and write it into the blockchain to form an on-chain storage record.

[0006] In a preferred embodiment, the evidence content digest is the evidence digest value obtained by performing a digest algorithm on the evidence content.

[0007] In a preferred embodiment, obtaining the corresponding evidence item for each acceptance point includes determining the evidence type list corresponding to the acceptance point, and selecting evidence items whose evidence type falls into the evidence type list from the evidence items as the corresponding evidence item for the acceptance point.

[0008] In a preferred embodiment, labeling structural topology points as node-type or rod-type points includes labeling acceptance points involving structural connection parts as node-type points and labeling acceptance points involving non-connecting straight segments as rod-type points.

[0009] In a preferred embodiment, the structural connection portion includes at least one of beam-column joint, rebar lap joint, sleeve connection portion, and connection portion between embedded part and main structure. The non-connection straight segment includes at least one of the straight segment of rebar in beam, slab, or wall and the straight segment of embedded part in non-connection section.

[0010] In a preferred embodiment, the evaluation trigger time is the time when the pouring release trigger event occurs, which includes a pouring instruction trigger event or a preset evidence preservation trigger event.

[0011] In a preferred embodiment, before calculating the idle time, the evaluation trigger time and the collection time are aligned to ensure that they are on the same time base and at the same time granularity.

[0012] In a preferred embodiment, the minimum operation time is determined by a preset operation process for concealed acceptance, which characterizes the minimum duration required to complete evidence collection and acceptance recording.

[0013] In a preferred embodiment, the decay function is a monotonically non-increasing function with respect to idle time, and the output evidence confidence falls within a preset confidence interval. Furthermore, the decay function parameters are selected based on the topology category and evidence type of the acceptance criteria, and for any identical idle time, the decay function parameter corresponding to the node category criteria is not less than the decay function parameter corresponding to the rod category criteria.

[0014] In a preferred embodiment, the preset aggregation rule is a weighted aggregation rule. Weight coefficients are configured for evidence items according to evidence type, and the confidence scores of evidence corresponding to the same acceptance point are weighted and aggregated to obtain the confidence score of the point. When the confidence score of any node-type point is lower than the node-type point threshold, a decision to postpone release is generated and a minimum supplementary collection list is output. Otherwise, a release decision is generated. The evidence summary further includes the minimum supplementary collection list and the confidence scores of each acceptance point. The evaluation trigger time, the confidence scores of each acceptance point, and the pouring release decision are written as frozen fields into the evidence summary, and the evidence summary summary value of the evidence summary is written into the blockchain to form an on-chain evidence record.

[0015] The effects and advantages of the blockchain-based trusted data storage method for construction engineering consulting in this invention: This invention provides a blockchain-based method for credible evidence storage of construction engineering consulting data. It determines the evaluation trigger time by using the pouring release trigger event and calculates the evidence confidence level using idle time as a uniform metric, ensuring consistent credibility of evidence items at the release decision time. Evidence items with idle time less than the minimum operation time are set to zero, and asymmetric decay constraints are applied according to topology categories to suppress staged evidence and expired acceptance, and strengthen key point constraints for node types. Based on evidence confidence level aggregation, key point confidence levels are obtained, and a release or delayed release and minimum supplementary data collection list are output, ensuring the supplementary data collection scope is minimal and executable. Frozen fields are written into the evidence storage summary and fixed on the blockchain, supporting multi-entity reconciliation and non-repudiation verification. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram illustrating the traditional strategy's determination and on-chain solidification. Figure 3 This is a schematic diagram illustrating the determination and on-chain solidification of the strategy of this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides a blockchain-based method for trusted data storage in construction engineering consulting. In cast-in-place concrete structure construction, before pouring, evidence items must be collected and recorded regarding key acceptance points for concealed works, such as reinforcement arrangement and connection, protective layer thickness, and the location and elevation of embedded parts. After pouring, the concealed objects enter a state where direct verification is not possible. The key basis for subsequent quality disputes and accountability can only be the consistency between the evidence items, their collection time, and the site conditions. Therefore, at the pouring release trigger moment, the key inputs that the system can directly rely on are the acceptance point list, evidence items, and their collection time. The evaluation trigger moment is determined by the pouring release trigger event to conduct the evaluation.

[0019] Current practices often archive evidence at the granular level of records or attachments, calculating summary values ​​or storing them on the blockchain to prove that the content has not been tampered with. This approach can reduce the risk of post-event tampering, but it assumes that evidence items can be accepted with long-term equivalent validity once collected, and that different acceptance criteria can be handled with the same standard, thus eliminating the need to establish a calculable and credible evaluation standard for idle time and structural topology differences. However, in actual working conditions, the credibility of evidence items decreases with increasing idle time, and node-type criteria require stricter constraints than rod-type criteria. At the same time, when the idle time is less than the minimum operation time, there is a risk of staged evidence, making evidence items that are close to being released unsuitable for high-confidence acceptance. As a result, expired evidence may still be released, or supplementary evidence may be mistakenly accepted, and the scope of manual review is easily out of control, making it difficult to provide an executable blocking conclusion or a minimum supplementary collection range at the release trigger moment.

[0020] Therefore, this invention uses the difference between the evaluation trigger time and the collection time to form the idle time caliber, introduces a minimum operation time threshold, applies an asymmetric decay function according to the topology category to calculate the evidence confidence, obtains the key point confidence according to the preset aggregation rules, and outputs the release or postponement release decision and the minimum supplementary collection list. Then, the frozen fields such as the evaluation trigger time, key point confidence and decision result are written into the evidence storage digest and the evidence storage digest summary value is written into the blockchain to form an on-chain evidence storage record, so as to support multi-entity reconciliation and non-repudiation verification.

[0021] Based on the above design, this invention constructs a complete process for a blockchain-based trusted data storage method for architectural engineering consulting, consisting of steps S101 to S103 sequentially. (Refer to...) Figure 1 , Figure 1This is a schematic diagram of the method flow of the present invention, which includes: Step S101, Acceptance Key Points and Evidence Initialization and Topology Category Labeling, is used to initialize acceptance key points and label topology categories before the pouring and release evaluation, establishing the correspondence between acceptance key points and evidence items and initializing the state set. This step reads the acceptance key point list X101 and the evidence item set X102, forming the key point identifier and topology category for each acceptance key point, and collects the corresponding evidence item index and collection time, writing them into the acceptance key point evaluation state set R101 for subsequent steps to read to calculate idle time and update evidence confidence. The evidence item index group in the acceptance key point evaluation state set R101 is used to record the evidence item set corresponding to each acceptance key point, enabling subsequent steps to perform confidence calculation and aggregation on the evidence items without expanding the evidence content.

[0022] Step S102, evaluating the trigger and calculating the confidence level of evidence, is used to respond to the pouring release trigger event during the pouring release evaluation phase. It determines the evaluation trigger time and performs threshold zeroing and asymmetric attenuation calculations based on idle time for each evidence item, thereby forming the evidence item-level confidence level. This step reads the acceptance key point evaluation status set R101 and the pouring release trigger event X103, writing the evaluation trigger time group, time alignment and idle time group, threshold zeroing and attenuation parameter group, and evidence confidence level group into R101. Optionally, it can export and generate the evidence confidence level result set R102 for subsequent steps to read for aggregation and decision-making. The threshold zeroing and attenuation parameter group in R101 records the minimum operation time threshold and attenuation parameter selection results, enabling subsequent steps to reuse the zeroing and attenuation criteria and support audit traceability.

[0023] Step S103, Key Point Confidence Aggregation Decision and On-Chain Consolidation, is used during the casting release decision archiving stage to perform key point confidence aggregation and threshold determination on the acceptance key point evaluation status, generating a release decision or a delayed release decision. Under the delayed release decision, a minimum supplementary sampling list is output, followed by the generation of evidence summary and on-chain consolidation. This step reads the acceptance key point evaluation status set R101, aggregates the evidence confidence according to preset aggregation rules to obtain the key point confidence, and writes it back to the key point confidence and threshold determination group of R101. For node-type key points, node-type key point threshold determination is performed to generate a casting release decision. Under the delayed release decision, a minimum supplementary sampling list is generated and written to the supplementary sampling demand group of R101. This step generates a storage digest R103. The evaluation trigger time, the confidence level of each acceptance point, and the decision to release the pouring are written into R103 as frozen fields. Optionally, the minimum supplementary sampling list can also be written into R103 as a frozen field. The digest algorithm is then executed on R103 to obtain the storage digest value, which is written into the blockchain X104 to form an on-chain storage record.

[0024] The implementation process and operational effects of the method of the present invention will be described in detail below with reference to specific embodiments. It should be understood that the embodiments are only used to illustrate the technical solution of the present invention, and not to limit it. The relevant steps, parameters, and module divisions can be appropriately adjusted without changing the essence of the invention.

[0025] In an optional embodiment, step S101 is responsible for converting the acceptance point list X101 and the evidence item set X102 into a computable structured record, and writing the point identifier group, topology category group, evidence item index group, and collection time group into the acceptance point evaluation status set R101. The processing of this step includes point record initialization and topology category determination, evidence item collection and filtering and index writing, and time attribute extraction and status set writing back.

[0026] In the initialization of key point records and the determination of topology categories, the acceptance key point list X101 is traversed, the key point identifier of each acceptance key point is read, and a corresponding record is created for the key point in R101, which is written into the key point identifier group of R101 as an index basis. At the same time, the part description information related to the acceptance key point in X101 is read. According to the preset structural topology classification rules, acceptance key points involving structural connection parts are marked as node-type key points, and acceptance key points involving non-connecting straight line segments are marked as rod-type key points. The marking results are written into the topology category group of R101 to support the subsequent application of asymmetric attenuation constraints according to topology categories. The structural topology classification rules are preset configurations and can be composed of a set of keywords or a set of topology tags in the part description information. When the part description information matches the judgment element corresponding to the structural connection part, it is judged as a node-type key point; otherwise, it is judged as a rod-type key point. This is used to unify the marking criteria of node-type key points and rod-type key points. The structural topology classification rules include at least a set of node-type judgment elements and a set of rod-type judgment elements. The judgment elements are composed of a set of keywords or a set of topology tags in the part description information. The processing procedure performs element hit judgment on the part description information and outputs the topology category identifier.

[0027] In the evidence item collection, filtering, and indexing process, for each acceptance point, a list of evidence types corresponding to that acceptance point is determined, and the evidence item set X102 is filtered and collected accordingly. The evidence type list is pre-configured by the system and maintained according to the acceptance point identifier. The processing uses the acceptance point identifier as the key to read the corresponding evidence type list for filtering and collection.

[0028] Evidence items are bound to acceptance point identifiers during collection. During aggregation, evidence items are assigned to corresponding acceptance points according to the acceptance point identifiers, and evidence items whose evidence type falls into the evidence type list are selected as the corresponding evidence items for that acceptance point. To ensure that subsequent steps have computable input, only evidence items with complete evidence type, collection time, and evidence content summary are included in the aggregation scope. Evidence items with missing fields are not included in subsequent idle time and confidence calculations. An evidence item index set is generated for the aggregated corresponding evidence items and written into the evidence item index group R101. The evidence item index is taken from the sequence number of the evidence item in the evidence item set X102 or the existing index number of the evidence item in X102.

[0029] In the time attribute extraction and state set write-back process, the collection time is read from the corresponding evidence item and written into the collection time group of R101. Time alignment is performed in subsequent steps. The key point identification group, topology category group, evidence item index group, and collection time group are written into the acceptance key point evaluation state set R101, so that subsequent steps can directly read the initialization results based on R101 and perform idle time calculation and evidence confidence update, avoiding repeated assembly of key points and evidence items at the evaluation trigger time.

[0030] In an optional embodiment, step S102 is responsible for calculating the idle time for each evidence item in R101 and updating the evidence confidence level at the evaluation trigger time. The processing of this step includes evaluation trigger determination and time alignment, idle time calculation and threshold zeroing, selection of asymmetric attenuation parameter and calculation of evidence confidence level, and result write-back and optional export.

[0031] In the evaluation trigger determination and time alignment, the evaluation trigger time is determined in response to the pouring release trigger event X103. The evaluation trigger time is taken as the occurrence time of the pouring release trigger event, which includes the pouring instruction trigger event or a preset evidence preservation and solidification trigger event. The evaluation trigger time is written into the evaluation trigger time group of R101, and the acquisition time group of R101 is read. Time alignment is performed on the evaluation trigger time and the acquisition time to ensure that they are on the same time base and at the same time granularity, so as to ensure the consistency of subsequent idle time calculation. Time alignment performs down-rounding or mapping to the granularity boundary according to the preset time granularity for both the evaluation trigger time and the acquisition time, thereby obtaining the aligned time value used for idle time calculation.

[0032] In the idle time calculation and threshold zeroing process, the idle time is calculated based on the aligned time data. The idle time is equal to the difference between the evaluation trigger time and the acquisition time, and is written into the time alignment and idle time group of R101. A minimum operation time threshold is introduced. The minimum operation time is determined by the preset operation process of concealed acceptance and is used to characterize the minimum duration required to complete evidence collection and acceptance recording. The minimum operation time threshold is a threshold parameter pre-configured by the system. For example, it can be set as a global constant or configured hierarchically according to the acceptance point identifier. The processing reads the corresponding threshold value according to the acceptance point identifier for threshold zeroing determination. When the idle time is less than the minimum operation time, the evidence confidence of the corresponding evidence item is set to zero, and the threshold zeroing mark and the minimum operation time threshold value or threshold identifier are written into the threshold zeroing and attenuation parameter group of R101.

[0033] In the selection of asymmetric attenuation parameters and the calculation of evidence confidence, for evidence entries that are not set to zero, the topology category group of R101 is read, and the attenuation coefficient is selected based on the topology category and evidence type to calculate the evidence confidence. The evidence type is carried by the evidence entry; the processing procedure locates the evidence entry set X102 to read the evidence type based on the evidence entry index of R101. The attenuation function is a monotonically non-increasing function with respect to idle time, and the evidence confidence ranges from zero to one. The attenuation coefficient is read from the preset attenuation coefficient configuration using the topology category identifier and evidence type as a joint key and written into the threshold zeroing and attenuation parameter group of R101. The preset attenuation coefficient configuration is pre-configured by the system; the joint key includes at least the topology category identifier and the evidence type, and the read result includes at least the attenuation coefficient value or the attenuation parameter identifier. The processing procedure writes the read result into the threshold zeroing and attenuation parameter group of R101 to support subsequent audit traceability.

[0034] Record the idle time after time alignment as The minimum task time threshold is denoted as The confidence level of the evidence is denoted as The attenuation coefficient is denoted as ,in Let the natural constant be the base of the exponential function. season .when At that time, according to the formula Calculate the confidence level of the evidence and write the calculated confidence level into the confidence level group of R101. To satisfy the asymmetric constraint of topological categories, for any given idle time, the decay coefficient corresponding to the node class key point is... Not less than the attenuation coefficient corresponding to the key points of the rod type This ensures that the confidence level of evidence corresponding to node-type key points is not higher than the confidence level of evidence corresponding to rod-type key points. This represents the attenuation coefficient corresponding to the node class key point. This indicates the attenuation coefficient corresponding to the key points of the rod type. This refers to the attenuation coefficient value read from the preset attenuation coefficient configuration based on the topology category and evidence type. For example, the minimum job time threshold... A 30-minute interval can be used to filter evidence collected too recently, and the attenuation coefficient can be set accordingly. and To make the evidence confidence of node-type key points decay more rapidly with idle time, the examples are only used to illustrate the parameter relationships and calculation process and do not constitute a limitation on the range of values.

[0035] In the result write-back and optional export, the evaluation trigger time group, time alignment and idle time group, threshold zeroing and decay parameter group, and evidence confidence group of R101 are updated and written back. Optionally, an evidence confidence result set R102 is generated from the evidence confidence group of R101, which is used as an aggregation input or audit export object in optional embodiments. The evidence confidence result set R102 is only used for audit export and process traceability and does not participate in the main chain calculation of S103. S103 uses the evidence confidence group of R101 as the aggregation input.

[0036] In an optional embodiment, step S103 is responsible for reading the evidence confidence group from R101 and performing confidence calculation and threshold determination for each acceptance point. Simultaneously, when a delay release decision is triggered, a minimum supplementary collection list is generated, and the evidence summary is generated and solidified on-chain. The processing steps of this step include aggregated confidence calculation of key points, threshold determination and decision generation, minimum supplementary collection list generation and deduplication, and evidence summary generation and on-chain solidification.

[0037] In the aggregation calculation of key point confidence, for each acceptance key point, the set of evidence items corresponding to that acceptance key point is located from the evidence item index group of R101, and the evidence confidence of each evidence item in that set is extracted from the evidence confidence group of R101. According to the preset aggregation rules, the evidence confidence of multiple evidence items under the same acceptance key point is aggregated into a key point confidence, and the key point confidence is written into the key point confidence and threshold judgment group of R101. Optionally, when the preset aggregation rule is a weighted aggregation rule, weight coefficients are configured according to evidence type. The evidence type is carried by the evidence item and can be located in the evidence item set X102 by the evidence item index. The processing reads the weight coefficients of each evidence item under the same acceptance key point according to its evidence type and performs normalization, so that the sum of the weight coefficients participating in the aggregation within that acceptance key point is 1. Then, the evidence confidence of each evidence item is multiplied by its normalized weight coefficient to obtain a weighted value, and the weighted values ​​of all evidence items under the same acceptance key point are summed to obtain the key point confidence of that acceptance key point. The weight coefficients are pre-configured weight parameters in the system. The processing reads the weight coefficients by evidence type and performs normalization within the same acceptance criteria range, so that the sum of the weight coefficients participating in the aggregation is 1.

[0038] In threshold determination and decision generation, the node class points in R101 are traversed, their point confidence levels are read, and compared with the node class point thresholds. The node class point thresholds are pre-configured threshold parameters in the system. The processing procedure reads the threshold values ​​according to the node class point identifier or node class point type for threshold determination. When the point confidence level of any node class point is lower than the node class point threshold, a temporary release decision is generated; otherwise, a release decision is generated, and the decision result is written to the point confidence level and threshold determination group in R101 for subsequent archiving and auditing.

[0039] In the generation and deduplication of the minimum supplementary evidence list, only when a temporary release decision is generated is the set of key points of the node class that caused the temporary release determined, and only for this set is a minimum supplementary evidence list generated to meet the minimumity criterion. For each triggering key point, the list of evidence types and weight coefficient configuration corresponding to the key point are read, and the set of candidate supplementary evidence types is identified based on the evidence confidence of each evidence item under the key point. Under the weighted aggregation rule, the evidence type with the highest weight contribution in the aggregation of the key point and the current evidence confidence is zero is selected as the supplementary evidence item, and evidence types are added in descending order of weight contribution as needed. To determine the minimumity shutdown condition, the process sorts the candidate evidence types in descending order of weight contribution, and prioritizes the evidence type with the current evidence confidence of zero. When adding the supplementary evidence candidate set in the sorted sequence, the upper bound of the key point confidence is estimated according to the preset confidence recovery caliber after supplementary evidence. When the estimated upper bound is not lower than the node class key point threshold for the first time, the addition stops and the current supplementary evidence candidate set is output. The confidence recovery caliber after supplementary sampling is a pre-configured recovery value or upper limit value, used to make a deterministic estimate of the probability of confidence recovery of key points before actual supplementary sampling. Each generated supplementary sampling entry includes at least the acceptance key point identifier and the type of evidence to be supplemented, and duplicate supplementary sampling entries for the same acceptance key point and the same type of evidence are deduplicated and written into the supplementary sampling request group of R101.

[0040] In the generation and on-chain solidification of evidence digests, an evidence digest R103 is created. The evaluation trigger time, the confidence level of each acceptance point, and the pouring release decision are extracted from R101 and written into R103 as frozen fields. Optionally, the minimum supplementary collection list can also be written into R103 as a frozen field. The digest algorithm is a repeatable digest calculation process, ensuring that the same frozen fields generate the same evidence digest value. The processing steps execute the digest algorithm on R103 to obtain the evidence digest value, and then write the evidence digest value into blockchain X104 to form an on-chain evidence record, supporting multi-party reconciliation and non-repudiation verification. Frozen fields are serialized according to a preset field order and encoding rules and used as input to the digest algorithm, ensuring that different parties generate the same evidence digest value under the same frozen field values. The blockchain type and consensus criteria affect the on-chain write interface and on-chain record format, but do not affect the calculation logic and field generation logic of this method on R101 and R103.

[0041] In one simulation embodiment, to illustrate the difference in judgment between the present invention's strategy and the traditional strategy under the scenario of casting release triggering, a comparative demonstration can be performed under the same evidence item set and the same casting release triggering time, referring to... Figure 2 and Figure 3 ,in Figure 2 Showing traditional strategies, Figure 3 The strategy of the present invention is shown. Figure 2The traditional strategy shown only puts the summary of the evidence content on the blockchain to prove that the content has not been tampered with. It assumes that the evidence item is always equivalent and valid after collection. It does not calculate the idle time based on the evaluation trigger time, nor does it set the minimum operation time threshold to zero or set the asymmetric decay constraint based on topology category. Therefore, it may still give a release conclusion when there are evidence items collected too recently, which poses a risk of wrong release. Figure 3 The strategy of this invention, as shown, responds to the pouring release trigger event, determines the evaluation trigger time, and calculates the idle time. When the idle time is less than the minimum operation time, the evidence confidence is set to zero; otherwise, based on the topology category, the idle time is input into a decay function to obtain the evidence confidence, ensuring that the evidence confidence corresponding to the node-type key point is not higher than the evidence confidence corresponding to the rod-type key point. Subsequently, the key point confidence is obtained according to a preset aggregation rule, and a node-type key point threshold judgment is performed on the node-type key points. Upon triggering, a decision to postpone release and a minimum supplementary collection list are output. Furthermore, the evaluation trigger time, the confidence of each acceptance key point, and the pouring release decision are written as frozen fields into the evidence storage digest, and the evidence storage digest summary value is generated and written to the blockchain to form an on-chain evidence storage record, used to support multi-party reconciliation and non-repudiation verification.

[0042] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0043] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0044] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0045] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0046] In conclusion, 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 spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A blockchain-based method for trusted storage of construction engineering consulting data, characterized in that: include: S101, Obtain the acceptance checklist and obtain the corresponding evidence item for each acceptance checklist, wherein the evidence item includes at least the evidence type, collection time, and evidence content summary; and mark the acceptance checklist as node-type checklist or rod-type checklist based on the structural topology. S102, respond to the pouring release trigger event to determine the evaluation trigger time, and calculate the idle time as the difference between the evaluation trigger time and the collection time; when the idle time is less than the minimum operation time, the evidence confidence of the corresponding evidence item is set to zero; otherwise, the idle time is input into the decay function according to the topology category of the acceptance key point to obtain the evidence confidence; whereby, for any same idle time, the evidence confidence of the node type key point is not higher than the evidence confidence of the rod type key point; S103, for each acceptance point, aggregate the evidence confidence level corresponding to the acceptance point according to the preset aggregation rule to obtain the point confidence level; when the point confidence level of any node-type point is lower than the node-type point threshold, generate a decision to postpone release and output the minimum supplementary collection list, otherwise generate a release decision; generate a storage summary, write the evaluation trigger time, the point confidence level of each acceptance point and the pouring release decision as frozen fields into the storage summary, and generate a storage summary summary value for the storage summary and write it into the blockchain to form an on-chain storage record.

2. The method according to claim 1, characterized in that, The evidence content digest is the evidence digest value obtained by performing a digest algorithm on the evidence content.

3. The method according to claim 2, characterized in that, Obtaining corresponding evidence items for each acceptance point includes determining the evidence type list corresponding to the acceptance point, and selecting evidence items whose evidence type falls into the evidence type list from the evidence items as the corresponding evidence items for the acceptance point.

4. The method according to claim 1, characterized in that, Based on structural topology annotations, key points are categorized as either node-type or member-type. Acceptance key points involving structural connection parts are annotated as node-type key points, while acceptance key points involving non-connecting straight segments are annotated as member-type key points.

5. The method according to claim 4, characterized in that, The structural connection parts shall include at least one of the following: beam-column joint, rebar lap joint, sleeve connection, and connection between embedded parts and the main structure. The non-connection straight segments shall include at least one of the following: straight segments of rebar in beams, slabs, or walls, and straight segments of embedded parts in non-connection areas.

6. The method according to claim 1, characterized in that, The evaluation trigger time is the time when the pouring release trigger event occurs. The pouring release trigger event includes the pouring instruction trigger event or the preset evidence preservation and solidification trigger event.

7. The method according to claim 6, characterized in that, Before calculating the idle time, the evaluation trigger time and the collection time are aligned to ensure that they are on the same time base and at the same time granularity.

8. The method according to claim 1, characterized in that, The minimum operation time is determined by the preset operation process of concealed acceptance, and is used to characterize the minimum duration required to complete evidence collection and acceptance recording.

9. The method according to claim 1, characterized in that, The decay function is a monotonically non-increasing function with respect to idle time, and the output evidence confidence falls within the preset confidence interval. The decay function parameters are selected according to the topology category and evidence type of the acceptance points, and for any identical idle time, the decay function parameter corresponding to the node type points is not less than the decay function parameter corresponding to the rod type points.

10. The method according to claim 9, characterized in that, The preset aggregation rule is a weighted aggregation rule. Weight coefficients are configured for evidence items according to evidence type, and the confidence scores of evidence corresponding to the same acceptance point are weighted and aggregated to obtain the confidence score of the point. When the confidence score of any node-type point is lower than the threshold of the node-type point, a decision to postpone release is generated and a minimum supplementary collection list is output. Otherwise, a release decision is generated. The evidence summary further includes the minimum supplementary collection list and the confidence scores of each acceptance point. The evaluation trigger time, the confidence scores of each acceptance point, and the pouring release decision are written as frozen fields into the evidence summary, and the evidence summary summary value of the evidence summary is written into the blockchain to form an on-chain evidence record.

Images