An engineering detection data blockchain notarization method and system based on dynamic weights

Abstract

The application relates to the technical field of engineering management, and particularly provides an engineering detection data blockchain storage method and system based on dynamic weights, which comprises the following steps: collecting current engineering detection data of a detection object, wherein the current engineering detection data comprises a plurality of characteristic parameters of different evaluation dimensions of the detection object in a current period; determining a credibility weight configuration of the current engineering detection data based on a fuzzy logic algorithm, and performing real-time monitoring on object parameters of the detection object, so that the credibility weight configuration is adjusted when the object parameters meet a preset change, and the current engineering detection data is adjusted and stored based on a new credibility weight configuration; wherein the credibility weight configuration is used for determining a storage processing mode corresponding to the current engineering detection data. Through multi-dimensional dynamic weight evaluation and storage strategy adaptation, the application can optimize the utilization efficiency of blockchain resources, and ensure that the data credibility evaluation is always matched with the actual quality.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of engineering management, and in particular to an engineering detection data blockchain storage method and system based on dynamic weights. BACKGROUND

[0002] In the field of engineering construction, the authenticity and reliability of detection data are crucial for engineering quality evaluation and safety decision-making. The blockchain technology provides a new solution for the storage of engineering detection data due to its characteristics of non-tamperability and traceability.

[0003] However, the existing blockchain-based storage solutions generally adopt a "one-size-fits-all" storage mode, that is, all detection data are stored on the chain in the same way. This mode has the following defects: waste of storage resources: the amount of engineering detection data is large and the types are diverse, and part of the low-value or low-credibility data occupies a large amount of on-chain storage space, resulting in high storage cost; lack of credibility evaluation: the influence of data sources (such as detection equipment accuracy and operator qualification) on data credibility is not considered, which cannot provide effective credibility reference for data users, and the data credibility may change with subsequent verification results, and the existing solutions cannot realize the synchronous improvement of the credibility management level of engineering detection data based on data changes. SUMMARY

[0004] In view of the problem that the existing solutions cannot realize the synchronous improvement of the credibility management level of engineering detection data based on data changes, the application provides an engineering detection data blockchain storage method and system based on dynamic weights, which can optimize the utilization efficiency of blockchain resources through multi-dimensional dynamic weight evaluation and storage strategy adaptation, and ensure that the data credibility evaluation always matches the actual quality.

[0005] To achieve the purpose of the application, the application provides the following technical solutions:

[0006] In a first aspect, the application provides an engineering detection data blockchain storage method based on dynamic weights, comprising:

[0007] Collecting current engineering detection data for a detection object, the current engineering detection data including a plurality of characteristic parameters of different evaluation dimensions of the detection object in a current period; the different evaluation dimensions include a detection equipment dimension, a detection personnel dimension, a detection environment dimension and a data characteristic dimension;

[0008] The credibility weight configuration of the current engineering detection data is determined based on a fuzzy logic algorithm, and object parameters of the detection object are monitored in real time, so that the credibility weight configuration is adjusted when the object parameters meet a preset variation, a new credibility weight configuration is generated, and the current engineering detection data is adjusted based on the new credibility weight configuration; wherein the credibility weight configuration is used to determine a storage processing mode corresponding to the current engineering detection data, and different credibility weight configurations correspond to different storage processing modes.

[0009] In a second aspect, the application provides an engineering detection data blockchain storage system based on dynamic weights, which is used to implement the engineering detection data blockchain storage method based on dynamic weights described above, comprising:

[0010] A data processing module is configured to collect current engineering detection data for a detection object, wherein the current engineering detection data includes a plurality of characteristic parameters of different evaluation dimensions of the detection object in a current period; the different evaluation dimensions include a detection equipment dimension, a detection personnel dimension, a detection environment dimension, and a data characteristic dimension.

[0011] A weight configuration module is configured to determine a credibility weight configuration of the current engineering detection data based on a fuzzy logic algorithm, and to monitor object parameters of the detection object in real time, so that the credibility weight configuration is adjusted when the object parameters meet a preset variation, a new credibility weight configuration is generated, and the current engineering detection data is adjusted based on the new credibility weight configuration; wherein the credibility weight configuration is used to determine a storage processing mode corresponding to the current engineering detection data, and different credibility weight configurations correspond to different storage processing modes.

[0012] Compared with the prior art, the application has the following beneficial effects:

[0013] Through quantitative evaluation of the four core dimensions of equipment, personnel, environment, and data itself, the characteristic parameters are converted into weight values by combining a fuzzy logic algorithm, thereby realizing fine classification of data credibility. Compared with the extensive mode of "data is credible" in the traditional storage mode, different sources and different quality data can be effectively distinguished, thereby providing a credible basis for engineering decision-making; the application designs differentiated storage strategies for high, medium, and low weight data, thereby significantly reducing the storage pressure and cost of the blockchain while ensuring data security, and is particularly suitable for the storage needs of massive data in the engineering detection field; at the same time, when the data weight changes across levels due to verification results, the storage strategy is automatically converted, thereby ensuring that resources on the chain are always preferentially allocated to high-value data, and avoiding resource mismatching of "low-value data occupying high-cost storage". BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings are used to provide further understanding of the present application, and form a part of the specification, together with the embodiments of the present application, to explain the present application, and do not form a limitation on the present application;

[0015] Figure 1 An optional flowchart of a dynamic weight-based engineering detection data blockchain storage method provided by an embodiment of the present application;

[0016] Figure 2 An optional flowchart of a dynamic weight-based engineering detection data blockchain storage method provided by an embodiment of the present application;

[0017] Figure 3 An optional flowchart of a dynamic weight-based engineering detection data blockchain storage method provided by an embodiment of the present application;

[0018] Figure 4 An optional flowchart of a dynamic weight-based engineering detection data blockchain storage method provided by an embodiment of the present application;

[0019] Figure 5 An optional flowchart of a dynamic weight-based engineering detection data blockchain storage method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] To make the purposes, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0021] The terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features; in the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0022] The technical solutions of the present application will be described below in conjunction with the embodiments shown in the drawings: Figures 1 to 5

[0023] Embodiment 1

[0024] A dynamic weight-based engineering detection data blockchain storage method provided by an embodiment of the present application, with reference to Figure 1 ​As shown, including the following steps S101 to S103:

[0025] Step S101, collect the current engineering detection data for the detection object, the current engineering detection data includes a plurality of characteristic parameters of different evaluation dimensions of the detection object in the current period; the different evaluation dimensions include detection equipment dimension, detection personnel dimension, detection environment dimension and data characteristic dimension.

[0026] In the embodiment of the present application, based on the analysis of the credibility influencing factors of engineering detection data, a plurality of core evaluation dimensions are divided, each dimension contains a plurality of quantifiable characteristic parameters. As a feasible implementation, the different evaluation dimensions of the embodiment include detection equipment dimension, detection personnel dimension, detection environment dimension and data characteristic dimension; wherein, the detection equipment dimension focuses on the basic influence of the detection equipment on the data credibility, containing four characteristic parameters: 1) hardware fingerprint (hardware fingerprint characteristic): device unique identifier (generated by combining chip serial number, sensor model, factory number), used to verify the legality of device identity; 2) calibration record (calibration record characteristic): the deviation of the last calibration time and the standard value (such as "calibration on 2024-06-15, deviation ±0.02mm"), reflecting the current precision state of the device; 3) historical failure rate (historical failure rate characteristic): the ratio of the number of device failures in the last 12 months to the total number of detections (such as "3 failures / 100 detections"), reflecting the stability of the device; 4) precision level (precision level characteristic): the error range of the device at the time of factory calibration (such as "±0.1%FS"), as a benchmark for the inherent performance of the device; the detection personnel dimension focuses on the influence of the detection personnel's operation specification on the data, containing four characteristic parameters: 1) qualification level (qualification level characteristic): the level of nationally certified detection personnel (such as A / B / C level, corresponding to "senior / middle / junior" qualification); 2) historical operation accuracy rate (historical operation accuracy rate characteristic): the proportion of data results consistent with third-party authoritative verification in the last 30 detections (such as "28 / 30=93.3%"); 3) training record (training record characteristic): the cumulative length of time (such as "40 hours") and examination results of professional skill training in the last year; 4) certification validity period (certification validity period characteristic): the remaining validity period of the personnel qualification certificate (such as "18 months to expiration"), ensuring the validity of the operation qualification; the detection environment dimension considers the interference of the detection site environment on data collection, containing four characteristic parameters: 1) temperature (temperature characteristic): the deviation value of the environmental temperature at the time of detection from the standard working condition (such as 25℃) (such as "+3℃"); 2) humidity (humidity characteristic): the deviation of the relative humidity at the time of detection from the standard range (such as 40%-60%) (such as "75%, 15% above the upper limit"); 3) vibration value (vibration value characteristic): the vibration acceleration (unit ​” (lower than 0.1

[0027] Here, the multi-dimensional weight data collection can be used to comprehensively and quantitatively evaluate the credibility of the engineering detection data, and provide accurate basis for subsequent hierarchical evidence storage strategies.

[0028] In step S102, the credibility weight configuration of the current engineering detection data is determined based on a fuzzy logic algorithm.

[0029] In the embodiments of the present application, the credibility weight configuration refers to the data level of the credibility of the current engineering detection data, which is divided according to different ranges of credibility weight values; the fuzzy logic algorithm refers to the mapping from the feature parameters to the comprehensive weight through three stages of fuzzification, rule reasoning and defuzzification. That is, in the embodiments of the present application, the standardized feature parameters (sub-parameter scores of the equipment, personnel, environment and data itself dimensions) are converted into comprehensive weight values (0-10) reflecting the data credibility through fuzzy logic reasoning, and dynamic adjustment based on the verification results is supported.

[0030] In the embodiments of the present application, the reference Figure 2 As shown in the figure, the content of step S102 can be realized by the content of steps S1021 to S1023 as follows:

[0031] In step S1021, each feature parameter is standardized to obtain a parameter score for each feature parameter.

[0032] In the embodiments of the present application, in order to eliminate the dimensional differences of different parameters, each feature parameter is mapped to a different score, and the higher the score, the greater the positive contribution to the data credibility.

[0033] ​As an example, each feature parameter is mapped to a score of 0-10; specifically: (1) detection equipment dimension parameter standardization: calibration record: ≤3 months for 10 points, 3-6 months for 7 points, 6-12 months for 4 points, >12 months for 1 point; historical failure rate: failure rate = 0 for 10 points, 0 < failure rate ≤ 5% for 8 points, 5% < failure rate ≤ 10% for 5 points, >10% for 2 points; accuracy level: error range ≤ ± 0.1% FS for 10 points, ± 0.1%-± 0.5% FS for 7 points, ± 0.5%-± 1% FS for 4 points, >± 1% FS for 1 point; hardware fingerprint: complete match with device registration information for 10 points, partial match (such as consistent model but inconsistent serial number) for 0 points; (2) detection personnel dimension parameter standardization: qualification level: A level for 10 points, B level for 7 points, C level for 4 points; historical operation accuracy: ≥95% for 10 points, 90%-95% for 8 points, 80%-90% for 6 points, <80% for 3 points; training record: ≥50 hours for 10 points, 30-50 hours for 7 points, 10-30 hours for 4 points, <10 hours for 1 point; certification validity period: ≥24 months for 10 points, 12-24 months for 7 points, 6-12 months for 4 points, <6 months for 1 point; (3) detection environment dimension parameter standardization: temperature deviation: ≤±2℃ for 10 points, ±2-±5℃ for 7 points, ±5-±10℃ for 4 points, >±10℃ for 1 point; humidity deviation: within the standard range (40%-60%) for 10 points, exceeds the range but ≤±10% for 7 points, ±10%-±20% for 4 points, >±20% for 1 point; vibration value: ≤0.05 for 10 points, 0.05-0.1 for 7 points, 0.1-0.2 for 4 points, >0.2 for 1 point; electromagnetic interference intensity: ≤10 mV for 10 points, 10-30 mV for 7 points, 30-50 mV for 4 points, >50 mV for 1 point; (4) data feature dimension parameter standardization: acquisition frequency: ≥20 times / second for 10 points, 10-20 times / second for 7 points, 5-10 times / second for 4 points, <5 times / second for 1 point; abnormal value proportion: =0 for 10 points, 0 < proportion ≤2% for 8 points, 2%-5% for 5 points, >5% for 2 points; integrity: missing rate =0 for 10 points, 0 < missing rate ≤5% for 7 points, 5%-10% for 4 points, >10% for 0 points; deviation degree from historical data: ≤3% for 10 points, 3%-5% for 7 points, 5%-10% for 4 points, >10% for 1 point.

[0034] Step S1022, for each of the evaluation dimensions, fuzzy processing is performed based on each of the parameter scores in the evaluation dimension, to obtain a dimension weight and a dimension fuzzy set corresponding to each of the evaluation dimensions;

[0035] In an embodiment of the present application, referring to Figure 3 as shown, the process of obtaining the dimension weight and the dimension fuzzy set corresponding to each evaluation dimension is implemented through the content of the following steps S10221 to step S10222:

[0036] Step S10221: For each of the evaluation dimensions, perform weighted summation on the multiple parameter scores to obtain the dimension weight corresponding to each evaluation dimension.

[0037] As a feasible implementation manner, the dimension weight of the detection device is the sum of the products of the parameter features of the device dimension and the corresponding dimension weights; for example, = 0.3 × Hardware fingerprint score + 0.3 × Calibration record score + 0.2 × Historical failure rate score + 0.2 × Accuracy level score. The dimension weight of the tester is the sum of the products of the parameter features of the personnel dimension and the corresponding dimension weights; for example, = 0.4 × Qualification level score + 0.3 × Historical accuracy score + 0.3 × Training record score (the certification validity period is used as a qualification threshold, not participating in the weighting but directly deducting 2 points when it is less than 6 months). The dimension weight of the detection environment is the sum of the products of the parameter features of the detection environment dimension and the corresponding dimension weights; for example, = 0.25 × Temperature score + 0.25 × Humidity score + 0.25 × Vibration value score + 0.25 × Electromagnetic interference score. The dimension weight of the data feature dimension is the sum of the products of the parameter features of the data feature dimension and the corresponding dimension weights; for example, = 0.2 × Acquisition frequency score + 0.3 × Proportion of outliers score + 0.3 × Integrity score + 0.2 × Deviation degree score.

[0038] Step S10222: Based on the dimension weight, perform membership degree calculation to determine the dimension membership score and the dimension fuzzy set corresponding to the dimension membership score.

[0039] In an embodiment of the present application, convert the precise scores (0 - 10) after standardizing each dimension into a fuzzy set (linguistic variable). Taking the three - level division of "high", "medium", and "low" as an example, define the membership degree function of each fuzzy set:

[0040] For example, in the dimension weight of the detection device ( ): Fuzzy set "high": score ∈ [8, 10], membership degree function is μ 高 (x) = (x - 8) / 2 (x is the standardized score, the same below); Fuzzy set "medium": score ∈ [4, 8], membership degree function is μ 中 (x) = (x - 4) / 4 (4 ≤ x ≤ 6) or μ 中 (x) = (8 - x) / 4 (6 < x ≤ 8); Fuzzy set "low": score ∈ [0, 4], membership degree function is μ低 (x) = (4 - x) / 4.

[0041] Here, the detection personnel dimension weight , the detection environment dimension weight , the data feature dimension weight , the same fuzzy set division and membership function as the detection equipment dimension weight are adopted, and only the standardized scores of respective dimensions are fuzzified. Exemplarily, if the detection equipment dimension weight standardized score is 7 points, it is calculated by the membership function that: μ 高 (7) = 0), μ 中 (7) = 0.25, and μ 低 (7) = 0, that is, the degree of belonging to “medium” is 25%.

[0042] Step S1023, calculating the multiple dimension fuzzy sets based on the constructed fuzzy rule base, determining the credibility weight value of the current engineering detection data and the credibility weight value matched with the credibility weight configuration.

[0043] In the embodiment of the present application, as shown in Figure 4 , the content of step S1023 can be realized by the content of steps S10231 to S10232 as follows:

[0044] Step S10231, forming a set group by the equipment dimension fuzzy set, the personnel dimension fuzzy set, the environment dimension fuzzy set and the data dimension fuzzy set of the current engineering detection data, and comparing the set group with multiple rules in the fuzzy rule base one by one to determine the triggered rule information matched with the current engineering detection data;

[0045] In the embodiment of the present application, the triggered rule information refers to the number of triggered rules, the membership degree of the triggered rules (calculated by the matching degree of the fuzzification result and the rule antecedent), and the center of the conclusion corresponding to the triggered rules. Here, the rule conclusion has five kinds, in which “extremely high” corresponds to 9.5, “high” corresponds to 8, “medium” corresponds to 5, “low” corresponds to 3, and “extremely low” corresponds to 1.5. The fuzzy rule base of the present embodiment is an experience rule defined according to the implementation characteristics of the engineering management field, which is used to capture the non-linear interaction between dimensions to realize the fine classification of data credibility. Here, the fuzzy rule base design is based on the expert experience in the field of engineering detection, and the fuzzy reasoning rule base is constructed. Exemplarily, it can contain 27 core rules (3 fuzzy sets x 3 dimension combinations), which are as follows:

[0046] Rule 1: if is “high” and is “high” and is “high” and is “high”, then the comprehensive weight W is "very high" (9-10);

[0047] Rule 2: If is "high" and is "medium" and is "high" and is "medium", then the overall weight is "high" (7-9);

[0048] Rule 3: If is "low" or is "low" or is "low", then the overall weight is "medium low" (3-5);

[0049] Rule 4: If is "low" and is "low", then the overall weight is "very low" (0-3);

[0050] Rule 5: If is "high" and is "high" and is "high" and is "medium", then the overall weight is "high" (7-9);

[0051] Rule 6: If is "high" and is "high" and is "high" and is "low", then the overall weight is "medium high" (6-7);

[0052] Rule 7: If is "high" and is "high" and is "medium" and is "high", then the overall weight is "high" (7-9);

[0053] Rule 8: If is "high" and is "high" and is "medium" and is "medium", then the overall weight is "medium high" (6-7);

[0054] Rule 9: If is "high" and is "high" and is "medium" and is "low", then the overall weight is "medium" (5 - 6);

[0055] Rule 10: If is "high" and is "high" and is "low" and is "high", then the comprehensive weight is "medium high" (6 - 7);

[0056] Rule 11: If is "high" and is "high" and is "low" and is "medium", then the comprehensive weight is "medium" (5 - 6);

[0057] Rule 12: If is "high" and is "high" and is "low" and is "low", then the comprehensive weight is "medium low" (3 - 5);

[0058] Rule 13: If is "high" and is "medium" and is "high" and is "high", then the comprehensive weight is "high" (7 - 9);

[0059] Rule 14: If is "high" and is "medium" and ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ is "high" and is "medium" and is "low", then the overall weight is "medium-low" (3-5);

[0063] Rule 18: If is "high" and is "medium" and is "low" and is "high", then the overall weight is "medium" (5-6);

[0064] Rule 19: If is "high" and is "medium" and is "low" and is "medium", then the overall weight is "medium-low" (3-5);

[0065] Rule 20: If is "high" and is "medium" and is "low" and is "low", then the overall weight is "low" (1-3);

[0066] Rule 21 : If is "high" and is "low" and is "high" and is "high", then the overall weight is "medium" (5-6);

[0067] Rule 22: If is "high" and is "low" and is "high" and is "medium", then the overall weight is "medium-low" (3-5);

[0068] Rule 23: If is "high" and is "low" and is "high" and is "low", then the overall weight is "low" (1-3);

[0069] Rule 24: If is "high" and is "low" and is "medium" and is "high", then the overall weight “low” (1-3) if

[0070] Rule 26: if is “high” and is “low” and is “low” and is “low”, then the comprehensive weight is “low” (1-3).

[0071] Rule 26: if is “high” and is “low” and is “low” and is “high”, then the comprehensive weight is “low” (1-3).

[0072] Rule 27: if is “low” and is “low” and is “low” and is “low”, then the comprehensive weight is “extremely low” (0-3).

[0073] In the embodiments of the present application, the rule base supports dynamic expansion. When a new engineering scene (such as underground engineering detection) is added, a targeted rule (such as “if the environmental vibration value is ‘high’, then the comprehensive weight is reduced by 1 level”) can be added through an on-chain voting mechanism. In this way, the system can continue to evolve with the development of the industry technology, and avoid the problem of outdated functions caused by scene solidification.

[0074] As a feasible implementation manner, based on the above dimension weight, the standardized dimension parameters can be fused into a comprehensive weight by using the following formula , and then the range in which the value of the comprehensive weight is located is divided into levels.

[0075] W = a W device + b W person + g W env + d W data

[0076] wherein a, b, g, d are dimension weight coefficients (a+b+g+d=1), which can be dynamically adjusted according to the engineering type: in laboratory detection data (such as material strength test), a=0.4, b=0.3, g=0.1, d=0.2, wherein the equipment and personnel have a greater impact; in the field monitoring data (such as bridge displacement), due to the greater influence of environmental interference, a=0.3, b=0.2, g=0.3, d=0.2.

[0077] Step S10232, the barycentric method is used to calculate the trigger rule information to obtain the credibility weight value.

[0078] In the embodiments of the present application, the fuzzy conclusion (such as "high", "medium", etc.) obtained based on fuzzy rule reasoning is converted into an accurate weight value of 0-10, which is realized by using the barycentric method, and the formula is as follows:

[0079]

[0080] wherein, is the accurate weight value, i.e. the credibility weight value; n is the number of triggered rules; here the number of triggered rules n depends on the matching of the dimension sub-weights and the fuzzy set; when the sub-weights belong to multiple fuzzy sets at the same time, for example, the device dimension weight score 7.0 belongs to medium and high, and multiple rules may be triggered, and the membership degree takes the minimum value of the membership degrees of the antecedents. i is the membership degree of the i th rule, which is calculated from the fuzzy result and the rule antecedent matching degree; x i is the center value of the conclusion corresponding to the i th rule.

[0081] For example, if a certain detection data triggers 3 rules: rule 1 (μ1=0.8): conclusion "high" (x1=8); rule 2 (μ1=0.5): conclusion "medium" (x1=5); rule 3 (μ1=0.2): conclusion "medium-high" (x3=6.5);

[0082] Then the credibility weight value (i.e. the comprehensive weight) W=(0.8×8+0.5×5+0.2×6.5) / (0.8+0.5+0.2)=(6.4+2.5+1.3) / 1.5=10.2 / 1.5=6.8.

[0083] In this way, the "one-size-fits-all" disadvantage is eliminated through multi-dimensional evaluation: through the quantitative evaluation of the four core dimensions of device, personnel, environment and data itself, the feature parameters are converted into a weight value of 0-10 by combining the fuzzy logic algorithm, and the fine classification of data credibility is realized. Compared with the extensive mode of "data is credible" in the traditional evidence storage mode, it can effectively distinguish data of different sources and different qualities (such as data collected by experienced testers with high-precision equipment in standard environment has a significantly higher weight than data collected by novices with uncalibrated equipment), and provides a credible basis for engineering decision-making.

[0084] Step S103: Monitor the object parameters of the detected object in real time, and adjust the credibility weight configuration when the object parameters meet the preset change, generate a new credibility weight configuration, and adjust the evidence storage of the current engineering detection data based on the new credibility weight configuration; wherein, the credibility weight configuration is used to determine the evidence storage processing method corresponding to the current engineering detection data, and different credibility weight configurations correspond to different evidence storage processing methods.

[0085] In this embodiment of the application, when the detection object generates new detection data within a preset period, or when a third-party organization provides verification data for the current engineering detection data, or when the current engineering detection data is applied to engineering decisions and deviations occur, the initial credibility weight configuration is adjusted.

[0086] For example, a weight adjustment is triggered when any of the following conditions are met: new test data is generated for the same test object within 30 days (e.g., a second displacement monitoring of the same bridge section); a third-party institution conducts authoritative verification of the data (e.g., laboratory retesting confirms material strength data); or the data is used for engineering decisions and deviations occur (e.g., structural assessments based on the data do not match actual conditions). For instance, if a batch of concrete strength data initially has a weight of 7.2, and after three third-party verifications reveal a deviation rate of 12%, the weight is automatically reduced to 7.13, ensuring that the data reliability assessment always matches the actual quality and avoiding the lag of static assessments. Thus, the dynamic weight adjustment mechanism based on verification data allows the weight values ​​to be continuously optimized based on feedback from actual applications.

[0087] In the embodiments of this application, reference is made to Figure 5 As shown, the adjustment of the initial credibility weight configuration can be achieved through steps S1031 to S1033:

[0088] Step S1031: Based on the application scenario corresponding to the current engineering inspection data, determine the dynamic factor value of the scenario importance; for example, θ=1.2 for key bridge structure data and θ=0.8 for ordinary wall inspection data;

[0089] Step S1032: Compare the deviation value between the current engineering test data and the new monitoring data or the verification data, and determine the consistency coefficient corresponding to the current engineering test data based on the deviation value;

[0090] For example, when the deviation between the verification data and the original data is ≤5%, △=+0.1, that is, adjust upward; when the deviation is 5%-10%, △=0, do not adjust; when the deviation is >10%, △=-0.2, that is, adjust downward.

[0091] Step S1033, superimpose an adjustment amplitude on the initial credibility weight value to obtain the new credibility weight value; wherein the adjustment amplitude is the product of the initial credibility weight value, the dynamic factor value, and the consistency coefficient.

[0092] In the embodiments of the present application, each weight adjustment generates an unforgeable record containing: adjustment timestamp, trigger condition (such as third-party verification); original weight, adjusted weight, adjustment amplitude; verification data digest (hash value) and verification agency signature. These records are stored in the weight adjustment log contract of the blockchain, supporting full-link traceability, ensuring the transparency and auditability of weight changes.

[0093] In this way, through the above process, dynamic weight calculation not only realizes the quantitative evaluation of the credibility of engineering detection data, but also adapts the uncertainty of complex scenarios through fuzzy logic, and ensures the dynamic optimization of weight with actual verification results through the feedback adjustment mechanism, providing accurate and flexible decision-making basis for subsequent hierarchical evidence storage.

[0094] In the embodiments of the present application, according to the credibility weight values determined in steps S102 and S103, the engineering detection data is divided into three levels: high weight (such as W>8), medium weight (such as 5

[0095] Specifically, for the current engineering detection data determined to belong to the high weight configuration, the original detection data is directly chained to the blockchain network; for the current engineering detection data determined to belong to the medium weight configuration, the original detection data is encrypted and stored offline, and only the additional metadata of the current engineering detection data is chained to the blockchain network; the additional metadata includes but is not limited to the credibility weight value corresponding to the credibility weight configuration, the device hardware fingerprint, the detection personnel ID, the environmental parameter snapshot, and the data hash value; for the current engineering detection data determined to belong to the low weight configuration, the original detection data is encrypted and stored locally, and the additional metadata is aggregated and chained to the blockchain network.

[0096] Among them, for the current engineering test data determined to belong to the high weight configuration, the original test data (such as structural stress curve, material strength value) is standardized in format, including data acquisition time, test position, value sequence and other core fields; Additional metadata: including dynamic weight value W, device hardware fingerprint, personnel ID, environmental parameter snapshot, data hash value (calculated by SHA-256 algorithm).

[0097] By calling the "high weight storage function" of the smart contract, the preprocessed original data and metadata are packaged into a storage transaction and sent to the blockchain network; The blockchain node verifies the legality of the transaction (such as verifying whether the device fingerprint has been registered on the chain, whether the personnel ID has the operation permission), and writes it into the block after verification, generating a unique storage address (composed of block height + transaction index). At the same time, through the built-in access permission list of the smart contract, only authorized roles such as engineering supervision, project leader and the like are allowed to view the original data. The storage address is bound to the data hash, which supports quick query of block information through the hash value, and tracing of the collection and storage of data.

[0098] For the current engineering test data determined to belong to the medium weight configuration, the "hash on chain + original data distributed storage" strategy is adopted, and the specific steps are as follows:

[0099] 1) Use symmetric encryption algorithm (such as AES-256) to encrypt the original data, and the key is generated by mixing the detection personnel private key and the device hardware fingerprint (to ensure that only authorized parties can decrypt). The encrypted original data is stored in a distributed file system (such as InterPlanetary File System, IPFS), and a unique file hash (CID) is obtained as a storage index.

[0100] 2) Build a "medium weight storage data packet", including: data hash (SHA-256 value of the original data), dynamic weight value W, metadata (device, personnel, environmental information), IPFS file hash (CID). Call the "medium weight storage function" of the smart contract, and chain the data packet. The blockchain only records the above core information, and does not store the original data.

[0101] Here, when verifying data integrity is needed, the user downloads the encrypted original data from IPFS, decrypts it, calculates the hash value, and compares it with the "data hash" stored on the chain. If they are consistent, it proves that the data has not been tampered with. The metadata is bound to the IPFS index, which supports tracing the storage location and credibility of the original data through the on-chain information.

[0102] For the current engineering test data determined to belong to the low weight configuration, the "Merkle tree aggregation on chain + local encryption storage" strategy is adopted, and the specific steps are as follows:

[0103] 1) Group low-weight data by detection scenario (e.g. same bridge section, same time period), each group contains 10-20 pieces of data (quantity can be dynamically adjusted). Take the hash value of each piece of data as the leaf node, calculate the parent node hash layer by layer, and finally generate the root hash (representing the overall fingerprint of the group data) to build a Merkle tree for each group of data.

[0104] 2) Build a "low-weight evidence data package" containing: Merkle tree root hash, weight list of data in the group (W value of each piece of data), grouping metadata (description of detection scenario, total amount of data), local storage path digest (e.g. hash value of "server A - folder B"); call "low-weight evidence function" through smart contract, chain up the data package, and only record aggregated information on the chain to significantly reduce on-chain storage.

[0105] Similarly, the original data in the group is stored in the local server after encryption (the encryption key is generated by the project group public key), and the storage path digest is bound to the on-chain information; when verifying, the single data needs to be read from the local server, the hash value is calculated and verified whether it belongs to the leaf node of the Merkle tree (derived by the on-chain root hash), to confirm the validity of the data.

[0106] This strategy not only guarantees data security, but also significantly reduces the storage pressure and cost of the blockchain, especially suitable for the evidence storage needs of massive data in the engineering detection field.

[0107] In the embodiments of the present application, when the data weight changes due to subsequent verification results, the evidence storage strategy is dynamically adjusted: the smart contract listens to the credibility weight value and level configuration output in step S102 in real time, and automatically triggers the adjustment process when the weight of a piece of data crosses the level threshold, such as from W=6 to W=8.5; if the weight is upgraded from low / medium level to high level: the original data needs to be supplemented on the chain, the smart contract calls the "data upgrade function", replaces the original evidence information (such as Merkle tree leaf node, IPFS index) with the original data and new metadata, and marks the adjustment record; if the weight is downgraded from high / medium level to low level: the original data needs to be removed from the chain (through the "historical data archiving" mechanism of the blockchain), converted to aggregated evidence mode, and only the root hash and adjustment record are retained. Here, an evidence storage strategy change log is generated each time, containing the weight value before and after the adjustment, the evidence storage mode, and the trigger reason, and the log hash value is written to the blockchain to ensure that the adjustment process is traceable. In this way, when the data weight changes across levels due to verification results (such as low-weight data upgraded to high-weight), the evidence storage strategy is automatically converted (supplementing the original data on the chain), ensuring that high-value data is always given priority in resource allocation on the chain, and avoiding resource mismatch such as "low-value data occupying high-cost storage".

[0108] It should be noted that the smart contract core function module provided in the embodiment is as follows:

[0109] 1) Storage logic module: containing high / medium / low weight data storage function, defining input parameter format (such as metadata field, hash value type) and on-chain storage structure; 2) Permission management module: through role control to limit the writing and query permission of storage data, high weight data is only allowed to be modified by authorized roles; 3) Adjustment trigger module: listen to weight change events, automatically call storage strategy conversion function when weight crosses threshold; 4) Query interface module: provide query functions according to data hash, weight range, detection scene and other dimensions, support users to quickly obtain storage information and verification basis.

[0110] Through hierarchical storage processing, the security and traceability of high credibility data can be ensured, and the lightweight storage optimization of medium and low weight data can be used to optimize the utilization of blockchain resources, solving the resource waste problem of traditional "one size fits all" mode. At the same time, the cross-level dynamic adjustment mechanism makes the storage strategy always match the data credibility, further improving the flexibility and accuracy of engineering detection data management.

[0111] In the embodiment of the application, verification data for the detection object is collected; the original detection data and the verification data are associated based on the identity of the detection object and the timestamp; in the case that the verification data and the original detection data are validly associated, it is judged whether the weight adjustment is triggered based on the preset adjustment trigger condition;

[0112] Specifically, the input includes: original detection data and verification data set, the verification data set refers to the subsequent verification results related to the original detection data, such as secondary detection data of the same detection point, laboratory reinspection report, and original data such as equipment model, detection time, environmental parameters, etc., which are used to associate the verification data. In order to ensure the reliability of the adjustment basis, the system needs to collect and associate the verification data, and the specific process is as follows:

[0113] Internal cross-validation: the detection data of the same object by different detection teams and different equipment in the same project (such as two independent detections of "bridge A 3-span displacement");

[0114] Third-party authoritative verification: reinspection report issued by a qualified third-party institution (such as laboratory retest results of material strength);

[0115] Engineering practice feedback: actual effect of data application in engineering decision (such as whether the structure reinforcement scheme based on certain stress data is effective).

[0116] The original data and the verification data are associated by "detection object unique identifier + timestamp" (e.g., the data corresponding to "Bridge A-3rd span-20240701" is bound with the verification data); if the detection object and the parameter type of the verification data and the original data are consistent (e.g., both are "concrete compressive strength"), and the time interval is less than or equal to 90 days (to avoid deviation caused by environmental changes), the data is determined to be validly associated.

[0117] When any of the following conditions is met, automatic weight adjustment is triggered:

[0118] Condition 1: The number of valid associated verification data is greater than or equal to 3 (to ensure statistical significance); Condition 2: The deviation rate of a single verification data from the original data is greater than 15% (deviation rate = |verification value - original data value| / original data value x 100%); Condition 3: There is "decision failure" in engineering practice feedback data (e.g., the evaluation based on the data does not match the actual structure state).

[0119] In the embodiments of the present application, based on the deviation characteristics of the verification data and the original data, a three-level adjustment algorithm is designed, and the formula is as follows:

[0120] W new = W old + λ·k· (V- θ)

[0121] wherein, is the adjusted confidence weight value, needs to satisfy ; is the unadjusted confidence weight value, is a deviation coefficient, which is determined according to the average deviation rate of the verification data and the original data, for example, when the deviation rate is less than or equal to 5%, λ = +0.1 (upward adjustment), when the deviation rate is between 5% and 10%, λ = 0 (no adjustment), and when the deviation rate is greater than 10%, λ = -0.2 (downward adjustment); is a preset influence factor determined according to different scenarios, which is used to feedback the confidence of the verification data, for example, third-party authoritative verification k = 1.2, internal cross-validation k = 0.8, and engineering practice feedback k = 1.0; is a consistency score of the verification data and the original detection data, which is calculated by the deviation degree between the verification data, and the value is 0-1; is a baseline threshold, which is compared with the consistency score to determine, and the default value is 0.5, when V > θ, the adjustment amplitude is enhanced, and when V ≤ θ, the adjustment amplitude is weakened.

[0122] Here, the constraint condition is that the adjusted weight needs to satisfy 0 ≤ Wnew ≤ 10, if the calculation result exceeds the range, the boundary value is taken (e.g., when Wnew < 0, it is forced to be 0).

[0123] Here, after each weight adjustment, an unforgeable adjustment record is generated, containing: the weight values Wold, Wnew before and after adjustment and the adjustment amplitude; the hash value of the verification data (to ensure that the verification basis is traceable); the values and calculation process of the adjustment algorithm parameters λ, k, V; the timestamp and adjustment node signature (to record the adjustment executor); the adjustment record is written into the blockchain through the smart contract, associated with the storage address of the original data, supporting the tracing of the whole process of weight change through on-chain query, ensuring the transparency and auditability of the adjustment process.

[0124] In addition, the access permission control (ACL) of high-weight data in the application ensures that only authorized roles (such as supervisors) can view the original data; at the same time, the encryption storage (AES-256) of medium and low-weight data and the hash verification mechanism prevent data from being tampered with during transmission or storage; the unforgeable nature of the on-chain storage record (including data hash, weight, and adjustment log) provides trusted traceability for the entire life cycle of data. The weight adjustment record, storage strategy change log, and query access record are all stored on the chain, supporting the tracing of the collection, storage, and adjustment of data through the storage address, ensuring that any operation can be audited, effectively preventing data falsification and abuse, and clearly defining responsibility.

[0125] The engineering detection data blockchain storage method and system based on dynamic weight proposed in the application, through the organic combination of multi-dimensional dynamic weight evaluation, hierarchical storage strategy, and self-adaptive adjustment mechanism, not only solves the pain points such as "fuzzy credibility", "high storage cost", and "poor scene adaptability" in traditional storage, but also provides precise data support for engineering quality control and safety decision-making, and has significant technical value and engineering application prospect.

[0126] The application embodiment further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the method in any of the embodiments of the application. Specifically, a system or device equipped with a storage medium can be provided, and the storage medium stores software program code for implementing the functions of any of the above embodiments, and the computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0127] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program in accordance with embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable storage medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication section, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-described functions defined in the system of the present application are performed.

[0128] It should be noted that the computer readable storage medium shown in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a carrier wave in a propagated data signal, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium can also be any computer readable storage medium that can send, propagate or transfer a program for use by or in connection with an instruction execution system, device or apparatus. The program code contained on the computer readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire line, optical cable, RF, or any suitable combination of the above.

[0129] The computer program product of the present application can be a computer program embodied on a non-transitory computer readable medium. The body of computer program instructions can be a source file, object file, executable file, or any other file that can be executed on a computer. The computer program product can be implemented on a computer or network of computers. The computer program product can be implemented on a computer or network of computers.

[0130] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can be located in a single processor or distributed over multiple processors. The name of the units in some cases does not limit the unit itself.

[0131] It should be noted that although several modules or units of the device for action execution are mentioned in the foregoing detailed description, such a division is not mandatory. Indeed, according to an embodiment of the application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units.

[0132] In several embodiments provided in the present application, it should be understood that the disclosed system, modules and methods can be implemented in other ways. For example, the above-described module embodiments are only schematic. For example, the division of the units is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, and can be electrical, mechanical or other forms.

[0133] The above examples are only used to illustrate the technical solutions of the present application, but not to limit them. The present application is not limited to the exact structure as has been described above and shown in the drawings, and the specific implementation of the present application should not be considered as limited to these descriptions. Any changes and modifications made by those skilled in the art without departing from the concept of the present application should be considered as falling within the scope of the present application.

Claims

1. A dynamic weight-based engineering detection data block chain notarization method, characterized in that, The method comprises the steps of: collecting current engineering detection data of a detection object, wherein the current engineering detection data comprises a plurality of characteristic parameters of different evaluation dimensions of the detection object in a current period; the different evaluation dimensions comprise a detection equipment dimension, a detection personnel dimension, a detection environment dimension, and a data characteristic dimension; determining a credibility weight configuration of the current engineering detection data based on a fuzzy logic algorithm, and monitoring an object parameter of the detection object in real time, so as to adjust the credibility weight configuration when the object parameter meets a preset variation, generate a new credibility weight configuration, and adjust the current engineering detection data based on the new credibility weight configuration; the step of determining the credibility weight configuration of the current engineering detection data based on the fuzzy logic algorithm comprises: standardizing each characteristic parameter to obtain a parameter score of each characteristic parameter; for each evaluation dimension, performing fuzzy processing based on each parameter score in the evaluation dimension to obtain a dimension weight and a dimension fuzzy set corresponding to each evaluation dimension; calculating a plurality of dimension fuzzy sets based on a constructed fuzzy rule base to determine a credibility weight value of the current engineering detection data and the credibility weight configuration matched with the credibility weight value; the step of, for each evaluation dimension, performing fuzzy processing based on each parameter score in the evaluation dimension to obtain a dimension weight and a dimension fuzzy set corresponding to each evaluation dimension, comprises: for each evaluation dimension, performing weighted summation on a plurality of parameter scores to obtain a dimension weight corresponding to each evaluation dimension; and performing membership calculation based on the dimension weight to determine a dimension membership score and a dimension fuzzy set corresponding to the dimension membership score; the step of calculating a plurality of dimension fuzzy sets based on the constructed fuzzy rule base to determine the credibility weight value of the current engineering detection data, comprises: comparing a set group formed by a device dimension fuzzy set, a personnel dimension fuzzy set, an environment dimension fuzzy set, and a data dimension fuzzy set of the current engineering detection data with a plurality of rules in the fuzzy rule base one by one to determine trigger rule information matched with the current engineering detection data; the trigger rule information comprises a trigger rule number, a membership degree of a target trigger rule, and a preset center value of the target trigger rule; and calculating the trigger rule information by using a barycenter method to obtain the credibility weight value; wherein the credibility weight configuration is used to determine a notarization processing mode corresponding to the current engineering detection data, and different credibility weight configurations correspond to different notarization processing modes; the credibility weight configuration comprises a high weight configuration, a medium weight configuration, and a low weight configuration, and the current engineering detection data comprises original detection data and additional metadata; the different credibility weight configurations correspond to different notarization processing modes, comprising: for the current engineering detection data determined to belong to the high weight configuration, directly chaining the original detection data to a blockchain network; For the current engineering detection data determined to belong to the medium weight configuration, the original detection data is stored offline after encryption, and only the additional metadata of the current engineering detection data is chained to the blockchain network; the additional metadata includes the credibility weight value corresponding to the credibility weight configuration, the device hardware fingerprint, the detection personnel ID, the environmental parameter snapshot, and the data hash value; For the current engineering detection data determined to belong to the low weight configuration, the original detection data is stored locally after encryption, and the additional metadata is aggregated and chained to the blockchain network.

2. The method of claim 1, wherein, The multiple characteristic parameters of the detection object in different evaluation dimensions in the current period include: a hardware fingerprint characteristic corresponding to the detection device dimension, a calibration record characteristic, a historical failure rate characteristic, and an accuracy level characteristic, a qualification level characteristic corresponding to the detection personnel dimension, a historical operation accuracy rate characteristic, a training record characteristic, and an authentication validity period characteristic, a temperature characteristic corresponding to the detection environment dimension, a humidity characteristic, a vibration value characteristic, and an electromagnetic interference intensity characteristic, and a collection frequency characteristic corresponding to the data characteristic dimension, an abnormal value proportion characteristic, an integrity characteristic, and a historical data deviation degree characteristic.

3. The method of claim 2, wherein, Before the credibility weight configuration of the current engineering detection data is determined based on the constructed fuzzy rule base, the method further includes: fusing each characteristic parameter after standardization processing into a comprehensive weight.

4. The method of claim 2, wherein, The object parameters include whether the detection object generates new detection data in a preset period, whether verification data of the current engineering detection data from a third party institution appears, and whether the current engineering detection data is applied to engineering decision and deviation appears; The object parameters of the detection object are monitored in real time to adjust the credibility weight configuration when the object parameters meet the preset variation, and a new credibility weight configuration is generated, including: In the case that the detection object generates new detection data in a preset period, or verification data of the current engineering detection data from a third party institution appears, or the current engineering detection data is applied to engineering decision and deviation appears, the initial credibility weight configuration is adjusted; The adjustment of the initial credibility weight configuration includes: determining a dynamic factor value of the scene importance based on the application scene corresponding to the current engineering detection data; comparing the deviation value between the current engineering detection data and the new detection data or the verification data, and determining a consistency coefficient corresponding to the current engineering detection data based on the deviation value; superimposing an adjustment amplitude value on the initial credibility weight value to obtain the new credibility weight value; wherein the adjustment amplitude value is the product of the initial credibility weight value, the dynamic factor value, and the consistency coefficient.

5. The method of claim 1, wherein, The current engineering detection data determined to belong to the high weight configuration is directly chained to the blockchain network, including: standard preprocessing of the original detection data; The additional metadata and the original detection data after standard preprocessing are packaged and sent to the blockchain network by calling a high-weight storage function of a smart contract, wherein the smart contract contains an access permission list; The original detection data is encrypted and stored offline, and only the additional metadata of the current engineering detection data is chained to the blockchain network, including: The original detection data is encrypted using a symmetric encryption algorithm, and the encrypted original detection data is stored in a distributed file system, and a unique file hash is obtained as a storage index; A medium-weight storage data packet is constructed, and the medium-weight storage data packet is chained to the blockchain network by calling a medium-weight storage function of the smart contract; the medium-weight storage data packet includes a data hash value, a trustworthiness weight value corresponding to the trustworthiness weight configuration, a device hardware fingerprint, a detection personnel ID, an environment parameter snapshot, and a file hash of the distributed file system; The original detection data is encrypted and stored locally, and the additional metadata is aggregated and chained to the blockchain network, including: The original detection data is grouped based on the detection scene, and the hash value of each data included in each group is taken as a leaf node, the parent node hash is calculated layer by layer, and the root hash is generated to construct a Merkle tree for each group of data; A low-weight storage data packet is constructed, and the low-weight storage data packet is chained to the blockchain network by calling a low-weight storage function of the smart contract; the low-weight storage data packet includes a Merkle tree root hash, a weight list of data in a group, grouping metadata, and a local storage path digest.

6. The engineering detection data blockchain notarization method based on dynamic weight according to claim 5, characterized in that, In the case of verifying the integrity of the original detection data belonging to the medium-weight configuration, the hash value of the original detection data is calculated and compared with the data hash value in the medium-weight storage data packet, and when the comparison result is consistent, it indicates that the original detection data has not been tampered with; In the case of verifying the integrity of the original detection data belonging to the low-weight configuration, a single data is read locally, the hash value of the single data is calculated and verified whether it belongs to the leaf node of the Merkle tree to confirm the validity of the data.

7. The method of claim 6, wherein, The method further includes: Collecting verification data for the detection object; Associating the original detection data and the verification data based on the identity of the detection object and the timestamp; In the case of judging that the verification data and the original detection data are valid association, judging whether to trigger weight adjustment based on a preset adjustment trigger condition; In the case of triggering weight adjustment, adjusting the trustworthiness weight value based on the deviation of the verification data and the original detection data using the following formula: ； wherein, is an adjusted credibility weight value, needs to satisfy ; is a credibility weight value before adjustment, is a bias coefficient, determined according to an average deviation rate of the verification data and the original data; is a preset influence factor determined according to different scenarios, used for feeding back the credibility of the verification data; is a consistency score of the verification data and the original detection data; is a reference threshold value, used for comparing with the consistency score to determine an adjustment range.

8. A dynamic weight-based engineering inspection data block chain storage system for implementing the dynamic weight-based engineering inspection data block chain storage method of any one of claims 1-7, characterized in that, including: A data processing module is configured to collect current engineering detection data of a detection object, the current engineering detection data including a plurality of characteristic parameters of different evaluation dimensions of the detection object in a current period; the different evaluation dimensions include a detection equipment dimension, a detection personnel dimension, a detection environment dimension, and a data characteristic dimension; A weight configuration module is configured to determine a credibility weight configuration of the current engineering detection data based on a fuzzy logic algorithm, and to monitor an object parameter of the detection object in real time, so as to adjust the credibility weight configuration in a case where the object parameter meets a preset variation, generate a new credibility weight configuration, and adjust the current engineering detection data based on the new credibility weight configuration; wherein the credibility weight configuration is used to determine a notarization processing mode corresponding to the current engineering detection data, and different credibility weight configurations correspond to different notarization processing modes.

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