Remote intelligent measurement calibration certificate automatic calculation method

Abstract

The invention relates to the technical field of metering detection, and discloses a remote intelligent metering calibration certificate automatic calculation method. According to the method, the real transmission of data is guaranteed by constructing a hardware encryption tunnel, the compensation weight matrix is generated by dynamically fusing the environment gradient and the equipment aging factor, and the real-time performance and accuracy of uncertainty evaluation are improved; based on a Monte Carlo re-evaluation mechanism, the calculation robustness in a high-interference scene is enhanced, and a multi-scene compliance demand is dynamically adapted through a topological certificate structure; the block chain anchoring root hash and the JSON-LD digital signature are utilized to ensure that the certificate is not tampered and can be traced by judicial traceability, meanwhile, the interactive visual design supports calculation process backtracking verification, and finally, full-chain automatic closed-loop processing of calibration data from collection and evaluation to certificate output is realized. The problems of environment interference quantization lag, equipment state compensation deficiency and weak legal effect are solved.

Description

Technical Field

[0001] This invention relates to the field of metrology and testing technology, specifically to a method for automatically calculating remote intelligent metrology calibration certificates. Background Technology

[0002] Metrological calibration is a core technical activity that ensures the accuracy and reliability of measuring equipment, product quality, production safety, and fair trade. The calibration certificate, as the final outcome and authoritative proof of the calibration process, records in detail the information of the calibrated equipment, the standards upon which calibration was based, the standard instruments used, the calibration environmental conditions, calibration point data, uncertainty assessment of the measurement results, and the final declaration of conformity.

[0003] After completing data acquisition on-site or in the laboratory, calibration technicians need to manually enter the raw data (such as instrument readings and standard output values) into the computer system (usually spreadsheets or specific software). Subsequently, technicians must manually perform tedious data processing, calculate correction values, calculate indication errors, and assess measurement uncertainty, based on complex mathematical models and metrological principles (such as least squares fitting and the uncertainty propagation law).

[0004] Such technologies suffer from systemic shortcomings: manual operation is inefficient and prone to calculation errors, especially during multi-point calibration; the data processing and certificate generation processes are disconnected, leading to extended delivery cycles and increased transcription risks; the lack of deep coupling between environmental dynamics, real-time equipment status, and evaluation models restricts accuracy and reliability in high-interference scenarios; in remote calibration scenarios, they cannot respond to real-time data streams, and manual processes cannot meet the stringent audit requirements of ISO 17025 for full-chain traceability. To address these shortcomings, a method for automatically calculating remote intelligent metrology calibration certificates is proposed. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for automatically calculating remote intelligent metrology calibration certificates, thereby resolving the problems described in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for automatically calculating remote intelligent metrology calibration certificates, comprising the following steps:

[0007] Step 1, Establishing a secure data flow:

[0008] A hardware-encrypted tunnel is constructed between the metrology standard and the edge node to transmit the original calibration data packet P = {p1, p2, ..., p} in real time. n}, where each data packet p i All include a timestamp t i and environmental parameter vector E i =(T i H i), where T i is temperature, H i is humidity;

[0009] Step 2, dynamic weight generation:

[0010] Based on the environmental gradient change vector (rate of temperature change, rate of humidity change) and the metrological standard device aging function f(τ) = e -λτ (τ is the usage time, λ is the attenuation coefficient), the real-time compensation weight matrix is generated:

[0011]

[0012] where α, β are industry correction factors, ‖·‖ is the Euclidean norm;

[0013] Step 3, uncertainty adaptive calculation:

[0014] Inject the weight matrix W into the uncertainty evaluation model:

[0015] Principal component calculation: (σ is the standard deviation of the data packet P);

[0016] Environmental component calculation: u B =k·∥W∥ F (k is the inclusion factor, ∥·∥ F is the matrix Frobenius norm);

[0017] Synthetic uncertainty:

[0018] When (γ is the threshold coefficient), based on the environmental gradient trigger the Monte Carlo method to re-evaluate the synthetic uncertainty u c ;

[0019] Step 4, certificate topology construction:

[0020] Based on the type identification of the metrological standard device, call the node library to construct the topology graph:

[0021] Metrological result node: stores the metrological standard device serial number, u c and the key data packet P key (defined as the data subset corresponding to the inflection point or extreme point of the calibration curve);

[0022] Legal validity node: stores the blockchain notarization identification;

[0023] Environmental declaration node: stores the environmental extreme value (extracted from );

[0024] Establish inter-node linkages:

[0025] Forced linkage: metrology result node → legal force node (one-way);

[0026] Conditional linkage: when ∥ > δ env , environmental declaration node metrology result node (δ env is the environmental fluctuation threshold);

[0027] Output the topological graph with the metrology result node as the root, the legal force node as the leaf, and the environmental declaration node as the conditional bypass node;

[0028] Step 5, Topology data anchoring:

[0029] Serialize the topological graph into structured data, calculate the root hash H root and write it to the blockchain to obtain the block height B h , anchor B h to the legal force node;

[0030] Step 6, Certificate compilation output:

[0031] Serialize the topological graph into JSON-LD format, generate a digital signature Sig SK (·) with the issuer's private key SK, and render it as a verifiable electronic certificate.

[0032] Preferably, the hardware encryption tunnel of step 1 is implemented as follows:

[0033] Implement an AES-256 encryption function hardware acceleration within the FPGA chip;

[0034] Set the transmission timeout threshold δ time (typical value 1s), activate the breakpoint resume when the data packet interval t i+1 -t i > δ time ;

[0035] Environmental parameter vector Additional sensor digital signature Sig sensor (T i , H i ).

[0036] Preferably, the aging function f(τ) of step 2 is dynamically updated as follows:

[0037] Obtain the historical calibration sequence {u c1 , u c2 ,..., u cm} of the metrological standard;

[0038] Fitting decay coefficient where u c0 is the initial calibration value, τ j is the usage duration at the jth calibration;

[0039] When the fitting residual is larger than a threshold, max a metrological standard failure alert is sent to the metrological platform.

[0040] Preferably, the Monte Carlo reevaluation of step 3 includes:

[0041] Based on the environmental gradient Generate perturbation data (k = 1, 2, …, K), where η (k) is a random variable following the standard normal distribution;

[0042] For each group Perform:

[0043] Calculate the principal component:

[0044] Synthesize the uncertainty B value u

[0045] Take the median of u as the final expanded uncertainty u c .

[0046] Preferably, step 4 performs differentiated node configuration based on the application scenarios of the metrological standard instrument:

[0047] Security association scenario: when the metrological standard instrument is used for vital sign monitoring or medical diagnosis, add a security declaration node and establish a permanent mandatory link with the metrological result node;

[0048] Production traceability scenario: when the metrological standard instrument is used for product manufacturing process monitoring, add a production batch identification node and link it to the metrological result node;

[0049] Environment-sensitive scenario: when the metrological standard instrument is used for precision experiments and environmental fluctuations > δ env , insert a usage restriction declaration node;

[0050] General scenario: by default, only the minimum topology structure of the metrological result node, legal validity node, and environmental declaration node is retained.

[0051] Preferably, the root hash calculation of step 5 uses:

[0052] Leaf node hash: H R = Hash(u c || Pkey ), H L = Hash (Legal Effectiveness Node Metadata);

[0053] Build a Merkle Tree: H root = Hash (Hash (H R )∥Hash (H L ));

[0054] When the blockchain is stored, a timestamp authority signature is added.

[0055] Preferably, the environmental gradient processing of step 2 includes:

[0056] Identify environmental mutation points: when or is marked as a transient section, where R T is the temperature mutation threshold, and R H is the humidity mutation threshold;

[0057] Only calculate the weight matrix W for steady-state section data;

[0058] Record the number of transient events in the environmental declaration node.

[0059] Preferably, the visualization rendering of step 6 realizes:

[0060] Parse the topology graph to generate an interactive node atlas;

[0061] Embed u c in the metrological result node;

[0062] The W matrix and the original environmental data curve are dynamically displayed after the backtracking function is clicked;

[0063] Map the environmental fluctuation intensity through color gradient.

[0064] Preferably, when the certificate is verified, the following is performed:

[0065] Query H root from the blockchain corresponding to the storage time T c ;

[0066] Verify that T c is earlier than the effective period of the metrological standard and that the digital signature Sig SK (·) is valid;

[0067] Reconstruct the topology graph to calculate H′ root and compare it with the blockchain record;

[0068] When they do not match, mark the tampering suspected node with a flashing red light in the atlas.

[0069] Preferably, the method further includes, step 7 historical credibility feedback:

[0070] Extracting the combined uncertainty of the first m calibrations of the metrological standard instrument

[0071] Calculate u this time c Standardization bias (μ is the historical mean, σ) hist (Historical standard deviation);

[0072] If z>z crit (The preset standardization deviation threshold) adds a warning label to the measurement result node and automatically triggers the audit process, retrieving the original data and re-executing the uncertainty assessment process of steps 3 to 4.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] This invention ensures authentic data transmission by constructing a hardware-encrypted tunnel, dynamically integrates environmental gradients and equipment aging factors to generate a compensation weight matrix, and improves the real-time performance and accuracy of uncertainty assessment. It enhances computational robustness in high-interference scenarios based on a Monte Carlo reassessment mechanism and dynamically adapts to compliance requirements across multiple scenarios through a topological certificate structure. It utilizes blockchain-anchored root hashes and JSON-LD digital signatures to ensure the certificate is tamper-proof and legally traceable. Simultaneously, an interactive visualization design supports backtracking verification of the computation process. Ultimately, it achieves fully automated closed-loop processing of calibration data from collection and assessment to certificate output, solving the problems of delayed environmental interference quantification, lack of equipment status compensation, and weak legal validity.

[0075] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0076] Figure 1 This is a flowchart of the remote intelligent metrology calibration certificate automatic calculation method of the present invention;

[0077] Figure 2 This is a schematic diagram of the dynamic weight generation mechanism of the present invention;

[0078] Figure 3 This is a flowchart of the Monte Carlo re-evaluation process of the present invention;

[0079] Figure 4 This is a flowchart of the blockchain anchoring process of the present invention. Detailed Implementation

[0080] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work belong to the scope of protection of the present application.

[0081] Please refer to Figures 1-4 , a remote intelligent metrological calibration certificate automatic calculation method in the present application solves three major problems of traditional calibration, i.e. environmental interference neglect, device aging compensation lag, and weak legal effect of the certificate. The method realizes real-time calibration process, intelligent evaluation, and legal certificate through the whole-chain closed-loop processing of encrypted data flow → dynamic weight calculation → self-adaptive uncertainty evaluation → topology certificate construction → block chain anchoring → visual output. Each step forms a close technical link: steps 1-2 are data layer processing, outputting purified data with environmental labels; steps 3-4 are calculation layer, generating environment-adaptive uncertainty and topology structure; steps 5-7 are legal layer, ensuring that the certificate is tamper-proof and traceable.

[0082] Step 1: Secure data flow establishment

[0083] An anti-interference data transmission channel is constructed to ensure the confidentiality, integrity and recoverability of the calibration data in the transmission process, providing a trusted data source for subsequent calculation.

[0084] Input: original calibration data stream generated by the metrological standard

[0085] 1. Hardware encryption tunnel construction:

[0086] FPGA chips are deployed between the metrological standard and the edge node to realize real-time encryption of data packets by solidifying the AES-256 encryption algorithm;

[0087] Each original calibration data packet p i contains:

[0088] Metrological original value (such as voltage / current signal);

[0089] High-precision timestamp t i ;

[0090] Environmental parameter vector , where T i is temperature and H i is humidity;

[0091] Sensor digital signature Sig sensor (T i , H i ) (based on the ECDSA algorithm to prevent environmental data tampering);

[0092] Output: Encrypted data stream P.

[0093] 2. Resume mechanism:

[0094] Set transmission timeout threshold δ time (default 1 second);

[0095] When the data packet interval t i+1 -t i > δ time :

[0096] Automatically record the storage location of the last valid packet p i ;

[0097] After reestablishing the connection, resume from p i ;

[0098] Check the hash value of the resumed data to ensure continuity.

[0099] Output: Encrypted data stream P = {p1, p2,..., p n} with timestamp and environmental signature.

[0100] By reducing transmission delay through hardware-accelerated encryption, combining digital signature and resume mechanism, eliminate the risk of data loss and prevent man-in-the-middle attacks, ensure the authenticity and continuity of data used for subsequent calculations.

[0101] Step 2: Dynamic weight generation

[0102] Quantify the impact of environmental mutations and device aging on calibration, providing dynamic compensation coefficients for uncertainty.

[0103] Input: Data stream P and environmental parameter vector

[0104] 1. Environmental mutation detection:

[0105] Calculate the environmental gradient of adjacent data packets:

[0106]

[0107] If the temperature change rate or the humidity change rate (R T , R H are industry preset thresholds):

[0108] Mark this period as a "transient period";

[0109] Exclude transient period data from weight calculation;

[0110] Record the number of transient events in the environment declaration node;

[0111] Output: Steady-state dataset P steady .

[0112] 2. Aging function is dynamically updated:

[0113] Obtain the device's historical calibration sequence {u c1 ,u c2 ,...,u cm};

[0114] Fitted attenuation coefficient λ:

[0115]

[0116] Where u c0 τ is the initial calibration value. j The duration of use during the j-th calibration;

[0117] When the fitting residual > max At that time, a metering standard failure alarm is sent to the metering platform;

[0118] Output: Attenuation coefficient λ.

[0119] 3. Weight matrix calculation:

[0120] Based on environmental gradient change vector ( For the rate of temperature change, (The rate of change of humidity) and the aging function of the metrological standard f(τ) = e -λτ (τ is the usage duration, λ is the attenuation coefficient), generate the real-time compensation weight matrix:

[0121]

[0122] Where α and β are industry correction factors, and ||·|| is the Euclidean norm;

[0123] Output: Real-time weight matrix W.

[0124] By filtering transient environmental noise and combining it with the historical attenuation patterns of the equipment, the weight matrix dynamically reflects the current environmental and equipment status, eliminating evaluation bias caused by sudden environmental changes and providing accurate compensation coefficients for subsequent uncertainty calculations.

[0125] Step 3: Adaptive Uncertainty Calculation

[0126] By integrating environmental weights and equipment aging factors, dynamic optimization and evaluation of calibration result uncertainty can be achieved.

[0127] Input: The weight matrix W output from step 2, and the stable dataset P. steadyand environmental gradient

[0128] 1. Base evaluation:

[0129] Injecting the weight matrix W into the uncertainty evaluation model;

[0130] Principal component u A : Characterizing data dispersion

[0131]

[0132] where σ is the standard deviation of the stable dataset P steady ;

[0133] Environmental component u B : Fusing the weight matrix

[0134] u B = k · ||W||F F

[0135] where k is the inclusion factor and ||·||F F is the matrix Frobenius norm;

[0136] Synthetic uncertainty initial value:

[0137] Output: Initial evaluation u c .

[0138] 2. Monte Carlo reevaluation:

[0139] Trigger condition: When the environment is dominant (γ is the threshold coefficient);

[0140] Based on the environmental gradient Generate K sets of perturbed data:

[0141]

[0142] Calculate the synthetic uncertainty for each set of data:

[0143]

[0144] Take the median as the final result:

[0145] If the number of iterations K > 10000 or the continuous 100 times u c fluctuation is less than 0.1%, terminate the simulation; similarly, take the median generated at present as the final synthetic uncertainty u c ;

[0146] If the termination reason is K > 10000 (i.e. not convergent), trigger an alarm and record the environmental gradient extreme value max For manual review;

[0147] The actual number of iterations K, the termination type (convergence / overlimit), and the final u are included. c The value is written into the environment declaration node metadata below.

[0148] Output: Optimized synthesis uncertainty u c .

[0149] By simulating the impact of extreme environmental fluctuations on data using Monte Carlo simulation and utilizing the statistical median to resist outlier interference, the robustness of the assessment under high-interference scenarios is significantly improved.

[0150] Step 4: Certificate Topology Construction

[0151] Build an extensible certificate data structure to meet compliance requirements in multiple scenarios.

[0152] Input: The optimized combined uncertainty u from step 3 c and the stable dataset P in step 2 steady Inflection point dataset P key and environmental data The extreme value.

[0153] 1. Core node creation:

[0154]

[0155] 2. Differentiated node configuration:

[0156] Security-related scenarios (e.g., medical devices):

[0157] Add a security assertion node (e.g., store medical compliance standards).

[0158] Establish a permanent mandatory link: Measurement Results Node → Security Statement Node

[0159] Production traceability scenarios (e.g., industrial manufacturing):

[0160] Add a production batch identifier node (e.g., to store batch number, process parameters).

[0161] Establish a mandatory link: Measurement Result Node → Batch Identifier Node

[0162] Environmentally sensitive scenarios (e.g., precision laboratories):

[0163] When ∥E max -E min ∥>δ env (δ env When the environmental fluctuation threshold is reached, insert a usage restriction declaration node (storage environment tolerance range).

[0164] 3. Topology definition:

[0165] Forced link: Measurement result node → Legal effect node (one-way and non-deletable)

[0166] Conditional link: Environmental declaration node → Measurement result node when environmental fluctuations exceed the standard;

[0167] Output: Topology graph G(V, E) with scenario nodes.

[0168] By dynamically adding and deleting nodes, it can adapt to the compliance requirements of different industries, such as removing production batch nodes in medical scenarios to simplify the structure, and strengthening the traceability ability in industrial scenarios to ensure that the warranty meets the legal effect and avoids information redundancy.

[0169] Step 5: Topology data anchoring

[0170] Using blockchain technology to solidify the topology relationship of the certificate, realizing tamper-proof storage.

[0171] Input: Topology graph G(V, E) of step 4.

[0172] 1. Merkle tree construction:

[0173] Leaf node hash calculation:

[0174] H R = SHA3-256(u c ∥P key )H L = SHA3-256(law node metadata)

[0175] Root hash generation: H root = SHA3-256(SHA3-256(H R )∥SHA3-256(H L )).

[0176] 2. Blockchain storage:

[0177] Write H root into the Hyperledger Fabric chain, preferentially choose a permissioned chain architecture, and ensure the legal identity of participants such as measurement agencies and regulators through node admission mechanisms.

[0178] Get block height B h and authoritative timestamp T c ;

[0179] Write B h into the storage identification bit of the legal effect node.

[0180] Output: Anchored topology graph G'(V, E);

[0181] Output: Blockchain storage identification (H root ,B h ,T c ), i.e., the anchored topological graph G'(V,E).

[0182] By compressing certificate data through the Merkel tree and generating a unique root hash, combined with the distributed storage characteristics of the blockchain, any node tampering will cause H root to fail, mathematically ensuring tamper resistance.

[0183] Step 6: Certificate compilation output

[0184] Generate human-readable and legally complete verifiable electronic certificates.

[0185] Input: Anchored topological graph G'(V,E) in step 5.

[0186] 1. Structured conversion:

[0187] Serializes the topological graph G'(V,E) into JSON-LD format, including:

[0188]

[0189] 2. Digital signature:

[0190] Generate signature Sig SK (JSON-LD) with issuer private key SK.

[0191] 3. Interactive rendering:

[0192] Dynamic node graph:

[0193] Measurement result node: for example, red (centered as root node);

[0194] Legal effect node: for example, blue (leaf node);

[0195] Environmental declaration node: color gradient (for example, light green → deep green mapping ∥ ∥ value).

[0196] Backtracking function:

[0197] Click on the measurement result node → pop up W matrix heat map and temperature and humidity curve;

[0198] Hover over the environmental declaration node → display transient event count.

[0199] Output: Digitally signed verifiable electronic certificate (for example: format PDF+JSON-LD)

[0200] The machine-readable legal semantics are realized by JSON-LD, and combined with the visual backtracking function, so that the verification personnel can intuitively trace the basis for calculating the uncertainty during artificial verification, greatly reducing the verification complexity.

[0201] Step 7: Historical credibility feedback

[0202] Abnormal calibration results are identified through historical data comparison.

[0203] Input: This time's u c and the historical combined uncertainty sequence.

[0204] Extract the combined uncertainty of the previous m calibrations of the measuring standard

[0205] Calculate the standardized deviation of this result:

[0206] Calculate the standardized deviation of this time's u c (μ is the historical mean, σ hist is the historical standard deviation);

[0207] If z>z crit (pre-set standardized deviation threshold):

[0208] Add a warning mark to the measurement result node;

[0209] Automatically trigger the audit process: retrieve the original data of this time and re-execute steps 3-4.

[0210] Output: Credibility status (normal / warning).

[0211] Based on the principle of statistical process control (SPC), the degree of deviation of this result from the historical trend is quantified by z-score, and problems such as equipment abnormalities or operation errors are discovered in a timely manner.

[0212] Step 8: Certificate verification

[0213] Verify the completeness and legal validity of the certificate.

[0214] Input: Electronic certificate, blockchain storage record

[0215] 1. Blockchain verification:

[0216] Query H root from the blockchain corresponding to the storage time T c ;

[0217] Verify that T c is earlier than the validity period of the measuring standard.

[0218] 2. Signature verification:

[0219] ​Verify Sig with authority public key SK (·) validity.

[0220] 3. Topology integrity check:

[0221] Reconstruct topology graph to calculate H' root ;

[0222] Compare H stored in blockchain root .

[0223] Tamper localization:

[0224] If H' root ≠ H root :

[0225] Flashing abnormal nodes at 0.5Hz frequency in interaction graph;

[0226] Output: verification result report (valid / invalid / tampered).

[0227] Through the triple verification mechanism (time validity, digital signature, and topology consistency), any tampering behavior can be located to a specific node, meeting the requirements of judicial evidence.

[0228] This embodiment builds a closed-loop remote intelligent metrological calibration method through encrypted data stream transmission, dynamic weight compensation of environmental mutation and device aging, adaptive uncertainty evaluation, and blockchain storage of topology certificates, realizes real-time calibration process, intelligent evaluation, and legalization of certificates, and solves the three core problems of traditional methods, i.e., environmental interference neglect, device aging compensation lag, and weak legal effectiveness of certificates.

Claims

1. A method for automatically calculating remote intelligent metrology calibration certificates, characterized in that, Includes the following steps: Step 1, Establishing a secure data flow: A hardware-encrypted tunnel is constructed between the metrology standard and the edge node to transmit the original calibration data packet P = {p1, p2, ..., p} in real time. n }, where each data packet p i All include a timestamp t i and environmental parameter vector E i =(T i H i ), where T i For temperature, H i Humidity; Step 2, Dynamic Weight Generation: Based on environmental gradient change vector (Rate of temperature change, rate of humidity change) and aging function of metrology standard f(τ) = e -λτ (τ is the usage duration, λ is the attenuation coefficient), generate the real-time compensation weight matrix: Where α and β are industry correction factors, and ||·|| is the Euclidean norm; Step 3, Adaptive uncertainty calculation: Inject the weight matrix W into the uncertainty assessment model: Principal component calculation: (σ is the standard deviation of data packet P); Environmental component calculation: u B =k·∥W∥ F (k is the inclusion factor, ∥·∥) F (where Frobenius norm is the matrix norm); Combined uncertainty: when When γ is the threshold coefficient, based on the environmental gradient Triggering Monte Carlo method to re-evaluate synthesis uncertainty u c ; Step 4, Certificate Topology Construction: Based on the type identifier of the metrological standard instrument, a topology graph is constructed by calling the node library: Measurement Result Node: Stores the serial number of the measurement standard instrument, u c and key data packet P key (Defined as a subset of data corresponding to the inflection point or extreme point of the calibration curve); Legal validity node: Stores blockchain-based evidence identifiers; Environment declaration node: Storage environment extremes (from Extracted from ( ); Establish the link relationships between nodes: Forced link: Measurement result node → Legal effect node (one-way); Conditional link: when At that time, the environment declaration node Measurement result node (δ) env (For environmental fluctuation thresholds); Output a topology graph with the measurement result node as the root, the legal effect node as the leaf, and the environmental statement node as the conditional bypass node; Step 5, Topology Data Anchoring: Serialize the topology graph into structured data and calculate the root hash H. root And write to the blockchain to obtain block height B h B h Anchored to the point of legal effect; Step 6, Certificate compilation output: Serialize the topology graph into JSON-LD format and generate a digital signature Sig using the issuing authority's private key SK. SK (·) is rendered as a verifiable electronic certificate.

2. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, The hardware encryption tunnel in step 1 is implemented in the following way: Hardware acceleration of AES-256 encryption functions is implemented within the FPGA chip; Set transmission timeout threshold δ time (Typical value 1s), when the data packet interval t i+1 -t i >δ time Activate resume download at breakpoint; Environmental parameter vector Additional sensor digital signature Sig sensor (T i H i ).

3. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, The aging function f(τ) in step 2 is dynamically updated in the following way: Obtain the historical calibration sequence of the metrology standard {u c1 ,u c2 ,...,u cm }; Fitted attenuation coefficient Where u c0 τ is the initial calibration value. j The usage time during the j-th calibration; When the fitting residual > max At that time, a metering standard failure alarm is sent to the metering platform.

4. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, Step 3 of the Monte Carlo reassessment includes: Based on environmental gradient Generate perturbation data Where η (k) Let be a random variable that follows a standard normal distribution; For each group P (k) implement: Calculate the principal components: Using step 3 u B Value combination uncertainty Pick The median is used as the final expanded uncertainty u c .

5. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, Step 4: Configure differentiated nodes based on the application scenarios of metrological standard instruments. Safety-related scenarios: When metrological standard instruments are used for vital sign monitoring or medical diagnosis, add a safety statement node and establish a permanent mandatory link with the metrological result node; Production traceability scenario: When metrological standard instruments are used for monitoring the product manufacturing process, add a production batch identification node and force a link to the metrological result node; Environmentally sensitive scenarios: When metrological standards are used in precision experiments and the environment fluctuates. When inserting, use a restricted declaration node; General scenario: By default, only the minimal topology of the measurement result node, legal effect node, and environmental statement node is retained.

6. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, The root hash calculation in step 5 uses: Leaf node hash: H R =Hash(u c ∥P key ),H L =Hash(legal validity node metadata); Constructing a Merkle tree: H root =Hash(Hash(H R )∥Hash(H L )); When storing evidence on the blockchain, add a timestamp and authoritative signature.

7. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 2, characterized in that, Step 2, environmental gradient processing, includes: Identify environmental mutation points: when The time segment is marked as a transient segment, where R T R is the threshold for temperature abrupt change. H The threshold for sudden changes in humidity; The weight matrix W is calculated only for the steady-state data. Record the number of transient events in the environment declaration node.

8. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, Step 6: Visual rendering implementation: Parse the topology graph to generate an interactive node graph; Embed u in the measurement result node c Calculation backtracking function; Clicking the backtracking function dynamically displays the W matrix and the original environmental data curve; The intensity of environmental fluctuations is mapped using color gradients.

9. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, Execute during certificate verification: Query H from the blockchain root The corresponding evidence storage time T c ; Verify T c Earlier than the expiration date of the metrological standard and digitally signed Sig SK (·)efficient; Calculate H′ by reconstructing the topology graph root And compare with blockchain records; When there is a mismatch, the suspected tampering node is marked with a flashing dot in the graph.

10. The method for automatically calculating remote intelligent metrology calibration certificates according to claim 1, characterized in that, The method also includes step 7, historical credibility feedback: Extracting the combined uncertainty of the first m calibrations of the metrological standard instrument Calculate u this time c Standardization bias (μ is the historical mean, σ) hist (Historical standard deviation); If z>z crit (The preset standardization deviation threshold) adds a warning label to the measurement result node and automatically triggers the audit process, retrieving the original data and re-executing the uncertainty assessment process of steps 3 to 4.

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