Blockchain-based power distribution transformer quality detection result discrimination method and device

Abstract

This invention provides a blockchain-based method and apparatus for judging the quality inspection results of distribution transformers, belonging to the field of transformer testing technology; it solves the problem of data tampering in distribution transformer quality inspection reports; it includes the following steps: S1: Data collection: collect quality inspection datasets of the same batch and model of distribution transformers provided by the distribution transformer supplier, and obtain the key state parameters of the distribution transformers in all quality inspection reports; S2: perform consistency comparison between the distribution transformer quality inspection dataset and the key state parameters extracted from the quality inspection reports, and solidify the comparison rules into a smart contract in the form of code, and deploy it in the blockchain node; S3: Blockchain node data synchronization: initiate consensus verification of parameter information and verification results over a period of time through the blockchain; S4: Screening of suspicious quality inspection reports: screen the quality inspection reports corresponding to the suspicious data verified in S3; this invention is applied to transformers.

Description

Technical Field

[0001] This invention provides a method and device for judging the quality inspection results of distribution transformers based on blockchain, belonging to the field of transformer inspection technology. Background Technology

[0002] With the continuous expansion of power equipment construction, the procurement volume of 10kV distribution transformers has increased significantly. This has led to risks associated with verifying the authenticity of distribution transformer test results, particularly for input materials provided by external parties. These materials still require verification from the participating parties, resulting in substantial management costs and posing risks to management due to the issue of material authenticity. Furthermore, during the provision of distribution transformer quality inspection reports, suppliers have been found to tamper with test results (adding or subtracting test items, modifying test parameters, or colluding with testing institutions to issue false reports). This necessitates power company procurement personnel submitting distribution transformers to internal testing institutions for sampling inspection. After sampling inspection, if the supplier raises objections to abnormal parameters, the test items require secondary quality inspection and verification. This involves manual verification and cross-comparison across multiple business processes, resulting in a large workload, large data volume, and high level of expertise required.

[0003] Patent application number 201910933689.6 discloses an auxiliary evaluation method for distribution transformer inspection results. The method includes: S1: organizing and extracting qualified distribution transformer sampling inspection data into a data feature sample set; S2: cleaning the data feature sample set; S3: normalizing the data feature quantities using the Z-score standardization method; S4: establishing a single-class support vector machine (SVM) judgment model to obtain the model parameters; S5: after training, using the established judgment model to make judgments on the test dataset, marking outliers, and outputting the results. This method preprocesses the inspection data of various indicators from multiple inspection reports, trains the normal data using OCSVM to obtain reasonable model parameters, and then uses the trained model to make judgments on the test set, thereby identifying suspected misjudged abnormal inspection reports. Experimenters can refer to the evaluation results to decide whether to re-inspect the transformers corresponding to the abnormal inspection reports. However, the above method still has the possibility of data tampering, and the authenticity and reliability of the inspection results are not high. Summary of the Invention

[0004] To address the problem of data tampering in distribution transformer quality inspection reports, this invention proposes a blockchain-based method and apparatus for judging distribution transformer quality inspection results.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a blockchain-based method for judging the quality inspection results of distribution transformers, comprising the following steps:

[0006] S1: Data collection: Collect the quality inspection dataset of the same batch and model of distribution transformers provided by the distribution transformer supplier, and obtain the key state parameters of the distribution transformers in all quality inspection reports. Organize them into a data feature sample set. The key state parameters extracted from the quality inspection report of a distribution transformer are regarded as a sample. The key state parameters generated by the various quality inspection tests carried out on this distribution transformer are used as the feature attributes of this sample.

[0007] S2: Perform consistency comparison between the key state parameters extracted from the distribution transformer quality inspection dataset and quality inspection report, solidify the comparison rules into a smart contract in the form of code, and deploy it in the blockchain node to achieve automated on-chain comparison;

[0008] S3: Blockchain node data synchronization: Initiating consensus verification by using the blockchain to synchronize parameter information and calibration results over a period of time;

[0009] S4: Screening of Suspicious Quality Inspection Reports: Screening of quality inspection reports corresponding to suspicious data that have been verified by S3.

[0010] The distribution transformer quality inspection dataset in step S1 includes key state parameter data and production process data of the distribution transformer. The key state parameters of the distribution transformer include: DC insulation resistance measurement between windings and ground and between windings, insulation inspection of core and clamps, winding resistance measurement, voltage ratio measurement and connection group label verification, no-load loss and no-load current measurement, short-circuit impedance and load loss measurement, external withstand voltage test, induced withstand voltage test, partial discharge measurement, on-load tap changer test, pressure sealing test, temperature rise test, sound level measurement, line-end lightning full wave impulse test, line-end lightning chopped wave impulse test, short-circuit withstand capacity test, and pressure deformation test data.

[0011] The consistency comparison of key state parameters extracted from the distribution transformer quality inspection dataset and quality inspection report in step S2 includes:

[0012] Compare the supplier's technical standard data for distribution transformers with the quality inspection report data from the testing agency;

[0013] Compare the supplier's power distribution transformer bidding parameters with the testing agency's quality inspection report data;

[0014] Compare the supplier's power distribution transformer production process data with the technical standard data for power distribution transformer inspection;

[0015] Compare the supplier's power distribution transformer production process data with the bidding parameter data for the power distribution transformer inspection;

[0016] The comparison rules are based on interval value judgment, which determines whether the data in the quality inspection report of the testing institution is within the range of the technical standard data for distribution transformers, whether the data in the quality inspection report of the testing institution is within the range of the bidding parameters data for distribution transformers, whether the production process data of distribution transformers is within the range of the technical standard data for distribution transformers, and whether the production process data of distribution transformers is within the range of the bidding parameters data for distribution transformers.

[0017] Step S3 specifically includes:

[0018] Blockchain nodes are divided into consensus nodes and parameter nodes. The storage blocks within a node are divided into parameter blocks and verification blocks. Consensus nodes store verification blocks, while parameter nodes only store parameter blocks. There are markers in the blocks to identify the block type.

[0019] a: Initiating consensus involves the master node collecting parameter information and verification results over a period of time and sending them to all consensus nodes and parameter nodes, thus initiating a consensus request;

[0020] b: Consensus data verification, that is, the nodes participating in the consensus verify the consensus requirements of the master node. If they pass, the consensus node will confirm the consensus information to the master node.

[0021] c: After the master node, consensus node, and parameter node reach a consensus, the master node broadcasts a consensus confirmation message, publishes parameters or verifies data to the block and adds it to the block. After the node adds the block to the blockchain it maintains, it deletes the request information in the log according to the checkpoint protocol and starts the next round of consensus.

[0022] The on-chain automated comparison process is as follows:

[0023] a: The testing personnel obtain public and private keys through system registration, formulate a smart contract for the quality testing report judgment method, and digitally sign it with their own private key. The digitally signed smart contract will be transmitted to the blockchain network.

[0024] b: Solidified quality inspection report judgment method: The smart contract is transmitted to the blockchain network for unified verification. The contract is spread through the network and stored on each node of the blockchain. Once the consensus mechanism is triggered and activated, the smart contract is verified by the testing agency to verify the validity of the contract. After successful verification, a hash block ID is generated and quickly spread to the entire network. Other consensus nodes store the smart contract.

[0025] c: Consensus result returned to the system: After consensus is reached, the hash block ID is returned to the system to determine the legality of the quality inspection report judgment method;

[0026] d: Parameter node calls smart contract to execute automatically: The parameter node will periodically traverse the smart contract of each consensus node and automatically complete the entire quality inspection report judgment;

[0027] e: Quality Inspection Report Judgment Process: When the smart contract execution conditions are met, the quality inspection report data and production process data to be judged are first read from the verification block and consistency verification is performed in the blockchain network. Then, the technical standard data and bidding parameter data are read from the parameter block and consistency verification is performed in the blockchain network. After completing the double consistency verification, the smart contract automatically completes the judgment of the quality inspection report data.

[0028] The method for screening suspicious quality inspection reports in step S4 includes two methods:

[0029] a: Based on the median value of the range of test items in the bidding parameters, calculate the variance with the test item parameters in the test report, and sort the test reports corresponding to the variance from largest to smallest;

[0030] b: Using the production parameter testing items as a benchmark, calculate the variance of the testing items in the testing report, and sort the testing reports corresponding to the variance from largest to smallest.

[0031] In step S4, when screening suspicious quality reports, two screening methods are considered together. Reports with large variances are more suspicious. Reports with large variances are selected for internal quality inspection by the power company. The parameters of the internal inspection reports are directly obtained through the distribution transformer testing equipment to determine whether the data of the internal quality inspection reports are within the range of the bidding parameters for distribution transformers. The above operation repeats the process of step S3 and is stored in the consensus node.

[0032] A blockchain-based distribution transformer quality inspection device includes: at least one processor, at least one temperature sensor, at least one humidity sensor, at least one transmission interface, at least one memory, and at least one system bus; the above modules communicate with each other through the system bus.

[0033] The processor has the following built-in features:

[0034] The quality inspection report data receiving module is used to obtain key status parameter data of the distribution transformer secondary quality inspection equipment.

[0035] The quality inspection report data standardization module is used to standardize the format of quality inspection report data according to the standard data format of quality inspection reports, so as to obtain the key status parameter data of the standardized quality inspection report.

[0036] The quality inspection calculation module is used to perform deviation compensation calculation on the key state parameter data according to the set calculation rules for the distribution transformer inspection environment, based on the temperature and data of the environment in which the distribution transformer is located during quality inspection, and to obtain the processed key state parameter data results.

[0037] The anomaly detection module determines the anomaly characteristics of the key state parameter data if the detected key state parameter data results are abnormal.

[0038] The result transmission module is used for on-chain storage of key state parameter results, temperature, humidity and other data.

[0039] The quality inspection calculation module includes: a quality inspection calculation slave module, which is used to perform error compensation on the key state parameters of the distribution transformer according to the set calculation rules, temperature and humidity, and then perform calculation according to the smart contract comparison rules to obtain the calculated key state parameter data results.

[0040] The monitoring device is deployed at the source end of the distribution transformer testing equipment. During secondary sampling and verification of the screened suspicious quality reports, the device can promptly obtain key status parameters from the test reports and assess the errors of these parameters based on the temperature and humidity of the testing environment, storing the data on the blockchain. The advantages of this invention compared to existing technologies are as follows: Based on trusted production process data and quality inspection process data stored on the blockchain, this invention establishes an automated processing flow for comparing quality inspection data obtained from the blockchain, enabling cross-verification of on-chain test reports, providing early warnings for suspicious data, preventing the forgery of test reports, thereby avoiding unfair quality inspection results caused by human factors, ensuring the openness and transparency of quality inspection operations, and achieving reliable and secure traceability verification of problematic products and reliable control over external suppliers and quality inspection agencies. Attached Figure Description

[0041] The present invention will be further described below with reference to the accompanying drawings:

[0042] Figure 1 This is a flowchart of the method of the present invention;

[0043] Figure 2 This is a schematic diagram of the detection device of the present invention;

[0044] Figure 3 This is a schematic diagram of the module connection structure built into the processor in the detection device of the present invention. Detailed Implementation

[0045] like Figures 1 to 3As shown, this invention solves the following problems: 1. The problem of data tampering in quality inspection reports: In the process of verifying the quality inspection reports provided by distribution transformer suppliers, suppliers may reduce original inspection items, modify original inspection item parameters, or collude with testing agencies to issue false reports. Power company internal testing personnel need to manually verify and cross-compare data in multiple business processes, resulting in a large workload, large data volume, and high level of expertise required; 2. The risk of lack of mutual trust: After quality problems occur in distribution transformers, there is a difficulty in defining quality responsibility, and suppliers and project units or suppliers and upstream suppliers may shift blame to each other. Determining responsibility through secondary inspections by testing agencies is costly and time-consuming.

[0046] To address the aforementioned issues, this invention provides a blockchain-based method for judging the quality inspection results of distribution transformers. This method is used to identify suspicious quality inspection results for distribution transformers and improve the credibility of internal quality inspections within power companies. This invention can identify quality inspection reports with suspected problems for inspection personnel to refer to, helping them decide whether to re-inspect the distribution transformers corresponding to the suspected problem reports.

[0047] This invention mainly includes the following steps:

[0048] 1) Data collection:

[0049] We collect quality inspection datasets (structured data of key state parameters and structured data of the production process) of distribution transformers of the same batch and model provided by the distribution transformer suppliers. We also obtain the key state parameters of the distribution transformers in all quality inspection reports (converting unstructured data to structured data), including winding-to-ground and winding-to-winding DC insulation resistance measurement, core and clamp insulation inspection, winding resistance measurement, voltage ratio measurement and connection group label verification, no-load loss and no-load current measurement, short-circuit impedance and load loss measurement, external withstand voltage test, induced withstand voltage test, partial discharge measurement, on-load tap changer test, pressure sealing test, temperature rise test, sound level measurement, line-end lightning full-wave impulse test, line-end lightning chopped wave impulse test, short-circuit withstand capacity test, pressure deformation test, etc. We organize these into a data feature sample set. The key state parameters extracted from the quality inspection report of a distribution transformer are regarded as a sample, and the key state parameters generated by the various quality inspection tests performed on this distribution transformer are used as the feature attributes of this sample.

[0050] 2) Perform a consistency comparison between the key state parameters extracted from the distribution transformer quality inspection dataset and the quality inspection report:

[0051] The technical standard data of the supplier's distribution transformer is compared with the quality inspection report data of the testing agency. This comparison is a range value judgment to determine whether the data in the quality inspection report of the testing agency is within the range of the technical standard data of the distribution transformer. The bidding parameter data of the supplier's distribution transformer is compared with the quality inspection report data of the testing agency, and the comparison rules are the same as above. The production process data of the supplier's distribution transformer is compared with the technical standard data of the distribution transformer, and the comparison rules are the same as above. The production process data of the supplier's distribution transformer is compared with the bidding parameter data of the distribution transformer, and the comparison rules are the same as above.

[0052] The above process involves embedding a smart contract in code and deploying it on a blockchain node to achieve automated on-chain comparison. The specific steps are as follows:

[0053] A: Internal testing personnel of the power company obtain public and private keys through system registration, formulate a smart contract for the quality testing report judgment method, and digitally sign it with their own private key. The digitally signed smart contract will be transmitted to the blockchain network.

[0054] b: Solidify the quality inspection report verification method. The smart contract is transmitted to the blockchain network for unified verification. The contract is distributed through the network and stored on every node of the blockchain. Once the consensus mechanism is triggered, the smart contract is verified for validity by the testing agency. After successful verification, a hash block ID is generated and quickly distributed to the entire network. Other consensus nodes store the smart contract.

[0055] c: Consensus result returned to the system. After consensus is reached, the hash block ID is returned to the system to verify the legality of the quality inspection report judgment method.

[0056] d: The parameter node invokes the smart contract for automatic execution. The parameter node periodically iterates through the smart contracts of each consensus node and automatically completes the entire quality inspection report evaluation.

[0057] e: Quality Inspection Report Judgment Process. When the smart contract execution conditions are met, the quality inspection report data and production process data to be judged are first read from the verification block. Consistency verification is performed on the blockchain network, that is, a consensus is reached on the quality inspection data and production process data to prove that the quality inspection report data and production process data have not been tampered with. Then, the technical standard data and bidding parameter data are read from the parameter block and consistency verification is performed on the blockchain network. After completing the double consistency verification, the smart contract automatically completes the judgment of the quality inspection report data.

[0058] 3) Blockchain node data synchronization mechanism:

[0059] The blockchain nodes are divided into consensus nodes (full nodes, with a quantity of 4 or more) and parameter nodes (light nodes, with a quantity of 1). The storage blocks within the nodes are divided into parameter blocks and verification blocks. The consensus nodes store verification blocks, and the parameter nodes only store parameter blocks. There are flags in the blocks to identify the block types.

[0060] a: Initiate consensus. The primary node collects parameter information and verification results within a certain period and sends them to all consensus nodes and parameter nodes, that is, initiate a consensus requirement. Among the above parameter information, it includes parameter information 1 and parameter information 2. Parameter information 1 refers to the technical standard range, that is, the range of key status parameters of the distribution transformer specified by the state. Within this range, it is recognized as qualified (verification result), and outside this range, it is recognized as unqualified (verification result). Parameter information 2 refers to the range of key status parameters required by the tender, and this range standard is superior to the national technical standard range. The verification results include the national technical standard range, the tender technical standard range, individual values in the production process, and individual values in the test report.

[0061] b: Consensus data verification. That is, the nodes participating in the consensus verify the consensus requirement of the primary node. If it passes, the consensus nodes will confirm the consensus information (parameter block, verification block) to the primary node.

[0062] c: After the primary node, consensus nodes, and parameter nodes reach a consensus, the primary node broadcasts a consensus confirmation message. The primary node publishes parameters or verification data to the block and adds it to the block. The condition for consensus in the network is that the primary node receives at least 2f identical consensus messages broadcast from other consensus nodes participating in the consensus. After the node adds the block to the blockchain it maintains (the consensus nodes only maintain verification blocks, and the parameter nodes only maintain parameter blocks), according to the checkpoint protocol, the request information in the log is deleted, and the next round of consensus begins. The checkpoint protocol refers to the checkpoint protocol of the Byzantine system in the blockchain consensus algorithm.

[0063] 4) Screening of suspicious quality inspection reports:

[0064] Screen the quality inspection reports corresponding to the suspicious data in the above verification.

[0065] a: Based on the median value of the tender parameter test item range (data source: tender data source parameter node), calculate the variance with the test item parameters in the test report, and sort the test reports corresponding to the variance from largest to smallest.

[0066] b: Based on the production parameter test item (data source: production data source parameter node), calculate the method with the test item parameters in the test report, and sort the test reports corresponding to the variance from largest to smallest.

[0067] Considering both screening methods a and b, the test reports with larger variances are more likely to be falsified. Test reports with high suspicion are selected for internal quality testing by the power company. The parameters of the internal test reports are directly obtained through the distribution transformer testing equipment. It is determined whether the data of the internal quality inspection report is within the range of the distribution transformer bidding parameter data (parameter data source parameter node). The above operation repeats the process of step (3) and is stored in the consensus node.

[0068] The two most suspicious forms are:

[0069] 1. If the distribution transformer does not meet the bidding parameter requirements, adjust the key state parameters by raising or lowering them to meet the bidding parameter requirements. Use the intermediate value of the bidding parameters to calculate the variance and identify those with high suspicion.

[0070] 2. Logically, the critical state parameters in the production process and the critical state parameters in the test report should be similar or identical. If two single values ​​differ significantly, there is a high degree of suspicion.

[0071] This application provides a blockchain-based device for judging the quality inspection results of distribution transformers.

[0072] like Figure 2 The diagram illustrates a structural composition of a blockchain-based distribution transformer quality inspection result discrimination device provided in this application. The device includes: at least one processor, at least one temperature sensor, at least one humidity sensor, at least one transmission interface, at least one memory, and at least one system bus; the modules communicate via the system bus.

[0073] A processor is a central processing unit (CPU), a graphics processing unit (GPU), etc.

[0074] Memory includes high-speed RAM and non-volatile memory, etc.

[0075] The memory stores a program, and the processor can execute the program stored in the memory to implement the various steps of the quality detection and discrimination method provided in this application embodiment:

[0076] A quality inspection and discrimination method, comprising:

[0077] a: Receiving key status parameter data for quality inspection; receiving key status parameters output by the power distribution transformer testing equipment.

[0078] b: Data standardization processing of quality inspection reports, used to standardize the format of quality inspection report data according to the standard data format of quality inspection reports, and obtain the key status parameter data of the standardized quality inspection report;

[0079] c: Quality inspection data processing, used to perform deviation compensation calculations on key state parameter data according to the set calculation rules for the distribution transformer inspection environment and based on temperature and humidity parameters, to obtain the processed key state parameter data results;

[0080] d: Anomaly detection alarm, using smart contract comparison rules to judge anomalies in the detected key state parameter data results;

[0081] e: Result transmission to the blockchain, used for storing key state parameter result data on the blockchain.

[0082] Because the properties and characteristics of various materials within a transformer are related to temperature—for example, the insulation resistance of a power transformer decreases as temperature increases and increases as temperature decreases within the range of -20℃ to 40℃—this invention, in order to detect the impact of temperature on key parameters of distribution transformers, incorporates temperature and humidity sensors in the testing device. These sensors collect data on the temperature and humidity of the transformer's environment during testing, ensuring the smooth progress of the transformer testing process and the accuracy and reliability of the test results.

[0083] This device is applied to distribution transformer testing equipment. A blockchain-based distribution transformer quality testing result discrimination system includes a quality testing discrimination device. Temperature and humidity sensors and a positioning module are installed on the testing device. The device is deployed at the source end of the distribution transformer testing equipment. During the secondary sampling and verification of suspicious quality reports screened in step S4, the testing device can promptly obtain key state parameters from the testing reports and perform error assessment on these key state parameters based on the temperature and humidity of the testing environment, storing the results on the blockchain.

[0084] Figure 3 A schematic diagram of the internal structure of the processor in this quality detection and discrimination device is shown. The processor of the detection device in this embodiment may include:

[0085] a: Quality inspection report data receiving module, used to obtain key status parameter data of the distribution transformer testing equipment for secondary quality inspection of the distribution transformer;

[0086] b: Quality inspection report data standardization module, used to standardize the format of quality inspection report data according to the standard data format of quality inspection report, and obtain the key status parameter data of the standardized quality inspection report;

[0087] c: Quality inspection calculation module, used to perform deviation compensation calculation on key state parameter data according to the set calculation rules for the distribution transformer inspection environment, based on the temperature and data of the environment in which the distribution transformer is located during quality inspection, and to obtain the processed key state parameter data results;

[0088] d: Anomaly detection module: If the detected key state parameter data results are abnormal, determine the abnormal characteristics of the key state parameter data;

[0089] e: Result transmission module, used for on-chain storage of key state parameter results, temperature, humidity and other data.

[0090] The quality inspection calculation module includes a quality inspection calculation slave module, which is used to perform error compensation on the key state parameters of the distribution transformer according to the set calculation rules, temperature and humidity, and then perform calculations according to the smart contract comparison rules to obtain the calculated key state parameter data results.

[0091] Regarding the specific structure of this invention, it should be noted that the connection relationships between the various component modules used in this invention are definite and achievable. Except as specifically described in the embodiments, their specific connection relationships can bring about corresponding technical effects and solve the technical problems proposed by this invention without relying on the execution of corresponding software programs. The models of the components, modules, and specific components appearing in this invention, the connection methods between them, and the conventional usage methods and expected technical effects brought about by the above technical features, unless specifically described, are all publicly disclosed content in patents, journal articles, technical manuals, technical dictionaries, and textbooks that can be obtained by those skilled in the art before the application date, or belong to conventional technology, common knowledge, and other existing technologies in this field. There is no need to elaborate, which makes the technical solution provided in this case clear, complete, and achievable, and can reproduce or obtain corresponding physical products based on this technical means.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based method for judging the quality of a power distribution transformer, characterized in that: Includes the following steps: S1: Data collection: Collect the quality inspection dataset of the same batch and model of distribution transformers provided by the distribution transformer supplier, and obtain the key state parameters of the distribution transformers in all quality inspection reports. Organize them into a data feature sample set. The key state parameters extracted from the quality inspection report of a distribution transformer are regarded as a sample. The key state parameters generated by the various quality inspection tests carried out on this distribution transformer are used as the feature attributes of this sample. S2: Perform consistency comparison between the key state parameters extracted from the distribution transformer quality inspection dataset and quality inspection report, solidify the comparison rules into a smart contract in the form of code, and deploy it in the blockchain node to achieve automated on-chain comparison; The consistency comparison of key state parameters extracted from the distribution transformer quality inspection dataset and quality inspection report in step S2 includes: Compare the supplier's technical standard data for distribution transformers with the quality inspection report data from the testing agency; Compare the supplier's power distribution transformer bidding parameters with the testing agency's quality inspection report data; Compare the supplier's power distribution transformer production process data with the technical standard data for power distribution transformer inspection; Compare the supplier's power distribution transformer production process data with the bidding parameter data for the power distribution transformer inspection; The comparison rules are based on interval value judgment, which determines whether the data in the quality inspection report of the testing institution is within the range of the technical standard data for distribution transformers, whether the data in the quality inspection report of the testing institution is within the range of the bidding parameters data for distribution transformers, whether the production process data of distribution transformers is within the range of the technical standard data for distribution transformers, and whether the production process data of distribution transformers is within the range of the bidding parameters data for distribution transformers. S3: Blockchain Node Data Synchronization: Initiate consensus verification by using the blockchain to synchronize parameter information and verification results over a period of time; Blockchain nodes are divided into consensus nodes and parameter nodes, and the storage blocks within a node are divided into parameter blocks and verification blocks. Consensus nodes store verification blocks, while parameter nodes only store parameter blocks. There are markers in the blocks to identify the block type. S4: Screening of Suspicious Quality Inspection Reports: Screening of quality inspection reports corresponding to suspicious data that have been verified by S3; The method for screening suspicious quality inspection reports in step S4 includes two methods: a: Based on the median value of the range of test items in the bidding parameters, calculate the variance with the test item parameters in the test report, and sort the test reports corresponding to the variance from largest to smallest; b: Using the production parameter testing items as a benchmark, calculate the variance of the testing items in the testing report, and sort the testing reports corresponding to the variance from largest to smallest.

2. The method for judging the quality inspection results of distribution transformers based on blockchain according to claim 1, characterized in that: The distribution transformer quality inspection dataset in step S1 includes key state parameter data and production process data of the distribution transformer. The key state parameters of the distribution transformer include: DC insulation resistance measurement between windings and ground and between windings, insulation inspection of core and clamps, winding resistance measurement, voltage ratio measurement and connection group label verification, no-load loss and no-load current measurement, short-circuit impedance and load loss measurement, external withstand voltage test, induced withstand voltage test, partial discharge measurement, on-load tap changer test, pressure sealing test, temperature rise test, sound level measurement, line-end lightning full wave impulse test, line-end lightning chopped wave impulse test, short-circuit withstand capacity test, and pressure deformation test data.

3. The method for judging the quality inspection results of distribution transformers based on blockchain according to claim 2, characterized in that: Step S3 specifically includes: a: Initiating consensus involves the master node collecting parameter information and verification results over a period of time and sending them to all consensus nodes and parameter nodes, thus initiating a consensus request; b: Consensus data verification, that is, the nodes participating in the consensus verify the consensus requirements of the master node. If they pass, the consensus node will confirm the consensus information to the master node. c: After the master node, consensus node, and parameter node reach a consensus, the master node broadcasts a consensus confirmation message, publishes parameters or verifies data to the block and adds it to the block. After the node adds the block to the blockchain it maintains, it deletes the request information in the log according to the checkpoint protocol and starts the next round of consensus.

4. The method for judging the quality inspection results of distribution transformers based on blockchain according to claim 3, characterized in that: The on-chain automated comparison process is as follows: a: The testing personnel obtain public and private keys through system registration, formulate a smart contract for the quality testing report judgment method, and digitally sign it with their own private key. The digitally signed smart contract will be transmitted to the blockchain network. b: Solidified quality inspection report judgment method: The smart contract is transmitted to the blockchain network for unified verification. The contract is spread through the network and stored on each node of the blockchain. Once the consensus mechanism is triggered and activated, the smart contract is verified by the testing agency to verify the validity of the contract. After successful verification, a hash block ID is generated and quickly spread to the entire network. Other consensus nodes store the smart contract. c: Consensus result returned to the system: After consensus is reached, the hash block ID is returned to the system to determine the legality of the quality inspection report judgment method; d: Parameter node calls smart contract to execute automatically: The parameter node will periodically traverse the smart contract of each consensus node and automatically complete the entire quality inspection report judgment; e: Quality Inspection Report Judgment Process: When the smart contract execution conditions are met, the quality inspection report data and production process data to be judged are first read from the verification block and consistency verification is performed in the blockchain network. Then, the technical standard data and bidding parameter data are read from the parameter block and consistency verification is performed in the blockchain network. After completing the double consistency verification, the smart contract automatically completes the judgment of the quality inspection report data.

5. The method for judging the quality inspection results of distribution transformers based on blockchain according to claim 1, characterized in that: In step S4, when screening suspicious quality reports, two screening methods are considered together. Reports with large variances are more suspicious. Reports with large variances are selected for internal quality inspection by the power company. The parameters of the internal inspection reports are directly obtained through the distribution transformer testing equipment to determine whether the data of the internal quality inspection reports are within the range of the bidding parameters for distribution transformers. The above operation repeats the process of step S3 and is stored in the consensus node.

6. A blockchain-based distribution transformer quality inspection device, characterized in that: The detection device includes: at least one processor, at least one temperature sensor, at least one humidity sensor, at least one transmission interface, at least one memory, and at least one system bus; the above modules communicate with each other through the system bus. The processor has the following built-in features: The quality inspection report data receiving module is used to obtain key status parameter data of the distribution transformer secondary quality inspection equipment. The quality inspection report data standardization module is used to standardize the format of quality inspection report data according to the standard data format of quality inspection reports, so as to obtain the key status parameter data of the standardized quality inspection report. The quality inspection calculation module is used to perform deviation compensation calculation on the key state parameter data according to the set calculation rules for the distribution transformer inspection environment, based on the temperature and data of the environment in which the distribution transformer is located during quality inspection, and to obtain the processed key state parameter data results. The anomaly detection module determines the anomaly characteristics of the key state parameter data if the detected key state parameter data results are abnormal. The result transmission module is used for the on-chain storage of key state parameter results, temperature, and humidity data.

7. The blockchain-based distribution transformer quality inspection device according to claim 6, characterized in that: The quality inspection calculation module includes: a quality inspection calculation slave module, which is used to perform error compensation on the key state parameters of the distribution transformer according to the set calculation rules, temperature and humidity, and then perform calculation according to the smart contract comparison rules to obtain the calculated key state parameter data results.

8. The blockchain-based distribution transformer quality inspection device according to claim 6, characterized in that: The monitoring device is deployed at the source end of the distribution transformer testing equipment. When conducting secondary sampling and verification of the screened suspicious quality reports, the testing device can obtain the key status parameters of the test report in a timely manner, and perform error assessment on the key status parameters based on the temperature and humidity of the testing environment, and store the evidence on the blockchain.

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