Elevator detection terminal data transparentizing system based on block chain data sharing

Abstract

The invention relates to the technical field of data sharing, and discloses an elevator detection terminal data transparentizing system based on block chain data sharing, comprising: a data acquisition module for acquiring operation data of an elevator in real time; the data processing module is used for dividing the preprocessed operation data to generate a normal detection data set and an abnormal detection data set; the data encryption module is used for encrypting the normal detection data set and the abnormal detection data set respectively to obtain encrypted data; the block chain module constructs an alliance chain, uploads the encrypted data to a block chain network, and writes the encrypted data into a block through a consensus mechanism; and the application module analyzes the access request based on the alliance chain, determines the access authority of the user, and accesses the encrypted data based on the access authority. The transparency and safety of elevator detection data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing, and in particular to an elevator detection terminal data transparency system based on blockchain data sharing. Background Art

[0002] With the acceleration of urbanization, elevators, as indispensable vertical transportation equipment in high-rise buildings, have become increasingly important. Their safety and reliability are directly related to the safety of people's lives and property. However, traditional elevator inspection systems suffer from numerous shortcomings, such as severe data silos, opaque inspection processes, susceptibility to data tampering or falsification, and delayed information transmission. These issues not only hinder the timely detection and resolution of elevator faults but also increase the difficulty and cost of elevator safety management.

[0003] Specifically, elevator inspection data is currently collected and stored independently by various inspection agencies or maintenance units, lacking a unified data sharing platform. This makes it difficult to effectively integrate and utilize this data. Furthermore, because the data is not encrypted or the encryption strength is insufficient, there is a risk of data leakage and illegal tampering, which affects the data's credibility and usability. Furthermore, unclear management of access and usage permissions for elevator inspection data leads to frequent data abuse and misuse.

[0004] Therefore, achieving transparent, secure, and efficient sharing of elevator inspection data has become a crucial issue in improving elevator safety management. Blockchain technology, with its decentralized, tamper-proof, and traceable nature, offers new insights and approaches to addressing these challenges. Summary of the Invention

[0005] The purpose of this invention is to provide an elevator detection terminal data transparency system based on blockchain data sharing, aiming to solve the above problems.

[0006] The present invention provides an elevator detection terminal data transparency system based on blockchain data sharing, including:

[0007] A data acquisition module is configured to collect elevator operation data in real time;

[0008] a data processing module configured to preprocess the operation data, divide the preprocessed operation data into normal detection data and abnormal detection data, extract data from the normal detection data to generate a normal detection data set, and set the abnormal detection data as the abnormal detection data set;

[0009] a data encryption module, configured to encrypt the normal detection data set and the abnormal detection data set respectively to obtain encrypted data;

[0010] A blockchain module is configured to build a consortium chain, receive the encrypted data, upload the encrypted data to the blockchain network, and write the encrypted data into a block through a consensus mechanism;

[0011] The application module is configured to receive an access request from a user, analyze the access request based on the alliance chain, determine the user's access rights, and access the encrypted data based on the access rights.

[0012] Preferably, the operating data includes vibration data, speed data, acceleration data and door status data;

[0013] The data acquisition module includes:

[0014] Vibration sensor, used to collect elevator vibration data in real time;

[0015] A speed sensor is used to collect speed data of the elevator in real time and determine acceleration data of the elevator based on the speed data;

[0016] Door status data is used to collect elevator door status data in real time.

[0017] Preferably, the data processing module preprocesses the operation data and divides the preprocessed operation data into normal detection data and abnormal detection data, including:

[0018] When preprocessing the operating data, the preprocessing includes data cleaning, data standardization and data time synchronization;

[0019] Pre-set standard value ranges for each operating data type;

[0020] Comparing the operating data at the same collection time point with the standard value range, if both the operating data are within the standard value range, classifying the operating data at the collection time point as normal detection data;

[0021] Otherwise, the operating data at the collection time point is classified as anomaly detection data.

[0022] Preferably, the data processing module extracts the normal detection data to generate a normal detection data set, including:

[0023] Presetting a first interval duration and a standard deviation value range for each operating data type;

[0024] Extracting data from the normal detection data at intervals of a first interval duration, and determining a data difference corresponding to each operating data type between adjacent normal detection data;

[0025] Comparing the data difference with the standard deviation range, if the data difference is within the standard deviation range, then not adjusting the first interval duration, and setting the extracted normal detection data as the normal detection data set;

[0026] Otherwise, the first interval duration is adjusted to obtain a second interval duration, and the normal detection data is extracted according to the second interval duration to generate a normal detection data set.

[0027] Preferably, the data processing module adjusts the first interval duration to obtain a second interval duration, including:

[0028] Determine the maximum value in the standard deviation range for each running data type and set the maximum value as the reference value;

[0029] determining a difference ratio of each running data type according to the data difference and the reference value;

[0030] An average value of the difference ratios is calculated, and the first interval duration is adjusted according to the average value to obtain a second interval duration.

[0031] Preferably, the data processing module adjusts the first interval duration according to the average value to obtain the second interval duration, including:

[0032] Presetting a first preset average value and a second preset average value, wherein the first preset average value is smaller than the second preset average value;

[0033] setting an adjustment coefficient according to a relationship between the first preset average value, the second preset average value and the average value, and adjusting the first interval duration based on the adjustment coefficient to obtain a second interval duration;

[0034] If the average value is less than or equal to the first preset average value, the adjustment coefficient is set to the first adjustment coefficient A1, and the first interval duration is adjusted to obtain a second interval duration equal to the first interval duration × A1;

[0035] If the average value is greater than the first preset average value and less than the second preset average value, the adjustment coefficient is set to the second adjustment coefficient A2, and the first interval duration is adjusted to obtain a second interval duration equal to the first interval duration × A2;

[0036] If the average value is greater than or equal to the second preset average value, the adjustment coefficient is set to the third adjustment coefficient A3, and the first interval duration is adjusted to obtain a second interval duration of the first interval duration × A3; wherein 1.0>A1>A2>A3>0.5.

[0037] Preferably, the data encryption module encrypts the normal detection data set and the abnormal detection data set respectively to obtain encrypted data, including:

[0038] Generate a first random key using a random number generator, and encrypt the normal detection data set according to the first random key to obtain normal encrypted data;

[0039] Using a random number generator to generate a second random key, a public key, and a private key, wherein the public key and the private key are asymmetric keys, encrypting the second random key using the public key to generate a third key, and encrypting the anomaly detection dataset using the third key to obtain anomaly encrypted data;

[0040] The encrypted data includes normal encrypted data and abnormal encrypted data.

[0041] Preferably, the blockchain module constructs a consortium chain, receives the encrypted data, uploads the encrypted data to the blockchain network, and writes the encrypted data into a block through a consensus mechanism, including:

[0042] When building a consortium chain, the consortium chain includes elevator manufacturers, maintenance companies, inspection agencies and regulatory authorities;

[0043] When the encrypted data is uploaded to the blockchain network, an upload timestamp and a hash value of the previous block are generated at the same time, and the upload timestamp, hash value and the encrypted data are written into the block together.

[0044] Preferably, the application module receives an access request from a user, analyzes the access request based on the alliance chain, and determines the user's access rights, including:

[0045] According to the identity information in the user's access request, query the user identity information and corresponding access permission list pre-stored in the alliance chain;

[0046] If the identity information in the access request exists in the user identity information and the corresponding access permission list, determining the user's access permission according to the access permission list;

[0047] If the identity information in the access request does not exist in the user identity information and the corresponding access permission list, the access request is rejected.

[0048] Preferably, the application module accesses the encrypted data based on the access permission, including:

[0049] decrypting and accessing the encrypted data according to the access permission, and if the user has access permission to the normal detection data set, decrypting the normal encrypted data using a decryption key corresponding to the first random key to obtain a normal detection data set;

[0050] If the user has access rights to the anomaly detection dataset, the third key is first decrypted using the private key to obtain a second random key, and then the second random key is used to decrypt the anomaly encrypted data to obtain the anomaly detection dataset.

[0051] Compared with existing technologies, the present invention offers significant advantages in that, by building a blockchain-based data sharing system, all elevator inspection data is recorded on the blockchain, achieving full data traceability. Whether normal or abnormal inspection data, its source, processing process, and storage status can be clearly traced, enhancing data transparency and credibility. Elevator inspection data is encrypted using advanced encryption technology to ensure data security during transmission and storage. Furthermore, the immutability of blockchain technology ensures that encrypted data, once uploaded to the blockchain, cannot be illegally modified or deleted, effectively preventing the risk of data leakage and tampering. The consortium chain enables efficient data sharing and collaboration among elevator inspection agencies, maintenance units, and regulatory agencies on the same platform. A consensus mechanism ensures data authenticity and consistency, promoting the rapid flow of information and optimal resource allocation. The application module provides granular control over user access rights based on user access requests and pre-defined permission rules. Only users with the appropriate permissions can access specific encrypted data, effectively preventing data abuse and misuse.

[0052] The transparent and secure data support provided by this system provides strong support for intelligent elevator safety management. By analyzing and mining large amounts of elevator inspection data, potential safety hazards and failure patterns can be discovered in a timely manner, providing a scientific basis for preventive maintenance and fault prediction, thereby improving the level of elevator safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0054] Figure 1 This is a structural block diagram of an elevator detection terminal data transparency system based on blockchain data sharing in the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] like Figure 1 As shown, the present invention provides an elevator detection terminal data transparency system based on blockchain data sharing, including:

[0057] A data acquisition module is configured to collect elevator operation data in real time;

[0058] a data processing module configured to preprocess the operation data, divide the preprocessed operation data into normal detection data and abnormal detection data, extract data from the normal detection data to generate a normal detection data set, and set the abnormal detection data as the abnormal detection data set;

[0059] a data encryption module, configured to encrypt the normal detection data set and the abnormal detection data set respectively to obtain encrypted data;

[0060] A blockchain module is configured to build a consortium chain, receive the encrypted data, upload the encrypted data to the blockchain network, and write the encrypted data into a block through a consensus mechanism;

[0061] The application module is configured to receive an access request from a user, analyze the access request based on the alliance chain, determine the user's access rights, and access the encrypted data based on the access rights.

[0062] The present invention improves the transparency and security of elevator detection data. By collecting elevator operation data in real time and performing preprocessing and division, it is possible to accurately distinguish normal detection data from abnormal detection data, thereby generating corresponding data sets. The data encryption module encrypts the normal detection data set and the abnormal detection data set to ensure the security and privacy of the data. The introduction of the blockchain module constructs an alliance chain, uploads the encrypted data to the blockchain network, and writes it to the block through a consensus mechanism, thereby achieving data traceability and non-tamperability. The application module accesses the encrypted data based on the user's access request and access rights, thereby improving the transparency and credibility of the data. The system can effectively solve the problems of opacity and low security of elevator detection data, and provide strong guarantees for the safe operation of elevators.

[0063] In some embodiments of the present application, the operating data includes vibration data, speed data, acceleration data and door status data; the data acquisition module includes: a vibration sensor, used to collect the vibration data of the elevator in real time; a speed sensor, used to collect the speed data of the elevator in real time, and determine the acceleration data of the elevator based on the speed data; door status data, used to collect the door status data of the elevator in real time.

[0064] In some embodiments of the present application, the data processing module preprocesses the operation data and divides the preprocessed operation data into normal detection data and abnormal detection data, including: when preprocessing the operation data, the preprocessing includes data cleaning processing, data standardization processing and data time synchronization processing; pre-setting a standard value range for each type of operation data; comparing the operation data at the same acquisition time point with the standard value range, if the operation data are both within the standard value range, then the operation data at the acquisition time point is divided into normal detection data; otherwise, the operation data at the acquisition time point is divided into abnormal detection data.

[0065] It's understandable that preprocessing operational data, including data cleaning, standardization, and time synchronization, ensures data accuracy and consistency, providing a reliable foundation for subsequent data segmentation. Setting a standard value range for each operational data type and segmenting operational data based on this range accurately distinguishes normal and abnormal detection data, helping to promptly identify potential issues during elevator operation. Furthermore, segmenting operational data collected at the same time point as a whole ensures data integrity and consistency, preventing misjudgments or omissions.

[0066] In some embodiments of the present application, the data processing module extracts data from normal detection data to generate a normal detection data set, including: pre-setting a first interval duration and a standard deviation value range for each operating data type; extracting data from the normal detection data at each first interval duration to determine the data difference corresponding to each operating data type between each adjacent normal detection data; comparing the data difference with the standard deviation value range; if the data difference values ​​are all within the standard deviation value range, the first interval duration is not adjusted, and the normal detection data extracted from the data is set as the normal detection data set; otherwise, the first interval duration is adjusted to obtain a second interval duration, and the normal detection data is extracted according to the second interval duration to generate a normal detection data set.

[0067] The data processing module adjusts the first interval duration to obtain a second interval duration, including: determining the maximum value in the standard deviation value range of each operating data type and setting the maximum value as a reference value; determining the difference ratio of each operating data type based on the data difference and the reference value; calculating the average value of the difference ratios, and adjusting the first interval duration based on the average value to obtain the second interval duration.

[0068] It's understandable that by dynamically adjusting the data extraction interval, we can more accurately filter out test data from elevators operating normally, thereby constructing a more accurate normal test data set. This adaptive data extraction method not only improves data processing efficiency but also effectively avoids data bias caused by improperly set intervals, further enhancing the accuracy and reliability of elevator test data.

[0069] In some embodiments of the present application, the data processing module adjusts the first interval duration according to the average value to obtain a second interval duration, including: presetting a first preset average value and a second preset average value, the first preset average value being less than the second preset average value; setting an adjustment coefficient according to a relationship between the first preset average value, the second preset average value and the average value, and adjusting the first interval duration based on the adjustment coefficient to obtain a second interval duration; if the average value is less than or equal to the first preset average value, setting the adjustment coefficient to a first adjustment coefficient A1, and adjusting the first interval duration to obtain a second interval duration equal to first interval duration × A1; if the average value is greater than the first preset average value and the average value is less than the second preset average value, setting the adjustment coefficient to a second adjustment coefficient A2, and adjusting the first interval duration to obtain a second interval duration equal to first interval duration × A2; if the average value is greater than or equal to the second preset average value, setting the adjustment coefficient to a third adjustment coefficient A3, and adjusting the first interval duration to obtain a second interval duration equal to first interval duration × A3; wherein 1.0>A1>A2>A3>0.5.

[0070] It is understandable that by presetting different average value intervals and corresponding adjustment coefficients, the data extraction time interval can be more finely adjusted. When the average difference ratio is small, it indicates that the elevator operation data is relatively stable. In this case, the time interval for data extraction can be appropriately reduced to reduce the data processing volume and improve processing efficiency. When the average difference ratio is large, it indicates that the elevator operation data may fluctuate. In this case, the data extraction time interval needs to be significantly shortened to more accurately capture the elevator's operating status and ensure data accuracy and reliability. This dynamic adjustment strategy based on the average value interval makes the data extraction process more flexible and intelligent, can better adapt to changes in the actual elevator operation situation, and provide more accurate and reliable data support for elevator safety monitoring and maintenance.

[0071] In some embodiments of the present application, the data encryption module encrypts the normal detection data set and the abnormal detection data set respectively to obtain encrypted data, including: using a random number generator to generate a first random key, encrypting the normal detection data set according to the first random key to obtain normal encrypted data; using a random number generator to generate a second random key, a public key and a private key, the public key and private key are asymmetric keys, using the public key to encrypt the second random key to generate a third key, and using the third key to encrypt the abnormal detection data set to obtain abnormal encrypted data; the encrypted data includes normal encrypted data and abnormal encrypted data.

[0072] In this embodiment, a medium-strength symmetric encryption algorithm (such as AES-128) may be used to encrypt the normal detection data set, and a high-strength symmetric encryption algorithm (such as AES-256) may be used to encrypt the abnormal detection data set.

[0073] It's understandable that by using different encryption methods for the normal detection dataset and the anomaly detection dataset, data security and privacy can be further guaranteed. The normal detection dataset is encrypted using a first random key, a relatively simple and efficient method that meets the needs of daily data processing. The anomaly detection dataset, on the other hand, uses a more complex asymmetric key encryption method. The public key is used to encrypt the second random key before encrypting the anomaly detection dataset. This method significantly improves data security and prevents the leakage of sensitive data. This differentiated encryption strategy ensures both data security and processing efficiency, providing more reliable protection for the transmission and storage of elevator detection data.

[0074] In some embodiments of the present application, the blockchain module builds a consortium chain, receives the encrypted data, uploads the encrypted data to the blockchain network, and writes the encrypted data into the block through a consensus mechanism, including: when building the consortium chain, the consortium chain includes elevator manufacturers, maintenance companies, inspection agencies and regulatory authorities; when uploading the encrypted data to the blockchain network, an upload timestamp and a hash value of the previous block are generated at the same time, and the upload timestamp, hash value and the encrypted data are written into the block together.

[0075] It's clear that by building a consortium chain, uploading encrypted elevator inspection data to the blockchain network and leveraging a consensus mechanism to ensure data security, traceability, and immutability are achieved. Participants in the consortium chain include elevator manufacturers, maintenance companies, inspection agencies, and regulatory authorities. This multi-party participation helps enhance the credibility and transparency of the data. During the data upload process, an upload timestamp and a hash value of the previous block are generated and written into the block along with the encrypted data, further enhancing data integrity and credibility.

[0076] In some embodiments of the present application, the application module receives an access request from a user, analyzes the access request based on the alliance chain, and determines the user's access rights, including: querying the user identity information and the corresponding access rights list pre-stored in the alliance chain based on the identity information in the user's access request; if the identity information in the access request exists in the user identity information and the corresponding access rights list, determining the user's access rights based on the access rights list; if the identity information in the access request does not exist in the user identity information and the corresponding access rights list, rejecting the access request.

[0077] As you can see, the application module's intelligent analysis of user access requests enables permission control based on the consortium blockchain. The system automatically queries and matches access permissions based on user identity information, ensuring data access security. For legitimate users, the system provides data access services tailored to their permission level, protecting data privacy while meeting the reasonable data needs of different users. For illegitimate users, the system denies access requests, effectively preventing data leakage and misuse.

[0078] In some embodiments of the present application, the application module accesses the encrypted data based on the access permission, including: decrypting and accessing the encrypted data according to the access permission, if the user has access permission to the normal detection data set, using the decryption key corresponding to the first random key to decrypt the normal encrypted data to obtain the normal detection data set; if the user has access permission to the abnormal detection data set, first using the private key to decrypt the third key to obtain the second random key, and then using the second random key to decrypt the abnormal encrypted data to obtain the abnormal detection data set.

[0079] As you can understand, by enabling the application module to decrypt and access encrypted data based on access rights, fine-grained control and on-demand access are achieved. For users with access to the normal detection dataset, the system directly provides the decrypted normal detection dataset, allowing them to understand the daily operation status of the elevator. For users with access to the abnormal detection dataset, such as elevator maintenance personnel or management personnel, the system provides the abnormal detection dataset through an additional decryption step, helping them identify and resolve potential issues in elevator operation.

[0080] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0084] 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 it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An elevator detection terminal data transparency system based on blockchain data sharing, characterized in that: include: A data acquisition module is configured to collect elevator operation data in real time; a data processing module configured to preprocess the operation data, divide the preprocessed operation data into normal detection data and abnormal detection data, extract data from the normal detection data to generate a normal detection data set, and set the abnormal detection data as the abnormal detection data set; a data encryption module, configured to encrypt the normal detection data set and the abnormal detection data set respectively to obtain encrypted data; A blockchain module is configured to build a consortium chain, receive the encrypted data, upload the encrypted data to the blockchain network, and write the encrypted data into a block through a consensus mechanism; The application module is configured to receive an access request from a user, analyze the access request based on the alliance chain, determine the user's access rights, and access the encrypted data based on the access rights.

2. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 1 is characterized in that: The operating data includes vibration data, speed data, acceleration data and door status data; The data acquisition module includes: Vibration sensor, used to collect elevator vibration data in real time; A speed sensor is used to collect speed data of the elevator in real time and determine acceleration data of the elevator based on the speed data; Door status data is used to collect elevator door status data in real time.

3. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 1 is characterized in that: The data processing module preprocesses the operation data and divides the preprocessed operation data into normal detection data and abnormal detection data, including: When preprocessing the operating data, the preprocessing includes data cleaning, data standardization and data time synchronization; Pre-set standard value ranges for each operating data type; Comparing the operating data at the same collection time point with the standard value range, if both the operating data are within the standard value range, classifying the operating data at the collection time point as normal detection data; Otherwise, the operating data at the collection time point is classified as anomaly detection data.

4. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 1 is characterized in that: The data processing module extracts the normal detection data to generate a normal detection data set, including: Presetting a first interval duration and a standard deviation value range for each operating data type; Extracting data from the normal detection data at intervals of a first interval duration, and determining a data difference corresponding to each operating data type between adjacent normal detection data; Comparing the data difference with the standard deviation range, if the data difference is within the standard deviation range, then not adjusting the first interval duration, and setting the extracted normal detection data as the normal detection data set; Otherwise, the first interval duration is adjusted to obtain a second interval duration, and the normal detection data is extracted according to the second interval duration to generate a normal detection data set.

5. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 4 is characterized in that: The data processing module adjusts the first interval duration to obtain a second interval duration, including: Determine the maximum value in the standard deviation range for each running data type and set the maximum value as the reference value; determining a difference ratio of each running data type according to the data difference and the reference value; An average value of the difference ratios is calculated, and the first interval duration is adjusted according to the average value to obtain a second interval duration.

6. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 5 is characterized in that: The data processing module adjusts the first interval duration according to the average value to obtain a second interval duration, including: Presetting a first preset average value and a second preset average value, wherein the first preset average value is smaller than the second preset average value; setting an adjustment coefficient according to a relationship between the first preset average value, the second preset average value and the average value, and adjusting the first interval duration based on the adjustment coefficient to obtain a second interval duration; If the average value is less than or equal to the first preset average value, the adjustment coefficient is set to the first adjustment coefficient A1, and the first interval duration is adjusted to obtain a second interval duration equal to the first interval duration × A1; If the average value is greater than the first preset average value and less than the second preset average value, the adjustment coefficient is set to the second adjustment coefficient A2, and the first interval duration is adjusted to obtain a second interval duration equal to the first interval duration × A2; If the average value is greater than or equal to the second preset average value, the adjustment coefficient is set to the third adjustment coefficient A3, and the first interval duration is adjusted to obtain a second interval duration of the first interval duration × A3; wherein 1.0>A1>A2>A3>0.

5.

7. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 1 is characterized in that: The data encryption module encrypts the normal detection data set and the abnormal detection data set respectively to obtain encrypted data, including: Generate a first random key using a random number generator, and encrypt the normal detection data set according to the first random key to obtain normal encrypted data; Using a random number generator to generate a second random key, a public key, and a private key, wherein the public key and the private key are asymmetric keys, encrypting the second random key using the public key to generate a third key, and encrypting the anomaly detection dataset using the third key to obtain anomaly encrypted data; The encrypted data includes normal encrypted data and abnormal encrypted data.

8. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 7 is characterized in that: The blockchain module builds a consortium chain, receives the encrypted data, uploads the encrypted data to the blockchain network, and writes the encrypted data into the block through a consensus mechanism, including: When building a consortium chain, the consortium chain includes elevator manufacturers, maintenance companies, inspection agencies and regulatory authorities; When the encrypted data is uploaded to the blockchain network, an upload timestamp and a hash value of the previous block are generated at the same time, and the upload timestamp, hash value and the encrypted data are written into the block together.

9. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 8 is characterized in that: The application module receives a user's access request, analyzes the access request based on the alliance chain, and determines the user's access rights, including: According to the identity information in the user's access request, query the user identity information and corresponding access permission list pre-stored in the alliance chain; If the identity information in the access request exists in the user identity information and the corresponding access permission list, determining the user's access permission according to the access permission list; If the identity information in the access request does not exist in the user identity information and the corresponding access permission list, the access request is rejected.

10. The elevator detection terminal data transparency system based on blockchain data sharing according to claim 9 is characterized in that: The application module accesses the encrypted data based on the access permission, including: decrypting and accessing the encrypted data according to the access permission, and if the user has access permission to the normal detection data set, decrypting the normal encrypted data using a decryption key corresponding to the first random key to obtain a normal detection data set; If the user has access rights to the anomaly detection dataset, the third key is first decrypted using the private key to obtain a second random key, and then the second random key is used to decrypt the anomaly encrypted data to obtain the anomaly detection dataset.

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