Building labor service identity management method, system and equipment based on block chain, and medium

Abstract

The invention provides a block chain-based building labor service identity management method, system and device, and a medium, and the method comprises the steps: obtaining the identity information of a building labor service worker, generating a digital identity label of the building labor service worker, and carrying out the digital identity label of the building labor service worker in a block chain network; binding the digital identity label with the working behavior data and the management block corresponding to the building labor service personnel; reading the working behavior data based on the digital identity label, and determining a working resume chain according to the working behavior data; according to a scoring strategy, scoring the work resume chain to obtain a comprehensive labor service score; performing data sensitive grade division on the identity information, the work resume chain and the comprehensive labor service score, and performing encryption processing on the data of each sensitive grade to obtain graded encrypted data; and storing the hierarchical encrypted data to a management block based on the digital identity label to form a hierarchical evidence storage chain of the building labor service personnel. And the safety, the efficiency and the reliability of identity management of the building labor service personnel are improved.

Description

Technical Field

[0001] This application relates to the field of digital management technology, and in particular to a blockchain-based method, system, device and medium for managing the identity of construction workers. Background Technology

[0002] Construction labor identity management is a key support for a credible labor credit system and improving the transparency and effectiveness of labor management in the industry. Currently, construction labor identity management typically involves employers independently collecting and storing construction worker identity data, including manual records of working hours, salary information, and skills certification data. Work history is maintained in scattered spreadsheets or paper files, and labor credit assessment relies on manual experience or calculations based on single-dimensional static indicators. However, with the rapid development of digital technology in the construction industry, centralized database-based labor identity management systems have emerged. These systems centrally manage worker identity information and work records through employers or third-party platforms, enabling the archiving of basic information about construction workers.

[0003] However, the current centralized management approach still has the following significant problems: First, centrally stored identity information and work data are easily tampered with or leaked, posing risks to data security and privacy. Second, the various data of construction workers are not shared between different management systems, creating a risk of data falsification. Third, it is difficult to conduct objective and quantitative comprehensive assessments of construction workers, failing to reflect their talent value. Finally, the ability to trace various data of construction workers is insufficient, making it impossible to reconstruct their behavioral patterns. Therefore, it is difficult to achieve secure, reliable, and efficient identity management for construction workers. Summary of the Invention

[0004] This application discloses a blockchain-based method, system, device, and medium for managing the identity of construction workers, which addresses the technical problem of the inability to securely, efficiently, and reliably manage the identity of construction workers.

[0005] This application provides a blockchain-based method for managing the identity of construction workers. The method includes: acquiring the identity information of construction workers, generating a digital identity identifier for the construction workers based on the identity information, and binding the digital identity identifier to the work behavior data and management block corresponding to the construction workers in a blockchain network; reading the work behavior data based on the digital identity identifier and determining the work history chain of the construction workers based on the work behavior data; scoring the work history chain according to a preset scoring strategy to obtain a comprehensive labor score for the construction workers; classifying the identity information, the work history chain, and the comprehensive labor score into data sensitivity levels, and encrypting the data at each sensitivity level to obtain hierarchically encrypted data; and storing the hierarchically encrypted data in the management block based on the digital identity identifier to form a hierarchical evidence storage chain for the construction workers.

[0006] In one embodiment of this application, determining the work history chain of the construction workers based on the work behavior data includes: cross-validation of the work behavior data by a labor supervision node, an employment management node, a labor wage settlement node, and a skills certification node according to preset consensus rules; if the cross-validation passes and a historical work history chain exists in the management block, the work behavior data is linked to the end of the historical work history chain to generate a new work history chain; if the cross-validation passes and no historical work history chain exists in the management block, the current work history chain is generated based on the work behavior data; wherein, the blockchain network includes the labor supervision node and the employment management node, the labor wage settlement node, and the skills certification node that provide the work behavior data, and the cross-validation includes source legality verification, time consistency verification, and logical rationality verification.

[0007] In one embodiment of this application, the step of scoring the work history chain according to a preset scoring strategy to obtain the comprehensive labor score of the construction worker includes: determining multi-dimensional data values ​​of the construction worker based on the work history chain, wherein the multi-dimensional data values ​​include attendance rate, skill level, and wage payment compliance; standardizing the attendance rate, skill level, and wage payment compliance according to the preset maximum and minimum values ​​of each dimension data value; determining the target weight of each dimension data value according to the basic weight of each dimension data value and a pre-constructed time decay function of the basic weight, wherein the basic weight comes from an external regulatory database; and performing a weighted calculation on the standardized attendance rate, skill level, and wage payment compliance according to the target weight of each dimension data value to obtain the comprehensive labor score.

[0008] In one embodiment of this application, the step of classifying the identity information, the work history chain, and the comprehensive labor score into data sensitivity levels, and encrypting the data at each sensitivity level, includes: constructing data sensitivity levels based on data privacy and data security levels, wherein the data sensitivity levels include a first sensitivity level, a second sensitivity level, and a third sensitivity level, with data privacy and data security levels respectively ranging from high to low; classifying the identity information into the first sensitivity level, the employment behavior data and salary details into the second sensitivity level, and the skill certification data and the comprehensive labor score into the third sensitivity level, wherein the work history chain includes the employment behavior data, the salary details, and the skill certification data; and encrypting the data at each sensitivity level according to a preset mapping relationship, wherein the mapping relationship represents the correspondence between data at different sensitivity levels and encryption strategies, and the security strength of the encryption strategy increases with the increase of the sensitivity level.

[0009] In one embodiment of this application, after obtaining the comprehensive labor score of the construction worker, the method further includes: matching the comprehensive labor score, the skill level, and the requirements of each position in the job database to generate a job recommendation list, and storing the job recommendation list in the management block based on the digital identity; comparing the comprehensive labor score with a preset scoring threshold, and if the comprehensive labor score is less than the scoring threshold, generating a skills training path for the construction worker based on the work history chain, and pushing the skills training path to the construction worker's terminal based on the digital identity, so as to train the construction worker according to the skills training path, obtain training results, and update the skills certification data in the work history chain according to the training results, and storing the updated skills certification data in the skills certification node in the blockchain network.

[0010] In one embodiment of this application, after determining the work history chain of the construction workers based on the work behavior data, the method further includes: detecting violations in the work history chain according to a preset violation database and preset violation detection rules; if a violation is detected, determining a violation risk value according to a preset violation risk mapping rule; if the violation risk value is greater than a preset risk threshold, generating a risk warning based on the violation, and storing the risk warning in the labor supervision node of the blockchain network based on the digital identity identifier.

[0011] In one embodiment of this application, after detecting violations in the work history chain, the method further includes: if a violation is detected, lowering the overall labor score according to the type of violation; generating a labor score repair record chain based on the lowered overall labor score; and linking the labor score repair record chain to the end of the work history chain based on the digital identity identifier.

[0012] This application also provides a blockchain-based construction labor identity management system, the system comprising: an identity information processing module, used to acquire the identity information of construction laborers, generate digital identity identifiers for the construction laborers based on the identity information, and bind the digital identity identifiers to the work behavior data and management blocks corresponding to the construction laborers in a blockchain network; a work history management module, used to read the work behavior data based on the digital identity identifiers, and determine the work history chain of the construction laborers based on the work behavior data; a comprehensive labor scoring module, used to score the work history chain according to a preset scoring strategy to obtain the comprehensive labor score of the construction laborers; a data hierarchical encryption module, used to classify the identity information, the work history chain, and the comprehensive labor score into data sensitivity levels, and encrypt the data of each sensitivity level to obtain hierarchically encrypted data; and a hierarchical data storage module, used to store the hierarchically encrypted data in the management blocks based on the digital identity identifiers to form a hierarchical evidence storage chain for the construction laborers.

[0013] This application also provides an electronic device, including: a processor; and a storage device for storing a program, which, when executed by the processor, causes the electronic device to implement the blockchain-based construction labor identity management method as described above.

[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform the blockchain-based construction labor identity management method as described above.

[0015] The beneficial effects of this application are as follows: This application provides a blockchain-based method, system, device, and medium for managing the identity of construction workers. First, it acquires the identity information of construction workers and generates digital identity identifiers based on this information. Then, in the blockchain network, it binds these digital identity identifiers to the corresponding work behavior data and management blocks of the construction workers. Next, based on the digital identity identifiers, it reads the work behavior data and determines the work history chain of the construction workers, achieving automatic integration and chained storage of the work behavior data, breaking down information silos, eliminating the risk of data forgery, and finally, scoring the work history chain according to a preset scoring strategy. Obtaining a comprehensive labor score for construction workers enables an objective and quantitative comprehensive assessment of them, improving the automation of labor credit evaluation. Subsequently, the identity information, work history chain, and comprehensive labor score are classified into data sensitivity levels, and the data at each sensitivity level is encrypted separately, resulting in hierarchically encrypted data. This hierarchical encryption mechanism strengthens the privacy protection of sensitive data, preventing data tampering or leakage. Finally, based on digital identity identifiers, the hierarchically encrypted data is stored in the management block, forming a hierarchical evidence chain for construction workers. This ensures the integrity and traceability of the overall data, thereby guaranteeing the security, efficiency, and reliability of construction worker identity management. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 This is a flowchart illustrating an exemplary embodiment of the present application of a blockchain-based method for managing the identity of construction workers; Figure 2 This is a block diagram illustrating a blockchain-based construction labor identity management system, as shown in an exemplary embodiment of this application. Figure 3 This is a block diagram illustrating a specific blockchain-based construction labor identity management system, as shown in an exemplary embodiment of this application. Figure 4 This is an exemplary embodiment of the present application illustrating an operation panel diagram of a blockchain-based construction labor identity management system; Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0018] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0021] With the rapid development of digital technology in the construction industry, labor identity management systems based on centralized databases have emerged. These systems centrally manage the identity information and work records of construction workers through employing companies or third-party platforms, achieving basic information archiving for construction workers. However, the inventors of this application have found that the current centralized management method still has the following significant problems: First, centrally stored identity information and work data are easily tampered with or leaked, posing risks to data security and privacy; second, various data information of construction workers is not shared between different systems, creating a risk of data forgery; third, it is difficult to conduct objective and quantitative comprehensive evaluations of construction workers, failing to reflect their talent value; and finally, the ability to trace various data information of construction workers is insufficient, making it impossible to reconstruct the behavioral trajectory of construction workers. Therefore, it is difficult to achieve secure, efficient, and reliable identity management for construction workers.

[0022] Therefore, this application provides a blockchain-based method for managing the identity of construction workers. This embodiment illustrates the application of this method to a terminal, but it is understood that the method can also be applied to a server, or to a system including both a terminal and a server, and implemented through the interaction between the terminal and the server. In the implementation environment, its hardware architecture consists of the following parts: a terminal device layer, including mobile terminals held by construction workers, such as smartphones or smart badges with dedicated applications installed, as well as gates, positioning sensors, and electronic badges deployed on-site, used to collect the identity information, work behavior data, and receive notifications of construction workers; a blockchain node layer, which is a distributed network consisting of employment management nodes based on the employing company's server, labor payment settlement nodes based on the bank or financial institution's server, skill certification nodes based on the training institution's server, and labor supervision nodes based on the government's regulatory server, achieving data synchronization and cross-validation through a consensus mechanism; and a smart contract server, which independently runs a scoring algorithm and a hierarchical encryption engine, dynamically processes sensitive data, and executes differentiated encryption strategies. In application scenarios, when construction projects require fully automated management of the entire labor process, terminal devices collect the identity information of construction workers in real time, generate on-chain digital identity identifiers after de-identification and encryption, and simultaneously upload employment behavior data such as working hours and operation logs to the blockchain node layer. The employment management node, in collaboration with the supervision node, verifies the legality of the data and constructs a Merkle tree structured work history chain. Smart contracts call dynamic weight factors to calculate a comprehensive labor score, and trigger job recommendations or generate skills training paths based on the score results, pushing them to the terminal. In the payroll settlement scenario, smart contracts automatically calculate wages and call bank interfaces to pay individuals, while processing data according to sensitivity levels. For example, payroll details are encrypted using dynamic threshold signatures. If an anomaly is detected, a risk warning is automatically generated and stored in the labor supervision node, realizing a closed-loop management of the entire chain from identity authentication to settlement risk control. Among them, smart contracts are automatically executed programs deployed in the blockchain network, triggering operations through preset code logic without human intervention.

[0023] Please see Figure 1 , Figure 1 This is a flowchart illustrating a blockchain-based method for managing the identity of construction workers, as shown in an exemplary embodiment of this application. Figure 1 As shown, in an exemplary embodiment, the blockchain-based construction labor identity management method includes at least steps S110 to S150, which are detailed below: Step S110: Obtain the identity information of construction workers, generate digital identity identifiers for construction workers based on the identity information, and bind the digital identity identifiers to the work behavior data and management blocks corresponding to the construction workers in the blockchain network.

[0024] The identity information consists of a dataset containing the construction workers' names, ID numbers, biometric features (such as facial images), job types, bank accounts, and contact information; the digital identity identifier is a unique, irreversible string generated based on a hash algorithm, such as SHA-256 (Secure Hash Algorithm 256-bit), which is bound to the identity information to form a verifiable credential; the work behavior data consists of multi-dimensional dynamic information of the construction workers collected through blockchain nodes; and the management block is an identity management block that corresponds independently to each construction worker.

[0025] In this embodiment, the identity information of construction workers can be collected through mobile terminals or on-site terminals. Then, a smart contract is invoked to generate work behavior data and digital identity tokens bound to the management blocks corresponding to the construction workers. The digital identity tokens include timestamps, blockchain node signatures, and encryption key indexes, forming traceable and privacy-controlled digital identity credentials.

[0026] Step S120: Based on the digital identity, read the work behavior data and determine the work history chain of the construction workers according to the work behavior data.

[0027] Among them, the work history chain is a time-series-based chain data structure that represents the work behavior data of construction workers and is an immutable data record.

[0028] In this embodiment, the corresponding behavioral data can be retrieved in real time based on the digital identity identifier through the employment management node, labor payment settlement node and skill certification node in the blockchain network. The labor supervision node can work with other nodes to execute preset consensus rules, such as PBFT (Practical Byzantine Fault Tolerance) or Raft algorithm (an algorithm for achieving consistency in distributed systems), to cross-verify the legality of the data source, time consistency and logical rationality. After verification, the data is encapsulated into Merkle tree leaf nodes in the order of timestamps, generating a new work history chain and linking it to the end of the existing work history chain corresponding to the digital identity identifier. If it is the first time it is generated, the current work history chain is created to achieve incremental and tamper-proof storage of the work history data.

[0029] Step S130: According to the preset scoring strategy, the work history chain is scored to obtain the comprehensive labor score of the construction laborers.

[0030] The pre-defined scoring strategy is embedded in the smart contract in the form of a calculation engine, which includes weighting factors that are dynamically adjusted based on construction labor industry standards (such as weighting coefficients for attendance rate, skill level, and wage payment compliance). The comprehensive labor score is a numerical credit indicator calculated based on the work history chain data according to the scoring strategy, which is used to quantify the overall performance of laborers.

[0031] In this embodiment, a blockchain network, specifically an employment management node or a labor supervision node, can trigger a smart contract to invoke a preset scoring strategy (such as a model based on weighted summation or machine learning algorithms). Dynamic weighting factors are applied to perform real-time calculations on multi-dimensional data in the work history chain. These dynamic weighting factors obtain the latest standards from an external regulatory database (such as an industry regulatory platform) through a smart contract interface and are automatically updated. The weight values ​​can be dynamically optimized according to policy changes. The calculation process includes parsing the Merkle tree structure of the work history chain, extracting behavioral data fields (such as attendance rate, skill level, and salary payment compliance), applying weighting factors for weighted summation, and generating a comprehensive labor score (0-100 points), thus achieving automation, real-time processing, and verifiability of the scoring.

[0032] Step S140: Divide the identity information, work history chain and comprehensive labor score into data sensitivity levels, and encrypt the data of each sensitivity level to obtain hierarchical encrypted data.

[0033] Among them, the sensitivity level is a pre-defined multi-level classification rule; the data of each sensitivity level is encrypted separately after classification, which means that differentiated encryption rules are adopted for data of different sensitivity levels; the hierarchical encrypted data is an irreversible ciphertext set formed after hierarchical encryption, which preserves the data correlation but blocks unauthorized access paths.

[0034] In this embodiment, the metadata structure of identity information, work history chain, and comprehensive labor score can be parsed through smart contracts. The data can be automatically classified according to its sensitivity level, and the encryption engine can be called to perform differentiated processing. That is, different encryption methods are used to generate hierarchical encrypted data for data with different sensitivity levels. The hierarchical encrypted data can be aggregated according to digital identity identifiers to form hierarchical encrypted data packets that can be accessed independently but are securely isolated, providing structured input for the subsequent construction of hierarchical evidence storage chain.

[0035] Step S150: Based on digital identity identifiers, hierarchical encrypted data is stored in the management block to form a hierarchical evidence storage chain for construction workers.

[0036] Among them, the hierarchical evidence storage chain is a dedicated chain structure created based on digital identity identifiers. It uses Merkle trees to organize encrypted data blocks, realizing an evidence storage system that is independently stored according to sensitivity level, traceable, and tamper-proof.

[0037] In this embodiment, a smart contract is invoked based on the digital identity identifier to parse the hierarchical structure of the tiered encrypted data. A storage request is then initiated through a blockchain network, specifically through a labor management node or a labor supervision node. The smart contract verifies the validity of the digital identity identifier (e.g., by checking the hash signature and timestamp), divides the tiered encrypted data into independent data blocks according to its hierarchy, and stores them in the management block, forming a tiered evidence storage chain. The management block anchors the hierarchical data using Merkle root hashes, ensuring that the data is immutable and traceable after storage. Authorized nodes can query the data as needed based on the digital identity identifier. This hierarchical access control achieves efficient data storage and strengthens data privacy protection.

[0038] The aforementioned blockchain-based construction worker identity management method acquires the identity information of construction workers and generates digital identity identifiers based on this information. Within the blockchain network, these digital identity identifiers are bound to the corresponding work behavior data and management blocks of each construction worker. Based on the digital identity identifiers, the method reads the work behavior data and determines the work history chain of each construction worker. This achieves automatic integration and chained storage of the work behavior data, breaking down information silos, eliminating the risk of data forgery, and scoring the work history chain according to a preset scoring strategy to obtain the comprehensive labor service record of each construction worker. The scoring system enables an objective and quantitative comprehensive assessment of construction workers, enhancing the automation of labor credit evaluation. It categorizes identity information, work history chains, and comprehensive labor scores into data sensitivity levels, and encrypts data at each sensitivity level to obtain tiered encrypted data. This tiered encryption mechanism strengthens the privacy protection of sensitive data, preventing tampering or leakage. Based on digital identity identifiers, the tiered encrypted data is stored in a management block, forming a tiered evidence chain for construction workers. This ensures the integrity and traceability of the overall data, thereby guaranteeing the security, efficiency, and reliability of construction worker identity management.

[0039] In one possible embodiment, before obtaining the identity information of construction workers, the process includes: constructing a blockchain network, which is a distributed blockchain network, including employment management nodes, labor payment settlement nodes, skills certification nodes, and labor supervision nodes.

[0040] The distributed blockchain network is a decentralized ledger architecture based on a consortium blockchain model, where multiple participating nodes jointly maintain data consistency and immutability. The labor management node is responsible for collecting and managing labor behavior data such as work hour records, work location data, and operation logs of construction workers, used for real-time tracking of labor behavior. The labor wage settlement node handles wage calculation and disbursement, automatically calculating wage details through smart contracts to ensure transparent settlement. The skills certification node stores and verifies the skills certification data of construction workers, used to record and update their skill level information. The labor supervision node performs supervisory functions, monitoring compliance, detecting violations, and generating risk warnings to ensure that labor activities meet industry standards.

[0041] In addition, the employment management node is operated by the construction company and is responsible for collecting employment information; the labor payment settlement node is operated by the bank or financial institution and handles the payment interface; the skills certification node is operated by the third-party certification body and stores skills data; the labor supervision node is operated by the government regulatory department and performs supervision functions; the nodes synchronize and cross-validate data through preset consensus rules to ensure the distributed nature of the network and data integrity. At the same time, the node communication protocol is configured to realize real-time data interaction and initialize smart contracts to embed subsequent management logic, forming an immutable and highly available blockchain infrastructure to support the automated operation of subsequent steps.

[0042] It should be noted that the digital identity token serves as the unique encrypted credential for construction workers in the blockchain network. It can be used to retrieve related data across nodes, that is, to read employment behavior data such as working hours records, work location data and operation logs from the employment management node, to read salary details from the labor payment settlement node, and to read skill certification data from the skill certification node, thereby obtaining multi-dimensional work behavior data.

[0043] In one possible embodiment, before generating digital identities for construction workers based on identity information, the method further includes: de-identifying and encrypting the identity information.

[0044] As one possible implementation, the identity information of construction workers can be collected via mobile devices or on-site terminals and transmitted to edge computing nodes for de-identification. For example, a k-anonymization model can be used to generalize fields such as ID numbers, and the Paillier algorithm (a homomorphic encryption algorithm) can be applied to critical data such as bank accounts to generate ciphertext, thereby ensuring the immutability of identity information and basic privacy protection. After the de-identified and encrypted data is verified through node consensus, it can be stored in the management block using a Merkle tree structure.

[0045] In one embodiment, determining the work history chain of construction workers based on work behavior data includes: cross-validation of the work behavior data by multiple nodes, including labor supervision nodes, employment management nodes, labor wage settlement nodes, and skill certification nodes, according to preset consensus rules; if the cross-validation passes and a historical work history chain exists in the management block, the work behavior data is linked to the end of the historical work history chain to generate a new work history chain; if the cross-validation passes and no historical work history chain exists in the management block, the current work history chain is generated based on the work behavior data; wherein, the blockchain network includes labor supervision nodes and employment management nodes, labor wage settlement nodes, and skill certification nodes that provide work behavior data, and the cross-validation includes source legality verification, time consistency verification, and logical rationality verification.

[0046] In this embodiment, a labor supervision node, in conjunction with an employment management node, a labor payment settlement node, and a skills certification node, performs multi-node cross-verification of work behavior data according to preset consensus rules. Verification focuses on the legality of the data source (e.g., confirming node signatures and permissions), time consistency (e.g., verifying the continuity of operation timestamps), and logical rationality (e.g., whether the salary amount matches the work hour records). If data fails verification, it is discarded; only the verified data is linked to the end of the historical work history chain in chronological order to generate a new work history chain, or the current work history chain is generated solely based on the verified data. This process leverages the distributed nature of blockchain and utilizes multi-node cross-verification to verify the legality of the work behavior data source, logical consistency, and logical rationality, effectively avoiding the risk of work behavior data being forged.

[0047] In one embodiment, a comprehensive labor score for construction workers is obtained by scoring the work history chain according to a preset scoring strategy. This includes: determining multi-dimensional data values ​​for construction workers based on the work history chain, wherein the multi-dimensional data values ​​include attendance rate, skill level, and wage payment compliance; standardizing the attendance rate, skill level, and wage payment compliance according to the preset maximum and minimum values ​​of each dimension data value; determining the target weight of each dimension data value according to the base weight of each dimension data value and the time decay function of the pre-constructed base weight, wherein the base weight comes from an external regulatory database; and weighting the standardized attendance rate, skill level, and wage payment compliance according to the target weight of each dimension data value to obtain the comprehensive labor score.

[0048] It should be noted that the work history chain is based on work behavior data, which includes employment behavior data such as work hour records, work location data, and operation logs from the employment management node, salary details from the labor payment settlement node, and skill certification data from the skill certification node. Therefore, based on the work history chain, multi-dimensional data values ​​of construction workers can be determined, namely attendance rate (determined based on employment behavior data), skill level (determined based on skill certification data), and salary payment compliance (determined based on salary details). Specifically, smart contracts can determine the skill level of construction workers, such as basic, intermediate, and advanced, based on skill certification data by matching a skill standard library.

[0049] In this embodiment, attendance rate, skill level, and wage payment compliance are standardized based on the preset maximum and minimum values ​​of each dimension's data, eliminating dimensional differences and facilitating subsequent scoring calculations. Target weights for each dimension's data are determined based on the base weights of the data values ​​from an external regulatory database and a pre-constructed time decay function for these base weights. The base weights are the standard weight coefficients for attendance rate, skill level, and wage payment compliance in the construction labor industry. A time decay factor is introduced to strengthen the influence of recent data. Target weights are recalculated based on these base weights, making them more relevant to the current situation. This allows the calculation of the comprehensive labor service score to overcome the limitations of static weights, significantly improving the timeliness and accuracy of credit assessment.

[0050] For example, the calculation formula for standardization is: Equation (1) in, This refers to any dimension, including attendance rate, skill level, and payroll compliance. Represents the standardized data value of any specific dimension (range 0-1); Represents the data value of any specific dimension before standardization; This represents the minimum value of any specific dimension (e.g., the minimum attendance rate is 0%). This represents the maximum value of any specific dimension (e.g., the maximum attendance rate is 100%).

[0051] For example, the formula for calculating the target weight is: Equation (2) in, This refers to any dimension, including attendance rate, skill level, and payroll compliance. The target weight represents the data value of any specific dimension (e.g., the base weight of attendance rate is 0.35). The basic weight representing the data value of any specific dimension; This represents the time decay function. After calculating the target weights, the target weights for each dimension are normalized so that their sum equals 1.

[0052] For example, the expression for the time decay function is: Equation (3) Here, t represents the number of months since the data for any specific dimension was last recorded, ensuring that the weight of data from the last 3 months is increased by 20%, while the weight of historical data from more than 3 months ago is reduced exponentially. Of course, this exemplary embodiment does not limit the specific number of months, the increase rate of weight, or the reduction rate of weight.

[0053] For example, the formula for calculating the comprehensive labor service score is as follows: Equation (4) in, This indicates the overall labor service score; Indicates the total number of dimensions (e.g.) (corresponding to attendance rate, skill level, and compliance of salary payment). This refers to any dimension, including attendance rate, skill level, and payroll compliance. The target weight representing the data value of any specific dimension; This represents the data value of any specific dimension after standardization.

[0054] It should also be noted that the entire scoring process can be triggered through a blockchain network (such as an employment management node), with smart contracts automatically executing formula calculations. Before calculation, the root hash of a Merkle tree is used to quickly verify data integrity, ensuring the scoring is real-time and reliable. After calculation, the generated comprehensive labor score is bound to a digital identity identifier via asymmetric encryption through a smart contract. The bound data is then digitally signed to generate a transaction, with the signing private key securely managed by the smart contract. This transaction is written to the management block through a consensus mechanism and linked to the end of the work history chain, forming an immutable record. Simultaneously, the smart contract triggers event log notifications to relevant nodes (such as labor supervision nodes), achieving distributed storage and real-time verifiability of the score.

[0055] In one embodiment, identity information, work history chain, and comprehensive labor score are classified into data sensitivity levels, and the data of each sensitivity level is encrypted. This includes: constructing data sensitivity levels based on data privacy and data security levels, wherein the data sensitivity levels include a first sensitivity level, a second sensitivity level, and a third sensitivity level, with data privacy and data security levels ranging from high to low; classifying identity information as the first sensitivity level, employment behavior data and salary details as the second sensitivity level, and skill certification data and comprehensive labor score as the third sensitivity level, wherein the work history chain includes employment behavior data, salary details, and skill certification data; and encrypting the data of each sensitivity level according to a preset mapping relationship, wherein the mapping relationship represents the correspondence between data of different sensitivity levels and encryption strategies, and the security strength of the encryption strategy increases with the increase of the sensitivity level.

[0056] It should be noted that the data sensitivity levels are pre-divided into three levels from high to low: Level 1, Level 2, and Level 3, based on the data privacy and data security levels. The higher the level, the higher the requirements for data privacy and data security. Level 1 corresponds to the highest privacy and security requirements, Level 2 corresponds to medium privacy and security requirements, and Level 3 corresponds to lower privacy and security requirements.

[0057] In this embodiment, identity information is automatically classified into the first sensitivity level, employment behavior data and salary details are classified into the second sensitivity level, and then skill certification data and comprehensive labor service scores are classified into the third sensitivity level. Differential encryption processing is performed on the data of each sensitivity level, which eliminates the risk of data leakage, improves the privacy protection of data, supports the compliance of construction labor management, and is suitable for large-scale distributed application scenarios.

[0058] For example, when employment behavior data is classified as data of the second sensitivity level, it includes operation logs in the employment behavior data.

[0059] For example, in the mapping relationship, the encryption strategy corresponding to the data of the first sensitivity level is to use asymmetric encryption algorithm and zero-knowledge proof mechanism for double encryption; the encryption strategy corresponding to the data of the second sensitivity level is to use dynamic threshold signature algorithm for encryption; and the encryption strategy corresponding to the data of the third sensitivity level is to use lightweight symmetric encryption algorithm for encryption. The data of each sensitivity level after being divided is encrypted separately, including: using asymmetric encryption algorithm and zero-knowledge proof mechanism for double encryption of the data of the first sensitivity level; using dynamic threshold signature algorithm for encryption of the data of the second sensitivity level; and using lightweight symmetric encryption algorithm for encryption of the data of the third sensitivity level.

[0060] Among them are asymmetric encryption algorithms such as RSA (Rivest-Shamir-Adleman, a public-key encryption algorithm); zero-knowledge proof mechanisms such as zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge); dynamic threshold signature algorithms such as Shamir (a threshold signature algorithm); and lightweight symmetric encryption algorithms such as AES-256 (Advanced Encryption Standard with 256-bit key).

[0061] In this exemplary embodiment, data at the first sensitivity level is double-encrypted using an asymmetric encryption algorithm and a zero-knowledge proof mechanism. The asymmetric encryption uses a public key to encrypt sensitive fields to generate ciphertext, while the zero-knowledge proof mechanism allows verification of data validity without revealing the original content, ensuring the privacy of identity information remains intact during querying or verification. Data at the second sensitivity level is encrypted using a dynamic threshold signature algorithm. This involves dividing the encryption key into multiple fragments and distributing them to blockchain nodes (such as employment management nodes and labor supervision nodes), requiring a preset threshold number of nodes to collaborate for decryption, preventing malicious access by a single node. Data at the third sensitivity level is processed using a lightweight symmetric encryption algorithm to generate efficient ciphertext, reducing computational overhead. This achieves enhanced data privacy protection (e.g., first-sensitivity level data is resistant to quantum attacks), secure access control (e.g., second-sensitivity level data requires multi-node consensus for decryption), and optimized processing efficiency (e.g., fast encryption and decryption of third-sensitivity level data).

[0062] In one embodiment, after obtaining the comprehensive labor score of construction workers, the process further includes: matching the comprehensive labor score and skill level with the requirements of each position in the job database to generate a job recommendation list, and storing the job recommendation list in a management block based on digital identity identifiers; comparing the comprehensive labor score with a preset scoring threshold, and if the comprehensive labor score is less than the scoring threshold, generating a skills training path for the construction workers based on the work history chain, and pushing the skills training path to the construction workers' terminals based on digital identity identifiers, so as to train the construction workers according to the skills training path, obtain training results, and then updating the skills certification data in the work history chain based on the training results, and storing the updated skills certification data in the skills certification node in the blockchain network.

[0063] The job database is a dataset storing job requirements in the construction industry; the job recommendation list includes the job title, project location, and requirement description of the recommended jobs.

[0064] In addition, the list is associated with a digital identity (a unique encrypted string) and stored in a new blockchain block through digital signature, so as to make the recommendation results immutable and traceable.

[0065] In this embodiment, a closed loop of intelligent job matching and dynamic skills enhancement based on comprehensive labor scores is realized. On the one hand, it accurately recommends suitable jobs and improves the efficiency of job matching. On the other hand, it automatically generates personalized training paths when the scores do not reach the threshold. Through the feedback mechanism of training, certification and blockchain, the skills data of laborers are continuously updated, which not only enhances their employability, but also ensures the real-time and credibility of skills certification information on the blockchain, thereby promoting the upgrading of construction labor management towards intelligence, precision and sustainable development.

[0066] For example, based on comprehensive labor assessment and skill level, the smart contract calls a pre-set job database to perform multi-dimensional matching and generates a job recommendation list using the following formula: Equation (5) in, Indicates job position Recommended items; Represents a skills certification data vector; Indicates skill level; This indicates the overall labor service score; and Indicates job position The minimum overall labor score and minimum skill level required; For the position The demand vector; Represents the cosine similarity function; This indicates the preset matching threshold, which is dynamically set based on historical job recommendation data.

[0067] For example, storing the job recommendation list in the management block based on digital identity includes: binding the job recommendation list to the digital identity and storing the job recommendation list in the management block through digital signature, so as to achieve the immutability and traceability of the recommendation results.

[0068] For example, the smart contract compares the overall labor score with a preset scoring threshold (e.g., 60 points). When a score below the threshold is detected, the smart contract analyzes the construction worker's skill deficiencies based on the work history chain. For instance, it identifies safety operation defects using a defect localization formula based on operation logs. The specific formula is as follows: Equation (6) in, Indicates the first The deficiency value of a skill; This indicates the skill type number and the corresponding classification system for skill certification nodes; This represents the total number of operations within the statistical period; t represents the index of a single operation event. This function indicates an error and generates a corresponding skills training path (a structured training plan that includes course modules, assessment criteria, and timelines). Training path by defect value Descending order generation The system pushes information in real time to the terminals associated with the digital identity through a blockchain event notification mechanism. Based on the assessment results, updated skill certification data is generated, such as adding advanced welding certification. This updated data is then stored in the skill certification node via smart contracts, forming a closed-loop skill development record. This process leverages the distributed nature of blockchain and the automated logic of smart contracts to achieve efficient matching of labor resources and dynamic skill optimization, eliminating the delays of traditional manual intervention, improving the accuracy of job matching, and enhancing the adaptability and compliance of construction labor management through chain-based data traceability.

[0069] In one embodiment, after determining the work history chain of construction workers based on work behavior data, the method further includes: detecting violations on the work history chain according to a preset violation database and preset violation detection rules; if a violation is detected, determining a violation risk value according to a preset violation risk mapping rule; if the violation risk value is greater than a preset risk threshold, generating a risk warning based on the violation, and storing the risk warning in a labor supervision node in the blockchain network based on a digital identity identifier.

[0070] The violation database is a dataset that stores known violation types and characteristics, containing violation risk mapping rules that define the mapping relationship between violation types and risk weights; violation detection rules include logical matching algorithms and abnormal pattern recognition models; violation risk values ​​are numerical indicators that quantify the severity of violations; risk warnings are structured alerts that include violation type, timestamp, and location data; and labor supervision nodes are dedicated nodes in the blockchain network responsible for risk management.

[0071] In this embodiment, the work history chain is first scanned and detected in real time based on the violation database and violation detection rules. When a violation is detected, the violation risk value is determined based on the violation risk mapping rules. When the risk value of violation exceeds the preset risk threshold (e.g.) When violations occur, risk warnings are generated based on the violations and stored in the labor supervision node through the encrypted channel of the smart contract according to the corresponding digital identity. Through the distributed consensus mechanism of blockchain and the automation of smart contracts, the record of violations is made immutable and verifiable, eliminating the delay and misjudgment of manual monitoring, improving the efficiency of violation detection, reducing the false alarm rate, and building a proactive defense and compliance system for construction labor management, which is suitable for large-scale distributed application scenarios.

[0072] For example, the formula for calculating the risk value of violations is: Equation (7) in, Indicates the risk value of violation; This indicates the basic risk weight for the type of violation, derived from a violation database, such as a security violation. ; This represents the amplification factor for repeated violations, with a default value of 0.2. This represents the total number of historical violations; T represents the total number of operations within the statistical period.

[0073] In one possible embodiment, the employment management node, the labor payment settlement node, and the skills certification node can receive early warning notifications from the labor supervision node in real time, thereby achieving synchronous early warning across nodes.

[0074] In one embodiment, after detecting violations in the work history chain, the method further includes: if a violation is detected, lowering the overall labor score according to the type of violation; generating a labor score repair record chain based on the lowered overall labor score; and linking the labor score repair record chain to the end of the work history chain based on a digital identity identifier.

[0075] In this embodiment, the comprehensive labor service score is dynamically lowered according to the specific type of violation, thereby realizing real-time linkage and updating of credit value and violation. Subsequently, based on the adjusted comprehensive labor service score, a labor service score repair record chain is automatically generated, and through digital identity identification, the repair record chain is seamlessly linked to the end of the work history chain, ensuring that the score repair process can form a complete, verifiable and tamper-proof association chain with the historical history.

[0076] For example, when a violation is detected, the smart contract automatically executes a program to calculate a real-time downward adjustment of the comprehensive labor service score based on the type of violation (e.g., security operation error is defined as type A, and salary fraud is defined as type B). The formula for calculating the adjusted comprehensive labor service score is as follows: Equation (8) in, This indicates the adjusted overall labor service score; This represents the original comprehensive labor service score; The basic risk weight for the type of violation (e.g., for type A, =0.1, for type B, =0.3), This represents the risk value for violations.

[0077] For example, based on the downgraded comprehensive labor score, the smart contract generates a labor score repair record chain. The labor score repair record chain is a structured data block that includes the original comprehensive labor score, the downgrade amount, the violation type, and the timestamp. The labor score repair record chain is organized through a Merkle tree structure to ensure the integrity and verifiability of the repair process. The block header hash value of the labor score repair record chain is linked with the hash of the preceding block and anchored to the end of the work history chain to form an inseparable chain association.

[0078] The aforementioned blockchain-based construction labor identity management method utilizes a distributed architecture based on a blockchain network, where four nodes—employment management, payroll settlement, skills certification, and labor supervision—operate collaboratively. When generating digital identity identifiers, identity information is anonymized and encrypted to ensure privacy and security from the source. A multi-node cross-verification mechanism ensures the authenticity and consistency of work behavior data, forming an immutable work history chain and breaking down traditional information barriers. Smart contracts dynamically invoke scoring strategies, combining industry standards to update weighting factors in real time to generate a comprehensive labor score, and linking it with skills certification data to achieve accurate job recommendations, overcoming the lag of static assessments. A graded encryption mechanism for sensitive data provides differentiated encryption for identity information, operation logs, and pay details, strengthening privacy protection. A closed-loop risk control system is built based on a violation detection and scoring repair record chain, and all process data is anchored to the blockchain in the form of a graded evidence storage chain. This achieves automated governance throughout the entire lifecycle, from identity management, behavior tracing, credit assessment to risk response, improving the security, collaboration, and efficiency of construction labor management.

[0079] Please see Figure 2 , Figure 2 This is a block diagram illustrating a blockchain-based construction labor identity management system, as shown in an exemplary embodiment of this application. Figure 2 As shown, in an exemplary embodiment, the blockchain-based construction labor identity management system 200 includes at least an identity information processing module 210, a work history management module 220, a comprehensive labor scoring module 230, a data hierarchical encryption module 240, and a hierarchical data storage module 250, which are described in detail below: The identity information processing module 210 is used to obtain the identity information of construction workers, generate digital identity identifiers for construction workers based on the identity information, and bind the digital identity identifiers to the work behavior data and management blocks corresponding to the construction workers in the blockchain network. The work history management module 220 is used to read work behavior data based on digital identity and determine the work history chain of construction workers based on the work behavior data. The comprehensive labor service scoring module 230 is used to score the work history chain according to the preset scoring strategy to obtain the comprehensive labor service score of the construction laborers. The data classification and encryption module 240 is used to classify the data sensitivity levels of identity information, work history chain and comprehensive labor score, and to encrypt the data of each sensitivity level to obtain classified encrypted data. The hierarchical data storage module 250 is used to store hierarchical encrypted data to the management block based on digital identity identifiers, forming a hierarchical evidence storage chain for construction workers.

[0080] In one embodiment, the work history management module 220 is specifically used for: cross-validating work behavior data by labor supervision nodes, employment management nodes, labor wage settlement nodes, and skill certification nodes according to preset consensus rules; if the cross-validation passes and a historical work history chain exists in the management block, the work behavior data is linked to the end of the historical work history chain to generate a new work history chain; if the cross-validation passes and no historical work history chain exists in the management block, the current work history chain is generated based on the work behavior data; wherein, the blockchain network includes labor supervision nodes and employment management nodes, labor wage settlement nodes, and skill certification nodes that provide work behavior data, and the cross-validation includes source legality verification, time consistency verification, and logical rationality verification.

[0081] In one embodiment, the comprehensive labor service scoring module 230 is specifically used for: determining multi-dimensional data values ​​of construction workers based on their work history chain, wherein the multi-dimensional data values ​​include attendance rate, skill level, and wage payment compliance; standardizing the attendance rate, skill level, and wage payment compliance according to the preset maximum and minimum values ​​of each dimension data value; determining the target weight of each dimension data value according to the basic weight of each dimension data value and the time decay function of the pre-built basic weight, wherein the basic weight comes from an external regulatory database; and performing a weighted calculation on the standardized attendance rate, skill level, and wage payment compliance according to the target weight of each dimension data value to obtain a comprehensive labor service score.

[0082] In one embodiment, the data hierarchical encryption module 240 is specifically used to: construct a data sensitivity level based on the data privacy level and the data security level, wherein the data sensitivity level includes a first sensitivity level, a second sensitivity level, and a third sensitivity level, with the data privacy level and data security level respectively ranging from high to low; classify identity information into the first sensitivity level, employment behavior data and salary details into the second sensitivity level, and skill certification data and comprehensive labor score into the third sensitivity level, wherein the work history chain includes employment behavior data, salary details, and skill certification data; and encrypt the data of each sensitivity level according to a preset mapping relationship, wherein the mapping relationship represents the correspondence between data of different sensitivity levels and encryption strategies, and the security strength of the encryption strategy increases with the increase of the sensitivity level.

[0083] In one embodiment, the comprehensive labor scoring module 230 is further specifically used to: match the comprehensive labor score, skill level, and the requirements of each position in the job database to generate a job recommendation list, and store the job recommendation list in the management block based on the digital identity identifier; and / or, compare the comprehensive labor score with a preset scoring threshold. If the comprehensive labor score is less than the scoring threshold, generate a skills training path for the construction laborer based on the work history chain, and push the skills training path to the construction laborer's terminal based on the digital identity identifier, so as to train the construction laborer according to the skills training path, obtain training results, update the skills certification data in the work history chain according to the training results, and store the updated skills certification data in the skills certification node in the blockchain network.

[0084] In one possible embodiment, see Figure 3 , Figure 3 This is a block diagram illustrating a specific blockchain-based construction labor identity management system, as shown in an exemplary embodiment of this application. Figure 3 As shown, the system also includes a blockchain network construction module 260, which is used to build a blockchain network. The blockchain network is a distributed blockchain network, including employment management nodes, labor and salary settlement nodes, skill certification nodes, and labor supervision nodes.

[0085] In one embodiment, please continue to see Figure 3 The system also includes a violation monitoring module 270, which is specifically used to detect violations on the work history chain according to a preset violation behavior database and preset violation detection rules. If a violation is detected, the violation risk value is determined according to the preset violation risk mapping rules. If the violation risk value is greater than the preset risk threshold, a risk warning is generated based on the violation and stored in the labor supervision node in the blockchain network based on the digital identity identifier.

[0086] In one embodiment, the violation monitoring module 270 is further configured to: if a violation is detected, lower the overall labor service score according to the type of violation; generate a labor service score repair record chain based on the lowered overall labor service score; and link the labor service score repair record chain to the end of the work history chain based on a digital identity identifier.

[0087] It should be noted that the blockchain-based construction labor identity management system and the blockchain-based construction labor identity management method provided in the above embodiments belong to the same concept. The content of the operation performed by each module has been described in detail in the method embodiments, and will not be repeated here.

[0088] Please see Figure 4 , Figure 4 This is an exemplary embodiment of the present application illustrating the operation panel diagram of a blockchain-based construction labor identity management system. (See diagram below.) Figure 4 As shown, this panel serves as a user interface, integrated into terminal devices (such as smartphones or on-site industrial control terminals) to achieve the following core functions: allowing construction workers to input or update their desensitized and encrypted identity information, and quickly query blockchain-stored data through digital identity identifiers; real-time display of work hour records, location data, and operation logs, supporting one-click generation of a work history chain and synchronous verification with employment management nodes; dynamically displaying the comprehensive labor score calculated by smart contracts, including violation risk warnings and skills training path recommendation buttons; providing tiered encryption options, such as asymmetric encryption or dynamic threshold signatures, with users able to customize sensitivity levels. The operation process includes: logging into the system, selecting a functional module (such as identity management or score query), and performing an operation (such as data upload or encrypted application). The panel design conforms to the usability and security standards of the construction labor scenario.

[0089] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Figure 5 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0090] like Figure 5As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0091] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0092] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit (CPU) 801, it performs various functions defined in the system of this application.

[0093] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the blockchain-based construction labor identity management method described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not deployed within that electronic device.

[0094] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0095] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A blockchain-based method for managing the identity of construction workers, characterized in that, The method includes: The system obtains the identity information of construction workers, generates digital identity identifiers for the construction workers based on the identity information, and binds the digital identity identifiers to the work behavior data and management blocks corresponding to the construction workers in the blockchain network. Based on the digital identity, the work behavior data is read, and the work history chain of the construction worker is determined according to the work behavior data; The work history chain is scored according to a preset scoring strategy to obtain the comprehensive labor score of the construction workers. The identity information, the work history chain, and the comprehensive labor score are classified into data sensitivity levels, and the data of each sensitivity level is encrypted to obtain hierarchically encrypted data. Based on the digital identity identifier, the hierarchical encrypted data is stored in the management block to form a hierarchical evidence storage chain for the construction workers.

2. The blockchain-based construction labor identity management method according to claim 1, characterized in that, Determining the work history chain of the construction workers based on the work behavior data includes: The work behavior data is cross-validated by multiple nodes, including labor supervision nodes, employment management nodes, labor wage settlement nodes, and skills certification nodes, according to preset consensus rules. If the cross-validation passes and a historical work history chain exists in the management block, the work behavior data is linked to the end of the historical work history chain to generate a new work history chain. If the cross-validation passes and there is no historical work history chain in the management block, then the current work history chain is generated based on the work behavior data. The blockchain network includes the labor supervision node and the employment management node, the labor wage settlement node, and the skill certification node that provide the work behavior data. The cross-validation includes source legality verification, time consistency verification, and logical rationality verification.

3. The blockchain-based construction labor identity management method according to claim 1, characterized in that, The step of scoring the work history chain according to a preset scoring strategy to obtain the comprehensive labor score of the construction workers includes: Based on the work history chain, multi-dimensional data values ​​of the construction workers are determined, including attendance rate, skill level, and wage payment compliance. Based on the maximum and minimum values ​​of the preset data for each dimension, the attendance rate, the skill level, and the payroll compliance are standardized respectively. The target weights of the data values ​​in each dimension are determined based on the base weights of the data values ​​in each dimension and the time decay function of the pre-constructed base weights, wherein the base weights are derived from an external regulatory database. Based on the target weights of the data values ​​in each dimension, the standardized attendance rate, skill level, and wage payment compliance are weighted and calculated to obtain the comprehensive labor service score.

4. The blockchain-based construction labor identity management method according to claim 1, characterized in that, The process of classifying the identity information, work history chain, and comprehensive labor service score into data sensitivity levels, and then encrypting the data at each sensitivity level, includes: A data sensitivity level is constructed based on the data privacy level and the data security level, wherein the data sensitivity level includes a first sensitivity level, a second sensitivity level, and a third sensitivity level, which are respectively ranked from high to low based on the data privacy level and the data security level. The identity information is classified as the first sensitivity level, the employment behavior data and salary details are classified as the second sensitivity level, and the skill certification data and the comprehensive labor score are classified as the third sensitivity level. The work history chain includes the employment behavior data, the salary details and the skill certification data. According to the preset mapping relationship, the data of each sensitivity level after division are encrypted respectively. The mapping relationship represents the correspondence between data of different sensitivity levels and encryption strategies, and the security strength of the encryption strategy increases with the increase of sensitivity level.

5. The blockchain-based construction labor identity management method according to claim 3, characterized in that, After obtaining the comprehensive labor score of the construction workers, the process also includes: The comprehensive labor service score and the skill level are matched with the requirements of each position in the job database to generate a job recommendation list, and the job recommendation list is stored in the management block based on the digital identity identifier. The comprehensive labor service score is compared with a preset scoring threshold. If the comprehensive labor service score is less than the scoring threshold, a skills training path for the construction worker is generated based on the work history chain. Based on the digital identity, the skills training path is pushed to the construction worker's terminal to train the construction worker according to the skills training path and obtain training results. Based on the training results, the skills certification data in the work history chain is updated, and the updated skills certification data is stored in the skills certification node in the blockchain network.

6. The blockchain-based construction labor identity management method according to any one of claims 1 to 5, characterized in that, After determining the work history chain of the construction workers based on the work behavior data, the method further includes: Based on a preset violation database and preset violation detection rules, violation detection is performed on the work history chain; If a violation is detected, the violation risk value is determined according to the preset violation risk mapping rules; If the violation risk value is greater than the preset risk threshold, a risk warning is generated based on the violation, and the risk warning is stored in the labor supervision node of the blockchain network based on the digital identity.

7. The blockchain-based construction labor identity management method according to claim 6, characterized in that, After detecting violations in the work history chain, the method further includes: If a violation is detected, the overall labor service score will be lowered based on the type of violation. Based on the adjusted comprehensive labor service score, a labor service score repair record chain is generated; Based on the digital identity identifier, the labor score repair record chain is linked to the end of the work history chain.

8. A blockchain-based construction labor identity management system, characterized in that, The system includes: The identity information processing module is used to obtain the identity information of construction workers, generate digital identity identifiers for the construction workers based on the identity information, and bind the digital identity identifiers to the work behavior data and management blocks corresponding to the construction workers in the blockchain network. The work history management module is used to read the work behavior data based on the digital identity identifier, and determine the work history chain of the construction worker based on the work behavior data; The comprehensive labor service scoring module is used to score the work history chain according to a preset scoring strategy to obtain the comprehensive labor service score of the construction laborers. The data tiered encryption module is used to classify the identity information, the work history chain and the comprehensive labor score into data sensitivity levels, and to encrypt the data of each sensitivity level after classification to obtain tiered encrypted data. The hierarchical data storage module is used to store the hierarchical encrypted data into the management block based on the digital identity identifier, forming a hierarchical evidence storage chain for the construction workers.

9. An electronic device, characterized in that, include: processor; A storage device for storing a program that, when executed by the processor, causes the electronic device to implement the blockchain-based construction labor identity management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the blockchain-based construction labor identity management method as described in any one of claims 1 to 7.

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