Work personnel safety portrait data sharing method and system based on blockchain technology

Abstract

The application belongs to the technical field of data processing and sharing, and particularly relates to a job worker safety portrait data sharing method and system based on a blockchain technology, wherein safety behavior data of a job worker is acquired, a weighted scoring card model is trained, a safety score distribution is obtained, a dynamic safety portrait is constructed, verification is performed in combination with zero-knowledge proof and a polynomial commitment, and a digital identity certificate is generated; an improved blockchain network is used to construct associated data chains, a chain feature map is generated in combination with IPFS distributed storage, a cross-chain consensus mechanism and a smart contract are fused, a multi-node weight distribution mechanism is used to locate a high-risk job key area and analyze a behavior abnormality mode, and a risk evolution trend is output; a distributed random forest model is used to quantitatively score a comprehensive safety, a risk level is identified and a visual atlas is generated, and high-risk personnel are labeled, and meanwhile, data sharing is performed. Thus, the problems of missing key features, misjudging risk areas and incomplete portraits in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing and sharing, and particularly relates to a job personnel safety portrait data sharing method and system based on a blockchain technology. BACKGROUND

[0002] With the accelerated promotion of industrial digital transformation, the sharing and application of job personnel safety portrait data have become a core element for ensuring production safety. Precise safety portraits can provide key support for risk early warning and emergency decision-making by integrating personnel behavior trajectories, environmental parameters, training records and other multi-source data. However, current safety portrait data sharing mainly relies on traditional centralized architecture, and data is stored in a single server, which not only faces the risk of data leakage and tampering caused by hacker attacks, but also forms "data islands" due to the lack of trust mechanisms in cross-department and cross-enterprise data circulation, which seriously restricts the collaborative efficiency of safety management. At the same time, the feature extraction method based on artificial rules is difficult to dynamically capture safety hazards in complex work scenarios, resulting in insufficient accuracy of safety portraits and failing to meet the demand of intelligent safety management.

[0003] However, the existing technology has obvious defects: the feature extraction algorithm is fixed, which is difficult to adapt to changing work scenarios and is prone to miss key features; the regional positioning is affected by equipment and environmental interference, which is prone to misjudge risk areas; the model has poor robustness to environmental changes and low consistency in data collection; it only relies on two-dimensional data and does not integrate multi-modal information, so the portrait is not comprehensive. Therefore, there is an urgent need for an intelligent personnel portrait data sharing method with high safety and high adaptability to realize precise safety management. SUMMARY

[0004] The application provides a job personnel safety portrait data sharing method and system based on a blockchain technology to solve the problems of missing key features, misjudging risk areas and incomplete portraits in the prior art.

[0005] The first aspect of this application provides a method for sharing safety profile data of workers based on blockchain technology, comprising the following steps: acquiring safety behavior data of workers; training a safety profile generation model based on the safety behavior data to obtain a safety score distribution; constructing a dynamic safety profile based on the safety score distribution; verifying the profile using zero-knowledge proofs and multinomial commitments to generate a digital identity credential; constructing a related data chain using an improved blockchain network based on the digital identity credential; generating an on-chain feature graph using the IPFS distributed storage mechanism; locating key areas of high-risk operations by integrating a cross-chain consensus mechanism and smart contracts based on the on-chain feature graph and using a multi-node weight allocation mechanism; synchronously analyzing abnormal behavior patterns; outputting risk evolution trends based on the abnormal behavior patterns; and quantifying the overall safety level using a distributed random forest model based on the risk evolution trends, identifying risk levels, generating a visual map and marking high-risk workers, while simultaneously sharing the data.

[0006] Preferably, the digital identity credential is generated through verification using zero-knowledge proofs and multinomial commitments, including: constructing a zk-SNARK zero-knowledge proof algorithm; analyzing security behavior data according to the zk-SNARK algorithm to generate a privacy credential based on multinomial commitments and extracting the multinomial commitment value; generating a multinomial commitment root value bound to data based on the multinomial commitment value, and verifying the node identity through Schnorr signature; and verifying on-chain data based on the multinomial commitment root value and open proofs to generate the digital identity credential.

[0007] Preferably, the zk-SNARK zero-knowledge proof algorithm formula is as follows:

[0008] ;

[0009] in, For a concise, non-interactive proof; This relates to arithmetic circuits; For public input; Witness for privacy; To prove the generating function; This is for verifying the function.

[0010] Preferably, based on the safety behavior data, a safety profile generation model is trained to obtain a safety score distribution, including: constructing a risk assessment model; analyzing operational compliance, device interaction records, and environmental adaptability data based on the risk assessment model; generating a real-time safety probability map based on the analyzed data and a Bayesian dynamic update mechanism; and obtaining safety score distribution data based on the safety probability map.

[0011] Preferably, the risk assessment model formula is:

[0012] ;

[0013] in, Assess safety risks; Input data; These are the weighting coefficients; It is a feature mapping function; For the i-th dimension of the safety index; Use the Sigmoid activation function; For risk scoring models; The total number of features.

[0014] Preferably, a visual map is generated for sharing, and high-risk personnel are marked, including: constructing an on-chain data traceability mechanism; calculating the gradient of block hashes on transaction records according to the traceability mechanism, performing spatiotemporal aggregation, and generating a risk heat map; overlaying the risk heat map with a three-dimensional model of the work scene, marking the distribution of high-risk personnel and the boundaries of abnormal behavior through color gradients, and sharing the data.

[0015] Preferably, a distributed random forest model is used to quantify and score the overall security level, identifying the risk level, including:

[0016] When the safety score is ≥90, the risk level is low, and full-authority operation is allowed; when the score is 70-89, the risk level is medium, and supervision needs to be strengthened; when the score is 50-69, the risk level is high, and high-risk operations are restricted; when the score is <50, the risk level is extremely high, and on-site operation is prohibited.

[0017] The second aspect of this application provides a blockchain-based system for sharing safety profile data of workers, comprising: an acquisition module for acquiring safety behavior data of workers; a verification module for training a safety profile generation model based on the safety behavior data to obtain a safety score distribution, constructing a dynamic safety profile based on the safety score distribution, verifying it using zero-knowledge proofs and multinomial commitments, and generating a digital identity credential; an analysis module for constructing a linked data chain using an improved blockchain network based on the digital identity credential, generating an on-chain feature graph using the IPFS distributed storage mechanism, integrating a cross-chain consensus mechanism and smart contracts based on the on-chain feature graph, locating key areas of high-risk operations through a multi-node weight allocation mechanism, synchronously analyzing abnormal behavior patterns, and outputting a risk evolution trend based on the abnormal behavior patterns; and a generation module for quantifying and scoring the overall safety level using a distributed random forest model based on the risk evolution trend, identifying risk levels, generating a visual map and marking high-risk workers, and simultaneously sharing the data.

[0018] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the blockchain-based worker safety profile data sharing method as described in the above embodiments.

[0019] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the blockchain-based worker safety profile data sharing method as described in the above embodiments.

[0020] Therefore, this application offers the following beneficial effects: The embodiments of this application utilize the immutability and encryption characteristics of blockchain to ensure the security and trustworthiness of safe behavior data storage and sharing, and leverage IPFS to achieve efficient data storage and retrieval; furthermore, it ensures the reliability of data verification through zero-knowledge proofs and multinomial commitments, and uses an improved blockchain network and smart contracts to accurately locate high-risk work areas and analyze abnormal behavior patterns; simultaneously, it quantifies safety risks based on federated learning, generates visual graphs to assist decision-making, effectively breaking through the security and efficiency bottlenecks of traditional data sharing, and realizing the secure, efficient, and accurate sharing and in-depth application of worker safety profile data, significantly improving the intelligence level of safety management and risk prevention capabilities. Thus, it solves the problems of missed key features, misjudged risk areas, and incomplete profiles in existing technologies.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein...

[0023] Figure 1 This is a flowchart of a method for sharing worker safety profile data based on blockchain technology, according to an embodiment of this application.

[0024] Figure 2 This is an example diagram of a safety portrait of a construction worker in a building construction scene according to an embodiment of this application.

[0025] Figure 3 This is an example diagram of a large manufacturing enterprise using security profiling according to an embodiment of this application.

[0026] Figure 4 This is an example diagram of a risk assessment for a construction company according to an embodiment of this application.

[0027] Figure 5This is an example diagram of a comprehensive safety quantification score for a large chemical industrial park provided according to an embodiment of this application.

[0028] Figure 6 This is a flowchart of a method for sharing worker safety profile data based on blockchain technology according to an embodiment of this application.

[0029] Figure 7 This is a schematic diagram of the structure of a blockchain-based worker safety profile data sharing system provided according to an embodiment of this application.

[0030] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0031] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0032] The following describes a method and system for sharing worker safety profile data based on blockchain technology, according to embodiments of this application, with reference to the accompanying drawings. Addressing the problem of misjudging risk areas mentioned in the background section, this application provides a method for sharing worker safety profile data based on blockchain technology. This method utilizes the immutability and encryption characteristics of blockchain to ensure the security and reliability of safety behavior data storage and sharing, and leverages IPFS to achieve efficient data storage and retrieval. Furthermore, it ensures the reliability of data verification through zero-knowledge proofs and multinomial commitments, and uses an improved blockchain network and smart contracts to accurately locate high-risk work areas and analyze abnormal behavior patterns. Simultaneously, it quantifies safety risks based on federated learning, generates visual maps to assist decision-making, effectively breaking through the security and efficiency bottlenecks of traditional data sharing, and realizing secure, efficient, accurate sharing and in-depth application of worker safety profile data, significantly improving the intelligence level of safety management and risk prevention capabilities. Thus, it solves the problems of missed key features, misjudgment of risk areas, and incomplete profiles in the prior art.

[0033] Specifically, Figure 1 This is a flowchart illustrating the blockchain-based method for sharing worker safety profile data, as provided in this application embodiment.

[0034] like Figure 1 As shown, the method for sharing worker safety profile data based on blockchain technology includes the following steps:

[0035] In step S101, safety behavior data of the workers are acquired.

[0036] It is understood that the embodiments of this application obtain the safety behavior data of the operators, comprehensively integrate the dynamic information of personnel operation behavior, environmental parameters, and training records, and ensure that the data covers the entire operation process and multi-dimensional scenarios, providing basic data for subsequent analysis; dynamically reflecting the safety status of the operators, and avoiding misjudgment of safety risks due to missing or delayed data.

[0037] In step S102, a weighted scoring card model is trained based on security behavior data to obtain a security score distribution. A dynamic security profile is constructed based on the security score distribution, and verification is performed by combining zero-knowledge proof and multinomial commitment to generate a digital identity credential.

[0038] Among them, the weighted scorecard model is a quantitative analysis model that assigns different weights to various indicators of the evaluation object, and obtains a comprehensive score distribution after weighted calculation, so as to realize the quantitative evaluation and hierarchical management of the target object.

[0039] It is understood that this application uses a weighted scoring card model, assigning differentiated weights to various indicators in the safety behavior data based on their importance, and generating an accurate safety score distribution through weighted calculation. This constructs a dynamic safety profile, intuitively presenting the safety status of workers. Verification is performed using zero-knowledge proofs and multinomial commitments to ensure the credibility and accuracy of the profile generation process and results. Under the premise of protecting data privacy, it generates authoritative and tamper-proof digital identity credentials, providing data support for subsequent blockchain-based data sharing, risk warning, and safety management decisions, thereby improving the refinement and intelligence of safety management.

[0040] It should be noted that the formula for the weighted scorecard model is:

[0041]

[0042] in, Rate the safety; Let be the weight of the i-th feature; Let be the standardized value of the i-th safety behavior characteristic; For index variables; This is to terminate the index.

[0043] For example, such as Figure 2As shown, in a construction scenario, a large construction company uses a weighted scoring card model to construct a safety profile for its workers. The model includes 15 key indicators across four dimensions: work ability, workload, safety penalties, and safety rewards. For work ability, key indicators include safety regulation exam scores (20% weight, 100 points maximum, 5 points deducted for scores below 80), skill level (15% weight for senior technicians and above, 3 points deducted for intermediate technicians, and 5 points deducted for junior technicians), and qualification certificates (15% weight, 10 points deducted for missing certificates). For workload, factors such as the number of jobs performed (10% weight, 3 points deducted for less than 5 jobs per month) and duration of work (10% weight, 5 points added for more than 100 hours per month) are considered. For safety penalties, the "four forms" of violations (8 points deducted for each minor violation, 15 points for a more serious violation, and 30 points for a serious violation) and the implementation of corrective measures (10 points deducted for failure to rectify on time) directly affect the score. In terms of safety rewards, the creation of a site free of violations is weighted according to risk level (low risk +5 points, medium risk +8 points, high risk +12 points). Through weighted calculation, a comprehensive safety score is generated, classifying personnel into high, medium, and low risk levels. This provides a basis for targeted safety training and task allocation, significantly improving the efficiency and accuracy of safety management.

[0044] In this embodiment of the application, digital identity credentials are generated through verification using zero-knowledge proofs and multinomial commitments, including: constructing a zk-SNARK zero-knowledge proof algorithm; analyzing security behavior data according to the zk-SNARK algorithm to generate a privacy credential based on multinomial commitments and extracting the multinomial commitment value; generating a multinomial commitment root value bound to data based on the multinomial commitment value, and verifying the node identity through Schnorr signature; and verifying on-chain data based on the multinomial commitment root value and open proofs to generate digital identity credentials.

[0045] Among them, the polynomial commitment value is a specific value generated after cryptographically committing to a polynomial.

[0046] It is understood that the embodiments of this application verify the consistency between the data and the commitment polynomial through a cryptographic proof mechanism to ensure the integrity and correctness of the data; transforming complex data verification into a mathematical proof of the commitment value, combined with the zk-SNARK zero-knowledge proof algorithm, can both hide the privacy of the original data and provide a trusted anchor for digital identity credentials through the immutability of the commitment value. At the same time, by using polynomial commitment root value binding and Schnorr signature, dual verification of the node identity legitimacy and the authenticity of on-chain data can be achieved.

[0047] It should be noted that Schnorr signature is a digital signature scheme based on the discrete logarithm problem. It features signature aggregation and key hiding, efficiently merging and verifying multiple signatures, and improving signature efficiency and privacy protection in blockchain scenarios.

[0048] For example, in the application of safety profiling for power workers, companies encode employee safety behavior data, such as the number of violations (3 times), safety training duration (16 hours), and equipment inspection pass rate (85%), into the polynomial P(x) = 2x³ + 3x² + 16x + 85, calculating the polynomial commitment value C = 98765. The regulatory agency randomly assigns a challenge value r = 2, and the company calculates P(2) = 2 × 2³ + 3 × 2² + 16 × 2 + 85 = 133, submitting P(2), the commitment value C, and relevant evidence based on zero-knowledge proofs to the regulatory agency. By verifying the validity of the commitment value C and the consistency between P(2) and P(x), the regulatory agency confirms the authenticity and accuracy of the employee safety profiling score without accessing the original safety behavior data, thus protecting employee privacy while ensuring the reliability and trustworthiness of the safety profiling data.

[0049] In this embodiment of the application, the formula for the zk-SNARK zero-knowledge proof algorithm is as follows:

[0050]

[0051] in, For a concise, non-interactive proof; This relates to arithmetic circuits; For public input; Witness for privacy; To prove the generating function; This is for verifying the function.

[0052] It is understood that the embodiments of this application, by employing the zk-SNARK zero-knowledge proof algorithm, can verify the authenticity and integrity of data without disclosing the specific content of the security behavior data, through a concise and efficient cryptographic proof mechanism. This transforms complex security behavior data verification into a lightweight mathematical proof, significantly reducing the computational resources and communication costs required for verification. Combined with multinomial commitments, it effectively hides data privacy and avoids the leakage of sensitive information. Simultaneously, the generated proof is verifiable and unforgeable, providing a robust security guarantee for the generation of digital identity credentials. This ensures that the authenticity of node identity and data is quickly confirmed during cross-node data interactions, enhancing security and privacy protection capabilities.

[0053] It should be noted that the zk-SNARK zero-knowledge proof algorithm is an algorithm that can prove the truth of a statement in a concise and efficient cryptographic way without revealing specific information.

[0054] For example, in a construction scenario, a large construction company with 1,200 workers needs to prove the authenticity and reliability of worker safety profile data to regulatory authorities while protecting worker privacy. Taking a worker as an example, in the fourth quarter of 2024, he violated safety regulations six times, accumulated 40 hours of safety training, and had an 85% compliance rate in using safety equipment. The company used the zk-SNARK zero-knowledge proof algorithm to transform this data into a specific mathematical problem, generating a concise zero-knowledge proof. After receiving the proof, the regulatory authorities verified it using publicly available parameters. Without needing to obtain sensitive information such as the specific scenarios of Wang Qiang's violations, training course content, or equipment usage records, they could confirm that the safety profile data provided by the company accurately reflected Wang Qiang's safety status. After verification using this algorithm, the company's safety data review efficiency improved by 60%, and no privacy leaks occurred, achieving both high efficiency and security in safety management data exchange.

[0055] In this embodiment of the application, a security profile generation model is trained based on security behavior data to obtain a security score distribution, including: constructing a risk assessment model; analyzing operational compliance, device interaction records, and environmental adaptability data based on the risk assessment model; generating a real-time security probability map based on the analyzed data and a Bayesian dynamic update mechanism; and obtaining security score distribution data based on the security probability map.

[0056] The Yeesian dynamic update mechanism refers to a mechanism based on Bayes' theorem that, after obtaining new observation data, updates the prior probability to the posterior probability through the likelihood function, and uses this as the new prior probability to continue iterative updates, thereby realizing the dynamic correction and optimization of the probability of event occurrence.

[0057] It is understood that the embodiments of this application combine prior knowledge with new observation data and use Bayes' theorem to dynamically adjust the probability distribution of security risk assessment. When new operational behaviors, abnormal device interactions, or environmental changes occur, new data is quickly analyzed, and the judgment of security risks is corrected in a timely manner. This makes the security score distribution accurately reflect the current actual risk situation, effectively adapt to the dynamic changes in the network security environment, and promptly capture new characteristics of security threats. This makes the security score distribution more in line with the actual risk situation, improves the accuracy and timeliness of the security profile generation model, provides a more reliable and dynamic quantitative basis for network security decision-making, and enhances the adaptive capability and early warning response efficiency of the security protection system.

[0058] It should be noted that the core of the Bayesian dynamic update mechanism is Bayes' theorem, as shown in the following formula:

[0059]

[0060] in, This is the posterior probability; It is the likelihood function; For edge likelihood; This represents the prior probability.

[0061] For example, such as Figure 3 As shown, the application of a Bayesian dynamic update mechanism to construct personnel safety profiles in a large manufacturing enterprise has yielded significant results. Initially, data on employee operational compliance over the past year was collected. For example, the average number of violations per employee was 2 per month, with an 85% accuracy rate in operational procedures. Regarding equipment interaction records, employees averaged 160 hours of equipment operation per month, and 20% of equipment malfunction reports were due to human error. Environmental adaptability data showed that 20% of employees experienced reduced efficiency in high-temperature environments, and 15% experienced increased operational errors in noisy environments. Based on this data, an initial safety probability was set, assuming an initial safety score of 70 points (out of 100). As time progressed and new data was continuously generated, when data from the latest quarter was collected, the number of employee operational violations decreased to 1 per employee per month, and the accuracy rate of operational procedures increased to 90%. Based on Bayes' theorem, the new evidence increased the safety probability in terms of operational compliance, reflected in the safety profile, where the operational compliance dimension score increased from 65 to 75 points. Meanwhile, the proportion of equipment malfunctions reported due to improper human operation decreased to 15%, and the score for the equipment interaction record dimension improved from 70 to 78. If the company introduces a new production process during the quarter, increasing environmental complexity, 30% of employees experience increased operational errors when working in the new environment, and the environmental adaptability dimension score drops from 75 to 65. Based on the data from all dimensions and calculated using a Bayesian dynamic update mechanism, the employee's safety score is updated to 72. With continuous iteration and updates, the company can use accurate and real-time personnel safety profiles to provide timely training to employees with high safety risks, reducing the accident rate by up to 30% and significantly improving the company's overall safety production level.

[0062] In this embodiment of the application, the risk assessment model formula is as follows:

[0063]

[0064] in, Assess safety risks; Input data; These are the weighting coefficients; It is a feature mapping function; For the i-th dimension of the safety index; Use the Sigmoid activation function; For risk scoring models; The total number of features.

[0065] It is understood that this application's embodiments quantify and assess the potential risks to operators in different scenarios by analyzing operational compliance, equipment interaction records, and environmental adaptability safety behavior data. Key risk factors are accurately identified from massive amounts of data, and the impact of high-risk indicators is highlighted through weighted allocation, helping managers quickly locate safety hazards. Combined with a Bayesian dynamic update mechanism, real-time dynamic risk assessment is conducted to ensure that the safety score distribution data always aligns with the actual safety situation, providing a data foundation for enterprise safety management decisions and reducing the probability of safety accidents.

[0066] For example, such as Figure 4 As shown, in a construction company, when constructing a risk assessment model, operational compliance data was incorporated, such as the frequency of worker violations (an average of 3 violations per person per month); equipment interaction records showed that equipment malfunctions due to improper operation accounted for 35% of total repairs; and environmental adaptability data indicated a 20% probability of project delays when operating in complex geological environments. Based on this data, the risk assessment model assigned weights of 40%, 30%, and 30% to operational compliance, equipment interaction records, and environmental adaptability, respectively, to calculate an initial risk score. As the project progressed, new data was continuously generated. For example, if the frequency of worker violations in a certain area increased to 5 violations per person per month, the model would update the risk score in real time based on the new data, showing that the risk level for that area had increased from "medium" to "high." Through the risk assessment model, the company could accurately identify high-risk areas and processes and take timely and targeted measures, such as strengthening personnel training, optimizing equipment maintenance processes, and adjusting construction plans, resulting in a 40% reduction in the overall project accident rate and effectively ensuring the smooth progress of the project and the safety of personnel.

[0067] In step S103, based on the digital identity credential, an associated data chain is constructed using an improved blockchain network. Combined with the IPFS distributed storage mechanism, an on-chain feature graph is generated. Based on the on-chain feature graph, a cross-chain consensus mechanism and smart contracts are integrated. Through a multi-node weight allocation mechanism, key areas of high-risk operations are located, abnormal behavior patterns are analyzed synchronously, and risk evolution trends are output based on the abnormal behavior patterns.

[0068] Among them, the improved blockchain network is a new type of distributed ledger network that, based on the core characteristics of traditional blockchain such as decentralization and immutability, solves performance bottlenecks, reduces energy consumption, or expands application scenarios through innovative designs such as optimizing consensus mechanisms, improving scalability, enhancing privacy protection, or introducing cross-chain interaction.

[0069] It is understood that the embodiments of this application utilize an improved blockchain network, based on an optimized consensus mechanism and scalability, to efficiently process large amounts of data in high-risk operation scenarios and quickly build a related data chain; combined with IPFS storage and cross-chain consensus mechanism, it achieves secure data storage and cross-chain interaction, improving data credibility; through smart contracts and multi-node weight allocation, it accurately locates high-risk areas, analyzes abnormal behavior, and outputs risk evolution trends in a timely manner, thereby improving the efficiency, accuracy, and security of risk management while ensuring data decentralization and immutability.

[0070] It should be noted that, based on digital identity credentials, an improved blockchain network is used to construct a linked data chain. Combined with the IPFS distributed storage mechanism, an on-chain feature graph is generated. Using the key information in the digital identity credentials, the improved blockchain network is used to sort out data associations and construct a structured data chain. The data is stored in IPFS distributed storage, and the corresponding hash value is obtained to establish a mapping relationship with the blockchain. Then, the features of the dispersed data storage are aggregated and encoded, and finally an on-chain feature graph containing data associations, storage locations, and feature information is generated.

[0071] Based on on-chain feature graphs, this system integrates cross-chain consensus mechanisms and smart contracts. Through a multi-node weight allocation mechanism, it locates key areas of high-risk operations, simultaneously analyzes abnormal behavior patterns, and outputs risk evolution trends based on these patterns. Using on-chain feature graphs as a foundation, it leverages cross-chain consensus mechanisms to achieve collaborative data verification between different blockchains. Relying on smart contracts to automatically execute preset rules, and combining a multi-node weight allocation mechanism to assign different levels of influence to nodes based on their importance, it accurately locates high-risk operation areas. Simultaneously, by analyzing the behavioral data of workers, it identifies abnormal behavior patterns, predicts risk development trends, and outputs risk evolution trends, providing data support and decision-making basis for safety management.

[0072] The smart contract's preset rules are structured around data verification, risk identification, location decision-making, and early warning feedback. Data verification rules include verifying the validity of digital identity credentials, the completeness and format requirements of on-chain feature graph data, and whether cross-chain interaction data has passed collaborative verification across different blockchains. Risk identification rules set quantitative standards for abnormal operational behavior, such as workers staying in high-risk areas for longer than normal thresholds, equipment operation frequency exceeding safety standards by multiples, or the number of times the operation sequence deviates from standard procedures. Location decision-making rules combine a multi-node weight allocation mechanism; when more than a certain percentage of nodes determine that an area poses a risk, that area is identified as a high-risk critical operation area. Early warning feedback rules specify that when the duration of an abnormal pattern reaches a set time, or the risk evolution trend touches the preset risk level boundary, different levels of early warnings are automatically triggered, and risk information is synchronized to relevant safety management personnel, providing a reliable basis for safety management.

[0073] IPFS distributed storage mechanism is a content-addressable, decentralized distributed storage network. It achieves efficient data storage, resistance to single points of failure, and distributed retrieval by fragmenting files and storing them on multiple nodes and uniquely identifying them with hash values.

[0074] Cross-chain consensus mechanisms are collaborative consensus mechanisms that solve the problem of blockchain silos by designing protocols across blockchains to achieve data verification, state synchronization, and consensus between different chains.

[0075] Smart contracts are distributed protocols that are predefined in code and executed automatically, based on the programmable nature of blockchain.

[0076] For example, in industrial production scenarios, smart contracts can be used to construct personnel safety profiles, enabling precise assessments of employee safety status. Employees log in daily using digital identity credentials, and their work data is uploaded to the blockchain in real time. The smart contract pre-sets multiple rules: regarding work hours, it automatically triggers a fatigue warning if an employee works continuously for more than 8 hours; regarding safety training, if an employee fails to complete the daily safety training, the smart contract will prohibit them from entering high-risk areas; regarding operational procedures, if an employee accumulates three incorrect operating steps, the smart contract automatically suspends their operating privileges. Once employee behavior data triggers these preset conditions, a safety profile label is automatically generated based on the smart contract. For example, if an employee makes five incorrect operating steps and enters a high-risk area twice within a month, the smart contract will mark them as a high-risk individual, issue a warning, restrict their access to specific areas, and provide them with targeted safety training courses. In this way, smart contracts achieve efficient and accurate assessment and management of personnel safety status, greatly improving the level of safe production.

[0077] In step S104, based on the risk evolution trend, the comprehensive safety level is quantitatively scored using a distributed random forest model to identify the risk level, generate a visual map and mark high-risk workers, and at the same time, data sharing is carried out.

[0078] Among them, the distributed random forest model is a machine learning model that implements the random forest algorithm on distributed computing.

[0079] It is understood that the embodiments of this application use the random forest algorithm to process massive amounts of data in parallel, quickly and efficiently quantify and score the overall safety level based on the risk evolution trend, accurately identify the risk level, solve the memory and performance problems of single-machine processing, and improve computing efficiency; combined with the visualization map to mark high-risk workers, the risk distribution is presented in an intuitive way, providing basic data for decision-making; at the same time, the data sharing function promotes multi-department collaboration, information exchange, and improves the collaborative work capabilities of departments.

[0080] It should be noted that the formula for the Random Forest algorithm is:

[0081]

[0082]

[0083] in, The number of base learners; Let be the predicted probability of the sample by the i-th base learner; The probability of the i-th base learner making an incorrect prediction; The final continuous value score is determined by the fusion process. The final predicted category label; This refers to the position corresponding to the maximum value of variable k; For indicator functions; Let x be the predicted class of the i-th base learner for sample x; To statistically analyze the prediction results of all base learners, the number of base models that predict class k is calculated.

[0084] For example, such as Figure 5 As shown, a large chemical industrial park used a distributed random forest model based on historical operational data of over 5,000 workers in the park (including equipment inspection records, hot work duration, safety training participation, and more than 10 other features, totaling 200GB of data) and 3 years of risk evolution trend data (updated monthly with 20,000 risk event updates). The model used 200 distributed decision trees in parallel to quantify the overall safety level. After 5 minutes of computation (70% more efficient than traditional single-machine models), it accurately identified the risk levels: 62% (3,100 people) were low-risk, 25% (1,250 people) were medium-risk, and 13% (650 people) were high-risk. A visualized safety risk map was simultaneously generated, marking 12 high-risk work areas with heatmaps. Combined with personnel location data, 650 high-risk workers were accurately identified (e.g., Zhang San, a hot work worker in a certain unit area, was judged as high-risk due to a safety score of 0.3 for failing to wear protective equipment correctly three times). Data sharing is achieved through the park's safety management platform, enabling safety supervision departments, workshop teams, and training centers to collaborate. Based on the profiling results, high-risk personnel complete specialized safety training within 48 hours (100% training coverage). A reassessment one month later shows that the proportion of high-risk personnel has dropped to 5%, effectively improving the intelligence and precision of the park's safety management.

[0085] In this embodiment of the application, a visual map is generated for sharing, and high-risk personnel are marked, including: constructing an on-chain data traceability mechanism; calculating the gradient of block hash to transaction records according to the traceability mechanism, performing spatiotemporal aggregation, and generating a risk heat map; overlaying the risk heat map with a three-dimensional model of the work scene, marking the distribution of high-risk personnel and the boundaries of abnormal behavior through color gradients, and sharing the data.

[0086] Among them, the on-chain data traceability mechanism uses blockchain to record information about the entire process of data generation and modification through blocks. With hash encryption, distributed storage and chain structure, it realizes a mechanism that makes the source, flow and operation of data verifiable, verifiable and traceable.

[0087] It is understood that this application embodiment utilizes the immutable and traceable characteristics of blockchain to accurately record the entire process of security data. This provides data for calculating the gradient of transaction records based on block hashes and spatiotemporal aggregation to generate risk heatmaps, ensuring the authenticity and reliability of the data. This allows the risk heatmaps to accurately reflect the distribution of operational risks. When subsequently overlaid with a 3D model of the operational scenario, the distribution of high-risk personnel and the boundaries of abnormal behavior can be accurately marked using color gradients, clearly presenting the security risk situation. At the same time, due to the trustworthy and shared attributes of on-chain data, data barriers are broken down, enabling efficient flow of security data in the management process. This facilitates multi-departmental collaboration in security supervision and risk management, comprehensively improving the accuracy, visualization, and sharing efficiency of security profiles.

[0088] It should be noted that, according to the traceability mechanism, the gradient of the block hash on the transaction record is calculated, and spatiotemporal aggregation is performed to generate a risk heat map. Based on the on-chain data traceability mechanism, by calculating the gradient of the block hash on the transaction record, the sensitivity of data changes during storage and circulation in the blockchain is quantified, and subtle features of data changes are captured. Using time and space as clues, transaction records from different time periods and different work areas are aggregated and analyzed, and risk information is classified according to intensity, ultimately generating a risk heat map that intuitively presents the distribution of high-risk work areas and high-risk periods.

[0089] For example, in a construction project of a super high-rise commercial complex, the on-chain data traceability mechanism ensured safety management throughout the entire process. During the construction period, approximately 1,200 data entries (including attendance records of 800 workers, operating parameters of 300 pieces of machinery, and 100 safety inspection records) were uploaded to the blockchain in real time each day, forming approximately 200 data blocks. After a high-altitude operation accident, by calculating the gradient of the block hash to the transaction records, it was discovered that in the three hours before the accident, the load-bearing sensor data of the tower crane involved showed abnormal fluctuations in block number 4567, with a hash value change gradient of 0.8 (normal range 0.1-0.3), pinpointing the key record of equipment overload operation. Based on spatiotemporal aggregation analysis, 23 records of equipment anomalies and 18 cases of personnel violations in the same area over the past 15 days were integrated to generate a risk heat map. The construction surface of floors 28-32, where the accident occurred, was highlighted in red. The risk heat map value of this area reached 92 points (out of 100), far exceeding the average level (65 points). The project team shared the complete on-chain traceability data with the safety supervision department, which included 127 tamper-proof operation logs and 28 sets of associated hash values. The accident liability determination was completed in just 2 days. Based on the heat map results, 8 sets of safety monitoring equipment were added, which reduced the accident rate of high-risk operations in the subsequent construction phase by 67%.

[0090] In this embodiment of the application, a distributed random forest model is used to quantify and score the overall safety level and identify the risk level, including: when the safety score is ≥90 points, the risk level is low risk and full-authority operation is allowed; when the score is 70-89 points, the risk level is medium risk and enhanced supervision is required; when the score is 50-69 points, the risk level is high risk and high-risk operation is restricted; when the score is <50 points, the risk level is extremely high risk and on-site operation is prohibited.

[0091] It is understood that this application's embodiments provide an efficient and accurate basis for operational control by quantifying comprehensive safety scores and classifying risk levels. When the safety score is ≥90, it is considered low-risk, allowing full-authority operation to fully guarantee production efficiency and avoid over-control; a medium-risk score of 70-89 triggers enhanced supervision in a timely manner to reduce the probability of risk occurrence; a high-risk score of 50-69 restricts high-risk operations to avoid major accidents; and an extremely high-risk score <50 directly prohibits on-site operation to eliminate serious safety hazards. This tiered control mechanism ensures safe production, flexibly balances production efficiency and safety risks, and allows for the rational allocation and efficient utilization of resources.

[0092] The method for sharing worker safety profile data based on blockchain technology proposed in this application utilizes the immutability and encryption characteristics of blockchain to ensure the security and trustworthiness of safety behavior data storage and sharing, and leverages IPFS to achieve efficient data storage and retrieval. Furthermore, it ensures the reliability of data verification through zero-knowledge proofs and multinomial commitments, and uses an improved blockchain network and smart contracts to accurately locate high-risk work areas and analyze abnormal behavior patterns. Simultaneously, it quantifies safety risks based on federated learning, generates visual graphs to assist decision-making, effectively breaking through the security and efficiency bottlenecks of traditional data sharing. This achieves secure, efficient, and accurate sharing and in-depth application of worker safety profile data, significantly improving the intelligence level of safety management and risk prevention capabilities. Therefore, it solves the problems of missed key features, misjudgment of risk areas, and incomplete profiles in existing technologies.

[0093] The following will illustrate a specific embodiment of a method for sharing worker safety profile data based on blockchain technology, such as... Figure 6 As shown, it includes:

[0094] Within the chemical plant, various devices were used to collect data on the safety behaviors of workers. In the reactor production workshop, a smart camera detected the following in worker Zhang San's actions from October 1st to October 7th, 2024: operating without protective gloves 3 times, each lasting approximately 5 minutes; and engaging in unauthorized hot work once, lasting 10 minutes. Equipment sensors recorded 4 abnormal startups of the reactor equipment he operated during that week (startup time exceeding the normal range by ±10 seconds), and temperature settings exceeding the safety threshold 3 times, each lasting 3-5 minutes. Environmental monitoring equipment showed that the concentration of the harmful gas chlorine in the work area reached 80 ppm (the safety threshold is 50 ppm) between 14:00 and 15:00 on October 3rd, and the temperature reached 45℃ at 10:00 on October 5th (the normal operating temperature range is 20-35℃). This data was organized chronologically to form a safety behavior dataset containing worker ID (Zhang San-001), data collection time, and specific data content.

[0095] Construct a risk assessment model: Set operational compliance weights =0.4, Device interaction record weight =0.3, environmental adaptability weight =0.3. Specific deduction rules are as follows: Operational Compliance: 8 points deducted for each instance of not wearing protective gloves; 15 points deducted for each instance of unauthorized hot work; 10 points deducted for each instance of not operating equipment according to regulations; 12 points deducted for each instance of not wearing safety goggles during high-risk operations; 20 points deducted for each instance of illegally carrying a source of ignition into a fire-prohibited area; 18 points deducted for each instance of smoking in the work area; 12 points deducted for each instance of unauthorized absence from duty. Equipment Interaction Records: 5 points deducted for each instance of abnormal equipment startup; 7 points deducted for each instance of operating parameters exceeding thresholds; 9 points deducted for each instance of abnormal vibration alarm of equipment components; 6 points deducted for each instance of abnormal valve switching. Environmental Adaptability: 0.5 points deducted per minute for exceeding the standard concentration of harmful gases; 1 point deducted per 1°C for abnormal temperature; 0.4 points deducted per minute for exceeding the standard concentration of dust. Combined with a Bayesian dynamic update mechanism, the model parameters are continuously updated based on newly collected data to generate a real-time safety probability map. Ultimately, it was determined that Zhang San's safety score for the week was distributed in the high-risk range (50-69 points), while Li Si and Wang Wu's safety scores were distributed in the medium-risk range (70-89 points).

[0096] Constructing a zk-SNARK zero-knowledge proof algorithm: The security behavior data of Zhang San, Li Si, and Wang Wu were analyzed separately to generate privacy credentials based on multinomial commitments, and the multinomial commitment values ​​were extracted as follows: =1234567890, =9876543210, =5678901234. Based on these commitment values, polynomial commitment root values ​​are generated and bound to data, and node identities are verified using Schnorr signatures. Finally, based on the polynomial commitment root values ​​and open proofs, on-chain data is verified, and respective digital identity credentials are generated to ensure data privacy and authenticity.

[0097] Based on the digital identity credentials of Zhang San, Li Si, and Wang Wu, and leveraging an improved blockchain network and the IPFS distributed storage mechanism, their safety behavior data, including records of violations, equipment interaction data, and environmental data, was stored on the IPFS network and their hash values ​​were recorded on the blockchain to generate an on-chain feature graph. A cross-chain consensus mechanism and smart contracts were integrated, and a multi-node weight allocation mechanism was used for analysis. In the reactor operation area, within a week, eight workers, including Zhang San, Li Si, and Wang Wu, had safety scores below 80 points. The area recorded 35 instances of abnormal equipment operation, with a total of 5 hours of hazardous gas concentration exceeding standards and 4 hours of dust concentration exceeding standards. Based on this data, the reactor operation area was designated as a high-risk, critical operation area. Further analysis of Zhang San's unauthorized debugging and unauthorized absence from his post, Li Si's failure to follow procedures, and Wang Wu's unauthorized carrying of fire sources and smoking, combined with historical data predictions, indicates that if these behaviors continue, Zhang San has an 85% probability of causing equipment failure and a 30% probability of causing serious accidents such as explosions in the next month; Li Si has a 70% probability of causing equipment damage and a 25% probability of causing production interruption; and Wang Wu has a 60% probability of causing a fire and a 15% probability of causing casualties.

[0098] The overall safety levels of Zhang San, Li Si, and Wang Wu were quantitatively scored using a distributed random forest model. Zhang San scored 58 points, classifying him as high-risk; Li Si scored 75 points, classifying him as medium-risk; and Wang Wu scored 72 points, classifying him as medium-risk. Zhang San was subsequently restricted from performing high-risk operations such as hot work and working at heights, and a risk warning was sent to him. Simultaneously, daily supervision of Li Si and Wang Wu was strengthened, and they were required to participate in safety training. An on-chain data traceability mechanism was constructed, calculating the gradient of block hashes on transaction records and performing spatiotemporal aggregation to generate a risk heatmap. In a 3D operational scenario model of the chemical plant, high-risk reactor operation areas were marked in red, and surrounding areas of medium-risk areas were marked in orange, clearly indicating the distribution and abnormal behavior boundaries of high-risk and medium-risk personnel such as Zhang San, Li Si, and Wang Wu. The visualized map, along with the specific violation records and risk prediction results of the three individuals, was shared with safety management personnel and operational staff. Based on this, safety management personnel formulated targeted supervision measures, such as adding inspection robots to the reactor area to conduct inspections every hour, and organizing safety warning training for all operators; after reviewing their own safety profiles, Zhang San, Li Si, and Wang Wu realized the seriousness of the problem, took the initiative to learn safe operating procedures, and actively improved their operating behaviors.

[0099] In summary, this invention, through multi-device collaborative collection of worker safety behavior data, combined with a risk assessment model and a Bayesian dynamic update mechanism, can accurately quantify safety risks and quickly locate high-risk personnel such as Zhang San and medium-risk personnel such as Li Si and Wang Wu. Utilizing the zk-SNARK zero-knowledge proof algorithm and IPFS distributed storage, it ensures data privacy and security while achieving efficient storage and trusted sharing. The blockchain-based interconnected data chain and multi-mechanism collaborative analysis can accurately pinpoint high-risk work areas such as reactor operation areas and scientifically predict risk evolution trends. Finally, quantitative scoring and visualized data sharing help safety managers quickly formulate and implement targeted regulatory measures, while workers can intuitively understand their own safety status, proactively regulate their operating behaviors, effectively reduce the probability of accidents, and comprehensively improve the level and efficiency of chemical plant operation safety management, building a solid defense for safe production.

[0100] Next, referring to the accompanying drawings, a blockchain-based worker safety profile data sharing system according to an embodiment of this application is described.

[0101] Figure 7 This is a block diagram of a blockchain-based worker safety profile data sharing system according to an embodiment of this application.

[0102] like Figure 7 As shown, the blockchain-based worker safety profile data sharing system 10 includes: an acquisition module 100, a verification module 200, an analysis module 300, and a generation module 400.

[0103] The system comprises the following modules: Acquisition module 100 acquires safety behavior data of workers; Verification module 200 trains a safety profile generation model based on the safety behavior data to obtain a safety score distribution, constructs a dynamic safety profile based on the safety score distribution, verifies it using zero-knowledge proofs and multinomial commitments, and generates digital identity credentials; Analysis module 300 constructs a linked data chain using an improved blockchain network based on the digital identity credentials, generates an on-chain feature graph using the IPFS distributed storage mechanism, integrates cross-chain consensus mechanisms and smart contracts, locates key areas of high-risk operations through a multi-node weight allocation mechanism, analyzes abnormal behavior patterns simultaneously, and outputs risk evolution trends based on these patterns; Generation module 400 quantifies and scores the overall safety level using a distributed random forest model based on the risk evolution trends, identifies risk levels, generates a visual map and labels high-risk workers, and simultaneously facilitates data sharing.

[0104] It should be noted that the foregoing explanation of the embodiment of the method for sharing safety profile data of operators based on blockchain technology also applies to the safety profile data sharing system of operators based on blockchain technology in this embodiment, and will not be repeated here.

[0105] The worker safety profile data sharing system based on blockchain technology proposed in this application utilizes the immutability and encryption characteristics of blockchain to ensure the security and trustworthiness of safety behavior data storage and sharing, and leverages IPFS to achieve efficient data storage and retrieval. Furthermore, it ensures the reliability of data verification through zero-knowledge proofs and multinomial commitments, and uses an improved blockchain network and smart contracts to accurately locate high-risk work areas and analyze abnormal behavior patterns. Simultaneously, it quantifies safety risks based on federated learning, generates visual graphs to assist decision-making, effectively breaking through the security and efficiency bottlenecks of traditional data sharing. This enables secure, efficient, and accurate sharing and in-depth application of worker safety profile data, significantly improving the intelligence level of safety management and risk prevention capabilities. Therefore, it solves the problems of missed key features, misjudgment of risk areas, and incomplete profiles in existing technologies.

[0106] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0107] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0108] When the processor 802 executes the program, it implements the method for sharing operator safety profile data based on blockchain technology provided in the above embodiments.

[0109] Furthermore, electronic devices also include:

[0110] Communication interface 803 is used for communication between memory 801 and processor 802.

[0111] The memory 801 is used to store computer programs that can run on the processor 802.

[0112] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0113] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0115] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0116] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for sharing worker safety profile data based on blockchain technology.

[0117] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the aforementioned method for sharing worker safety profile data based on blockchain technology.

[0118] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0119] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0120] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0121] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0123] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for sharing safety profile data of workers based on blockchain technology, characterized in that, The method comprises the following steps: acquiring safety behavior data of workers, wherein the safety behavior data of workers is acquired to comprehensively integrate personnel operation behavior, environmental parameters, and dynamic information of training records; training a weighted scorecard model according to the safety behavior data to obtain a safety score distribution, constructing a dynamic safety portrait based on the safety score distribution, verifying through zero-knowledge proof to generate a digital identity certificate, wherein a zk-SNARK algorithm is constructed; analyzing the safety behavior data according to the zk-SNARK algorithm to generate a privacy certificate based on a polynomial commitment, extracting a polynomial commitment value; based on the commitment value, generating a polynomial commitment root value binding data, and simultaneously verifying the identity of a Schnorr signature node; verifying on-chain data according to the polynomial commitment root value and an open proof to generate a digital identity certificate; constructing associated data chains by using an improved blockchain network according to the digital identity certificate, wherein the improved blockchain network is a new type of distributed ledger network that solves performance bottlenecks, reduces energy consumption, and expands application scenarios on the basis of the core characteristics of traditional blockchain decentralization and tamper resistance by optimizing the consensus mechanism, improving scalability, and enhancing privacy protection; combined with an IPFS distributed storage mechanism, a chain feature map is generated, based on the chain feature map, a cross-chain consensus mechanism and a smart contract are fused, a high-risk operation key area is located through a multi-node weight distribution mechanism, behavior anomaly patterns are synchronously analyzed, and risk evolution trends are output according to the behavior anomaly patterns; based on the risk evolution trends, a comprehensive safety degree is quantitatively scored by a distributed random forest model, risk levels are identified, a visual atlas is generated and high-risk workers are labeled, and data sharing is performed.

2. The blockchain-based job worker safety profiling data sharing method of claim 1, wherein, The formula of the zk-SNARK algorithm is: ； wherein, succinct non-interactive argument of knowledge; for an arithmetic circuit relation; for a public input; for a private witness; for a proof generating function; for a verification function. 3.The blockchain-based job worker safety profiling data sharing method of claim 1, wherein, training a weighted scorecard model according to the safety behavior data to obtain a safety score distribution, comprising: constructing a risk assessment model; analyzing operation compliance, device interaction records, and environmental adaptability data according to the risk assessment model; generating a real-time safety probability graph based on the Bayesian dynamic updating mechanism according to the analyzed data, and obtaining safety score distribution data according to the safety probability graph. 4.The blockchain-based job worker safety profiling data sharing method of claim 3, wherein, The formula of the risk assessment model is: ； wherein, is a security risk score; is input data; is a weight coefficient; is a feature mapping function; is the i-th dimension security indicator; is a Sigmoid activation function; is a risk score model; is the total number of features. 5.The blockchain-based job worker safety profiling data sharing method of claim 1, wherein, generating a visual atlas for sharing, and labeling high-risk workers, comprising: constructing an on-chain data traceability mechanism; calculating the gradient of block hash on transaction records according to the traceability mechanism, performing spatio-temporal aggregation to generate a risk heat map; superimposing the risk heat map on a three-dimensional model of the work scene, labeling high-risk personnel distribution and behavior anomaly boundaries through color gradient, and simultaneously sharing data. 6.The blockchain-based job worker safety profiling data sharing method of claim 1, wherein, quantitatively scoring a comprehensive safety degree by a distributed random forest model to identify risk levels, comprising: when the safety score is greater than or equal to 90 points, the risk level is low-risk, allowing full-authorization operation; when the score is 70-89 points, the risk level is medium-risk, requiring intensified supervision; when the score is 50-69 points, the risk level is high-risk, limiting high-risk operation; and when the score is less than 50 points, the risk level is extremely high-risk, prohibiting on-site operation.

7. A workman safety profile data sharing system based on blockchain technology, characterized by, ​ An acquisition module is configured to acquire safety behavior data of a job worker, wherein the safety behavior data of the job worker is acquired to comprehensively integrate personnel operation behavior, environmental parameters, and training record dynamic information. A verification module is configured to train a weighted scorecard model according to the safety behavior data, obtain a safety score distribution, construct a dynamic safety portrait based on the safety score distribution, perform verification through zero-knowledge proof, and generate a digital identity certificate, wherein a zk-SNARK algorithm is constructed; safety behavior data is analyzed according to the zk-SNARK algorithm, a privacy certificate based on a polynomial commitment is generated, a polynomial commitment value is extracted; based on the commitment value, a polynomial commitment root value binding data is generated, and a Schnorr signature verification node identity is performed; based on the polynomial commitment root value and an open proof, on-chain data is verified, and a digital identity certificate is generated. An analysis module is configured to construct associated data chains by using an improved block chain network according to the digital identity certificate, wherein the improved block chain network is a new type of distributed ledger network that is based on the core characteristics of traditional block chain decentralization and tamper resistance, and solves performance bottlenecks, reduces energy consumption, and expands application scenarios by optimizing the consensus mechanism, improving scalability, enhancing privacy protection, introducing cross-chain interaction, generating a chain feature map based on the IPFS distributed storage mechanism, and fusing a cross-chain consensus mechanism and a smart contract through a multi-node weight distribution mechanism to locate high-risk work key areas, synchronously analyze abnormal behavior patterns, and output risk evolution trends according to the abnormal behavior patterns. A generation module is configured to quantitatively score a comprehensive safety degree by using a distributed random forest model based on the risk evolution trends, identify a risk level, generate a visual atlas and label high-risk job workers, and perform data sharing.

8. An electronic device, comprising: A computer program or instructions are executed to implement the job worker safety portrait data sharing method based on the block chain technology according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, A computer program or instructions are executed to implement the job worker safety portrait data sharing method based on the block chain technology according to any one of claims 1-6.

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