Electric welding machine use permission verification method based on big data

Abstract

The invention discloses an electric welding machine use permission verification method based on big data, and particularly relates to the technical field of big data analysis, and the method comprises the following steps: firstly, collecting electric welding machine use permission verification data and request response data through log records, and forming a big data set; performing permission request response analysis and security verification analysis based on the data set, and evaluating local resource performance; then, whether temporary cloud resources are started or not is determined according to an evaluation result, and resource scheduling duration estimation is carried out; and finally, closing the cloud resources after a preset duration is reached, recovering the use of the local resources, and circularly executing the above steps. Dynamic resource management can be carried out through the authority verification response index and the safety verification completion index, seamless switching between cloud and local resources can be achieved, if the local resources cannot meet requirements, the cloud resources are automatically called to ensure normal operation of a preset electric welding machine user identity verification system, manual intervention is reduced, and the efficiency is improved. And the equipment management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and more specifically, to a method for verifying the usage authority of a welding machine based on big data. Background Art

[0002] With the development of industrial automation and intelligence, the usage management of heavy equipment such as welding machines has become increasingly complex. Traditional methods for verifying the usage authority of welding machines usually rely on manual approval or simple identity verification systems. This method is easily affected by human errors and has low efficiency when dealing with large-scale usage. Especially in complex production environments, situations such as improper operation, equipment abuse, or unauthorized use may occur, increasing safety hazards.

[0003] In some high-risk industrial fields, especially in scenarios that require high-precision and high-safety operations, verifying the usage authority of welding machines not only depends on simple identity verification but also needs to consider various factors such as the qualifications of operators, usage environments, and operation history records. Traditional permission management systems cannot efficiently process large amounts of data and respond slowly to dynamically changing operating conditions, making it difficult to achieve real-time and accurate permission verification. In addition, with the advancement of intelligent manufacturing, the improvement of data collection and processing capabilities, and the popularization of big data technology, it has become a more scientific and efficient method to use a preset identity verification system for welding machine users to conduct big data analysis for permission verification.

[0004] However, in actual applications, a preset identity verification system for welding machine users may experience performance bottlenecks due to insufficient local resources, and thus cannot guarantee the quality of usage authority verification. Therefore, the present invention proposes a method for verifying the usage authority of a welding machine based on big data in order to solve the above problems. Summary of the Invention

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for verifying the usage authority of a welding machine based on big data, comprising the following steps: Step 1: When using local resources, within a fixed time window, collect welding machine usage authority verification data and permission request response data respectively through the log records in a preset identity verification system for welding machine users to form a log big data set; Step 2: Conduct permission request response analysis and security verification analysis based on the log big data set respectively, and perform local resource performance evaluation according to the results of the permission request response analysis and the security verification analysis; Step 3: Determine whether to enable temporary cloud resources based on the local resource performance evaluation results. When enabling temporary cloud resources, estimate the resource scheduling duration based on the results of permission request response analysis and security verification analysis to obtain the total duration value for enabling temporary cloud resources. Step 4: After reaching the total duration value for enabling temporary cloud resources, turn off the temporary cloud resources, resume using local resources, and execute Steps 1 to 3 again.

[0006] In a preferred embodiment, the preset electric welding machine user authentication system is used to verify permissions according to the identity information input by the user and the preset permission rules, determine whether the user has the permission to use the electric welding machine, and control the enabling or disabling of the electric welding machine according to the determination result.

[0007] In a preferred embodiment, when performing permission request response analysis based on the log big data set, the result obtained is the permission verification response index. When performing security verification analysis based on the log big data set, the result obtained is the security verification completion index.

[0008] In a preferred embodiment, the acquisition logic of the permission verification response index is as follows: Under a fixed time window, obtain the time series data corresponding to the response time of each request. To emphasize the response efficiency within the time window, calculate the response time impact value corresponding to the time series data: ; represents the standard deviation of the time series data, represents the th request response time in the time series data, represents the total number of requests corresponding to the time series data, represents the response time impact value corresponding to the time series data, represents a preset non-zero constant; Obtain the number of successfully processed permission verification requests, and at the same time consider the penalty for failed requests, and calculate the penalty factor: ; represents the number of successfully processed permission verification requests, represents the penalty factor; Obtain the number of requests received in each preset request sub-window, and at the same time, to emphasize the impact of the load on the response, calculate the load impact value: ; represents the average value of the number of requests received in all request sub-windows, represents the standard deviation of the number of requests received in all request sub-windows, represents the load impact value; The calculation formula for the permission verification response index is: ; is a preset non - zero adjustment coefficient, , are both preset non - zero influence coefficients, represents the permission verification response index.

[0009] In a preferred embodiment, the acquisition logic of the security verification completion index is as follows: Under a fixed time window, obtain the total number of verifications for all requests , the number of successful verifications completed, the number of failed verifications , and the number of timed - out verifications , as well as the average response time of the verifications , then calculate the ratio of the number of successful verifications completed to the total number of verifications for all requests to obtain the verification completion degree under the time window ; Reflect the impact of failed and timed - out verifications on the overall security verification efficiency through a penalty term. The formula for the penalty term is: ; is the penalty term, is the failure penalty value, is the timeout penalty value; ; ; , are both preset non - zero penalty factors, and ; Considering the impact of the response time, introduce an adjustment function : ; is the preset maximum verification acceptance response time, is the preset adjustment coefficient, with a value between 0 and 1; The calculation formula for the security verification completion index is: ; is the security verification completion index.

[0010] In a preferred embodiment, performing local resource performance evaluation refers to: Substitute the security verification completion index and the permission verification response index into a pre - trained convolutional neural network model. The output result of the convolutional neural network model is 0 or 1. When the result is 0, it is determined that the local resource performance meets the preset operating conditions of the electric - welder user identity verification system. When the result is 1, it is determined that the local resource performance does not meet the preset operating conditions of the electric - welder user identity verification system, and temporary cloud resources are enabled when the result is 1.

[0011] In a preferred embodiment, the total duration value of enabling temporary cloud resources is determined by the following logic: Obtain the security verification completion index , the permission verification response index , and then substitute them into the resource scheduling duration estimation formula: ; , are both preset non-zero conversion coefficients, is the preset resource scheduling buffer duration, is the total duration value of enabling temporary cloud resources.

[0012] Technical effects and advantages of the present invention: The present invention performs dynamic resource management through the permission verification response index and the security verification completion index. The permission verification response index reflects the response efficiency of the system when processing permission requests. If this index is low, it means that the system's ability to process permission requests is limited, which may allow unauthorized personnel to operate the device. By dynamically adjusting the resource configuration (such as increasing computing resources), the response speed of the system can be improved, thereby effectively preventing abuse and ensuring the security of device use. The security verification completion index reflects the completion situation of the system's security verification. If the security verification fails to be completed in a timely manner, there may be loopholes in the system's operation permission verification, increasing security risks.

[0013] The present invention can achieve intelligent switching between the cloud and the local based on the permission verification response index and the security verification completion index. When the permission verification response index is low, the permission verification efficiency can be improved by increasing local computing resources (such as processors, memory, etc.). This intelligent resource scheduling can ensure the smooth operation of the electric welding machine operation under high load conditions. When the security verification completion index is low, it means that the completion degree of the security verification is poor and there may be security hazards in the system. In this case, the security can be enhanced by activating security resources, such as introducing high-performance firewalls, authentication systems, etc. This dynamic adjustment not only improves the security of the system but also ensures the efficient use of resources.

[0014] The present invention can achieve seamless switching between cloud and local resources. Local resources are mainly responsible for daily tasks such as permission verification and data processing. When local resources meet the system requirements, efficient operation can be ensured. When dealing with tasks with high load or complexity, it can be decided whether to enable cloud resources through evaluation. If local resources cannot meet the requirements, cloud resources are automatically invoked to ensure the normal operation of the preset user authentication system for electric welding machines. The enabling of cloud resources is based on the permission verification response index and the security verification completion index, which makes the enabling of cloud resources not only to cope with load pressure, but also to perform precise scheduling according to security and verification completion, reduce costs, and improve overall flexibility.

[0015] Traditional methods for verifying the permissions of electric welding machines usually rely on manual review, which is complex and error-prone, especially in an environment with a large number of devices and frequent use. The present invention greatly reduces manual intervention and improves the efficiency of device management through an automated permission verification system, intelligent resource scheduling, and efficient switching between cloud and local resources. This automated management not only improves work efficiency, but also better complies with safety operating procedures, reduces the impact of human factors on device management, and avoids operation risks caused by human errors. Through the analysis of the permission verification response index and the security verification completion index, the system can intelligently enable cloud resources or adjust local resources according to actual needs. This resource scheduling method avoids resource waste in the traditional static resource configuration mode, can maximize the utilization efficiency of resources, and reduce unnecessary cost expenditures. During the resource scheduling process, through the switching between cloud and local resources, not only the balance of system performance and security is ensured, but also the cost can be flexibly managed according to the load situation, reducing unnecessary consumption of cloud computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings; Figure 1 It is a schematic diagram of the method for verifying the usage permissions of electric welding machines based on big data in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Refer to Figure 1 The following embodiments are obtained: Embodiment

[0019] Method for verifying the usage permission of an electric welding machine based on big data, comprising the following steps: Step 1: When using local resources, within a fixed time window, collect the data for verifying the usage permission of the electric welding machine and the data for the response to the permission request respectively through the log records in the preset user identity verification system for the usage of the electric welding machine, so as to form a log big data set; By recording the data for verifying the usage permission and the data for the response to the permission request during the usage of the electric welding machine, it can be ensured that the operations of each user are strictly authenticated, and the whole process of verifying the usage permission can be monitored in real time. This provides the basic data for subsequent analysis and decision-making. As a dangerous industrial equipment, the management of the usage permission of the electric welding machine must be strict. In the past, manual review and traditional permission management methods were easily affected by human errors and could not achieve efficient and real-time monitoring. By collecting relevant log data and conducting centralized management, accurate and traceable data support can be provided for the system.

[0020] Step 2: Conduct the analysis of the response to the permission request and the analysis of security verification respectively based on the log big data set, and conduct the performance evaluation of local resources according to the results of the analysis of the response to the permission request and the analysis of security verification; By deeply analyzing the log data, key performance indicators such as the response time, success rate, and load impact of the permission request can be understood in real time, so as to evaluate the operating status and security of the equipment. The analysis of security verification can identify potential risks and improve the security of equipment operation. The electric welding machine may face security risks during operation, especially when the verification of the usage permission is not timely or abnormal.

[0021] Step 3: Decide whether to enable temporary cloud resources based on the results of the performance evaluation of local resources, and when enabling temporary cloud resources, estimate the duration of resource scheduling based on the results of the analysis of the response to the permission request and the analysis of security verification, so as to obtain the total duration value of enabling temporary cloud resources; By evaluating the performance of local resources, the operation mode of the electric welding machine can be dynamically adjusted to decide whether to enable temporary cloud resources to enhance the processing capacity. This not only improves the resource utilization rate but also can be expanded when the load is heavy, avoiding the impact on the usage of the electric welding machine due to insufficient performance. In practical applications, the preset user identity verification system for the usage of the electric welding machine may have performance bottlenecks due to certain reasons (such as high-load operation, network delay, etc.). Through the performance evaluation of local resources, it can be decided in real time whether to enable cloud resources to ensure high-quality applications.

[0022] Step 4: After reaching the total duration value for enabling the temporary cloud resources, turn off the temporary cloud resources, resume using local resources, and execute Steps 1 to 3 again. Once the usage duration of the temporary cloud resources reaches the preset value, the system will turn off the cloud resources, resume using local resources, and maintain the balance of resources. This flexible resource management method can reduce unnecessary costs and improve the overall sustainability of the system. Since cloud resources usually need to be paid for, their usage should be minimized to avoid unnecessary long-term resource consumption. This step ensures the reasonable use of cloud resources, reduces costs, and guarantees the stability of operations through periodic restoration of local resources.

[0023] With the development of industrial automation and digital technologies, the intelligent management of equipment such as welding machines has become increasingly important. Traditional permission management and resource scheduling methods often suffer from problems such as slow response, low efficiency, and poor security. The method for verifying the usage permissions of welding machines based on big data is an innovative solution proposed in response to this demand background. By collecting and analyzing data and combining the dynamic scheduling of cloud and local resources, it can effectively improve the operation safety, management efficiency, and resource utilization rate of welding machines.

[0024] The preset user identity verification system for welding machine usage is used to verify permissions based on the identity information input by the user and the preset permission rules, determine whether the user has the permission to use the welding machine, and control the enabling or disabling of the welding machine according to the determination result.

[0025] The preset user identity verification system for welding machine usage is regarded as prior art in the present invention. By verifying permissions based on the identity information input by the user and the preset permission rules, it ensures that only eligible users can use the welding machine and prohibits its use when the permissions are not met. The following details its implementation method and significance: System working principle: User identity verification: Before using the welding machine, the user needs to input identity information, such as username, employee number, or other authentication information. The system confirms whether the user's identity meets the requirements for using the welding machine by comparing the input identity information with the user data stored in the database.

[0026] Permission rule verification: The identity information of each user is preset with a certain permission level. For example, some users may only have the permission to operate simple welding machines, while some users may have the permission to use high-precision welding machines. The system verifies the permissions of the identity information according to the preset permission rules (such as the user's department, job type, training certificate, etc.). If the user meets the permission requirements, the system allows them to enable the welding machine; otherwise, operation is prohibited.

[0027] Controlling the enabling or disabling of the welding machine: After the system performs authentication and permission rule verification, it can control the enabling and disabling of the welding machine in real time. If the user authentication fails, the system will refuse to start the device. This system can be linked with hardware interfaces such as the control panel and control software of the welding machine to achieve physical-level start and stop control of the welding machine.

[0028] Significance of the system: Ensuring safety: The welding machine is a high-risk industrial device, and incorrect operation may lead to equipment damage or serious safety accidents. Through authentication and permission verification, it is ensured that only authorized personnel can operate the equipment, greatly reducing the risk of unqualified personnel operating the equipment. The system can restrict high-risk operations based on the user's operation history or qualification requirements. For example, untrained personnel cannot operate high-voltage welding machines to avoid safety hazards.

[0029] Improving operation management efficiency: Through permission verification, managers can real-time understand the usage situation of each welding machine, ensure that operators have the corresponding qualifications, and avoid unauthorized use or abuse of equipment. The system's automated authentication and permission verification reduce the workload of manual approval and monitoring, improving the overall efficiency of equipment management.

[0030] Enhancing operation traceability: Each enabling and disabling of the welding machine is associated with data such as user identity information and operation time. The system will record operation logs to provide complete operation traceability. This provides data support for equipment management, problem diagnosis, and liability investigation. This traceability not only helps trace problems but also facilitates later equipment maintenance and auditing.

[0031] Meeting compliance requirements: In many industrial safety fields, there are strict compliance requirements for the use of equipment, especially for equipment involving dangerous operations. The authentication system ensures that operators meet compliance standards, such as having received necessary training and holding valid operation certificates. This permission management mechanism helps meet the compliance requirements of enterprises and industries, avoiding legal disputes or safety accidents caused by improper operation.

[0032] Flexible permission management: Preset permission rules can be flexibly configured according to the company's organizational structure, personnel training situation, equipment type, etc., to adapt to different levels of management needs. The system can not only authenticate users but also dynamically adjust permission configurations according to permission rules to ensure the flexibility and scalability of the system under changing operation requirements.

[0033] When analyzing permission request responses based on a large log data set, the result is the permission verification response index. When analyzing security verification based on a large log data set, the result is the security verification completion index. The permission verification response index is calculated by analyzing the time series data of permission request response time. It reflects the response efficiency and load of the system when processing permission requests. The index combines factors such as the standard deviation of response time, the number of successfully processed requests, the penalty factor for failed requests, and the impact of request load.

[0034] Optimize system performance: The permission verification response index can accurately reflect the efficiency of the system in processing permission requests in different time windows. If the response time is too long or the standard deviation is too large, the system may have performance bottlenecks or overload problems. Through this index, administrators can promptly identify system performance problems and make optimization adjustments. The calculation of response time takes into account the situation of all requests, ensuring that the system can still maintain a high responsiveness under high load.

[0035] For welding machine users, a faster permission verification response time means that they can obtain authorization to use the equipment more quickly, thereby improving work efficiency. If the response time is too long, it may affect the user's operating experience and delay the work process. The permission verification response index can be used as an indicator for performance optimization to help system maintenance personnel adjust system resource allocation to improve the overall user experience.

[0036] Delays in request responses may be caused by malicious attacks (such as denial of service attacks) or system anomalies. Monitoring the permission verification response index can help identify these potential security risks and take measures to enhance system security.

[0037] The safety verification completion index is calculated by analyzing factors such as successful verification, failed verification, timed verification, and verification response time during the safety verification process. It reflects the efficiency and success rate of the system in performing safety verification.

[0038] The safety verification completion index shows the percentage of safety verifications that the system has successfully completed within a specific time period, which in turn reflects the system's performance in ensuring operational safety. A higher index indicates that the safety verification process is efficient and can effectively prevent unauthorized users from operating the welding machine, thereby ensuring safety. A lower index may indicate that there are more failed or timed verifications, which means that potential safety risks may not be identified and prevented in a timely manner.

[0039] The security verification completion index can help administrators promptly identify potential security risks in the system. If the system fails to conduct effective security verification, it may lead to unauthorized personnel obtaining operation permissions, increasing the probability of security incidents. Through this index, managers can quickly identify vulnerabilities in the system and perform targeted repairs. For example, a decrease in the index may indicate a slower verification response speed of the system, resulting in the security verification not being completed within the scheduled time, which may in turn pose security risks. In certain industries or regions, the use of equipment must comply with strict security specifications and legal requirements. The security verification completion index provides a quantitative standard that can assist equipment managers in tracking and recording the completion status of each verification process, ensuring that the system complies with relevant compliance requirements. Failure to complete security verification may lead to compliance issues and increase legal risks.

[0040] The permission verification response index and the security verification completion index are two key indicators for evaluating the operation efficiency and security of the system. Through continuous monitoring of these two indexes, potential performance bottlenecks and security risks can be promptly discovered, enabling the implementation of optimization measures to ensure the efficient and secure operation of the system. These two indexes provide quantitative data support to help managers make more reasonable decisions. For example, when the permission verification response index is low, managers can consider increasing system resources; when the security verification completion index drops, security resources can be strengthened to enhance the security of the system. The subsequent mentioned resource scheduling includes system resources and security resources. The two indexes can provide a basis for the direction of resource scheduling. According to this direction, resource scheduling can be carried out according to the preset scheduling method. The specific scheduling implementation method can be any one of the existing technologies well-known to those skilled in the art that can achieve the efficient and secure operation of the system, which is not limited. Ultimately, the efficient and secure operation of the system can be achieved. Through these two indexes, the system can intelligently evaluate when additional cloud resources need to be enabled or the allocation of local resources adjusted to ensure the efficient operation of the system under different load conditions.

[0041] The acquisition logic of the permission verification response index is as follows: Under a fixed time window, obtain the time series data corresponding to the response time of each request. To emphasize the response efficiency within the time window, calculate the response time impact value corresponding to the time series data: ; represents the standard deviation of the time series data, represents the th request's response time in the time series data, represents the total number of requests corresponding to the time series data, represents the response time impact value corresponding to the time series data, Denote a preset non - zero constant, which is used to avoid calculation anomalies; the core idea of this formula is to measure the overall response ability of time - series data. To emphasize the importance of quick response, an exponential function (exp) is used for calculation, so that larger response - time influence values will be exponentially amplified to highlight poor response performance. Standardization processing is used in the formula, that is , which can reduce the impact of uneven data distribution on the calculation and improve the adaptability to different load conditions.

[0042] Obtain the number of successfully processed permission verification requests, and at the same time consider the penalty for failed requests to calculate the penalty factor: ; denotes the number of successfully processed permission verification requests, denotes the penalty factor; this formula is used to measure the system's processing ability for failed requests to ensure an additional penalty in case of permission verification failure. The reason for using the logarithmic function: in the case of a large number of failed requests, the logarithmic function can relieve the growth rate so that the exponent will not drop too fast to prevent over - punishment. When the number of successfully processed requests is large, will also increase accordingly, thus encouraging the system to improve the success rate as much as possible.

[0043] Obtain the number of requests received in each preset request sub - window, and at the same time, to emphasize the impact of load on response, calculate the load influence value: ; denotes the average value of the number of requests received in all request sub - windows, denotes the standard deviation of the number of requests received in all request sub - windows, denotes the load influence value; the size of the load will directly affect the processing efficiency of permission verification. Therefore, by calculating the number of requests in the request sub - window and its standard deviation, the stability of the system under different loads can be evaluated. Since the increase in the number of requests often means an increase in the system's load pressure, to highlight the impact in high - load situations, the exponential function exp is used to amplify the influence value.

[0044] The calculation formula for the permission verification response index is: ; is a preset non - zero adjustment coefficient, , are both preset non - zero influence coefficients, which are used to adjust the weights of response time and load influence value to ensure adaptability in different scenarios. denotes the permission verification response index. By comprehensively considering factors such as response time, load, and failure penalty, the formula constructs a flexible, stable, and adjustable calculation model for the permission verification response index. This model can be used to evaluate the real - time permission verification ability of the system and provide data support for subsequent resource scheduling and optimization.

[0045] The acquisition logic of the security verification completion index is as follows: Under a fixed time window, obtain the total number of verifications for all requests , the number of successful verifications completed, the number of failed verifications , and the number of timed-out verifications , as well as the average response time of the verification , then calculate the ratio of the number of successful verifications completed to the total number of verifications for all requests to obtain the verification completion degree under the time window ; within a given time window, the proportion of verifications successfully completed by the system, as an intuitive measure of system efficiency. The calculation method uses the ratio of the number of successful verifications to the total number of verifications. This method can effectively measure the success rate of the overall verification, and the value range is between 0 and 1. When WC is close to 1, it indicates that the system has strong processing ability and almost all security verifications are completed; when WC is too low, it indicates that the verification success rate is poor, and there may be a high number of failures or timeouts.

[0046] Reflect the impact of failed and timed-out verifications on the overall security verification efficiency through a penalty term. The penalty term corresponding formula is: ; is the penalty term, is the failure penalty value, is the timeout penalty value; ; ; , are both preset non-zero penalty factors, and ; The penalty term is used to reflect the failures and timeouts encountered by the system during the security verification process. By increasing the penalty term, the final value of the security verification completion index AQ is reduced. The more the number of failed and timed-out verifications, the higher the P value, thereby reducing AQ to ensure that the system will not be misjudged as efficient in the case of a high failure rate. means that the impact of failed verifications is greater than that of timed-out verifications. This is because failed verifications usually mean that there are problems in the system authentication process, while timed-out verifications may be caused by short-term system congestion or network latency, so different weights are given.

[0047] This design method can more reasonably reflect the impact of different verification anomalies on overall security.

[0048] Considering the impact of response time, introduce an adjustment function : ; is the preset maximum verification acceptance response time, is a preset adjustment coefficient, with a value between 0 and 1; this function is used to introduce the impact of verification time on security evaluation. The shorter the verification time, the higher the verification efficiency of the system. Conversely, if the verification time is too long, it indicates that the system response is slow, which may affect the overall security. When decreases linearly, indicating that the verification response time exceeds the set maximum acceptable value, affecting the verification efficiency. Therefore, a certain deduction needs to be made for the security evaluation of the system. The linear decrease method is adopted to ensure that the adjustment range is controllable and will not over-punish short-term abnormal fluctuations.

[0049] The calculation formula for the security verification completion index is: ; is the security verification completion index. By quantifying the verification success rate, failure rate, timeout rate, and response time, a comprehensive security verification completion index AQ is provided to evaluate the verification efficiency of the system. The proportion WC of successful verifications is used as the basic score, directly reflecting the completion of the verification. The penalty term P for failed and timed-out verifications is used as a deduction item to ensure that the system is not considered efficient due to a large number of failures or timeouts. The adjustment function of the average response time further strengthens the influence of time factors in verification, ensuring that the system can operate under the premise of high efficiency and security. This formula not only focuses on the verification success rate but also combines the penalties for failures and timeouts, and further considers the influence of the response time, thus constructing a complete evaluation system that balances performance and security. This design method enables the security verification system to be dynamically adjusted according to different business requirements, improving the overall reliability and practicality.

[0050] Conducting local resource performance evaluation refers to: Substitute the security verification completion index and the permission verification response index into the pre-trained convolutional neural network model. The output result of the convolutional neural network model is 0 or 1. When the result is 0, it is determined that the local resource performance meets the preset operating conditions of the electric welding machine user identity verification system. When the result is 1, it is determined that the local resource performance does not meet the preset operating conditions of the electric welding machine user identity verification system. When the result is 1, temporary cloud resources are enabled.

[0051] Taking the security verification completion index and the permission verification response index as inputs, the CNN model is allowed to learn and process these two parameters to determine whether the local resources meet the preset operating conditions. When the output of the CNN model is 0, it indicates that under the current operating conditions, the local resources can meet the operating requirements of the electric welder user authentication system. At this time, there is no need to enable temporary cloud resources, and the system can continue to use the local resources for permission verification and security verification. If the local resources can meet the system requirements, the enabling of temporary cloud resources is avoided, unnecessary resource consumption is reduced, efficiency is improved, and operating costs are lowered. When the output of the CNN model is 1, it indicates that the current local resources cannot meet the operating requirements of the electric welder user authentication system. At this time, the system determines that it is necessary to start temporary cloud resources to supplement the deficiencies of the local resources.

[0052] Temporarily enabling cloud resources can dynamically expand system resources according to actual load requirements, avoiding situations where the system response time is delayed or the system cannot operate properly due to insufficient local resources. By temporarily using cloud resources, it is possible to ensure that the user authentication system of the electric welder is always in a good operating state, while avoiding the high costs of long-term use of cloud resources. By introducing the security verification completion index, it is possible to reflect the security and integrity of the system during the execution of verification, ensuring that the system is not easily attacked or tampered with when processing user authentication. Through the permission verification response index, it is possible to evaluate the response efficiency of the system in real time, especially under high load, ensuring that the usage permissions of the electric welder can be quickly verified and avoiding affecting production efficiency due to excessive response time. Inputting these two indexes into the convolutional neural network can comprehensively consider the security and performance of the system and achieve dynamic resource management.

[0053] The application of the convolutional neural network enables the system to automatically make judgments through the trained model to decide when to enable cloud resources and when to rely on local resources, which makes the entire resource management more intelligent and automated. Based on the judgment of the model output, the system can automatically adapt to different load conditions without manual intervention, thereby improving the autonomy and response speed of the system.

[0054] The permission verification response index reflects the response efficiency of the system during the permission verification process. If this index is low, it indicates that the system responds sluggishly or is not processed in a timely manner under high load. Managers can consider increasing system resources (such as computing power or storage capacity) to improve processing efficiency.

[0055] The security verification completion index measures the efficiency of the system when all verification requests are completed. If this index decreases, it indicates that the verification efficiency of the system has decreased, and there may be security vulnerabilities or unreasonable resource allocation. At this time, managers can strengthen security resources (such as firewalls, identity verification processing capabilities, etc.) to improve security and reduce failures or potential risks.

[0056] Two indices provide the direction of the system's resource requirements in different aspects: when the permission verification response index is low, the system may require more computing resources to improve processing speed. When the security verification completion index is low, more security resources may be needed to ensure the efficiency and security of the verification. Based on these two indices, the system can perform dynamic resource scheduling, ensuring the system can operate efficiently and securely by appropriately increasing computing or security resources.

[0057] The convolutional neural network model takes these two indices (security verification completion index and permission verification response index) as inputs, comprehensively considering the current system's performance and resource requirements. The training process of the convolutional neural network can automatically identify the bottlenecks of the system under different conditions and decide whether resource scheduling is needed based on these inputs. Inputting the security verification completion index can help the network identify potential performance degradation during the verification process and make timely adjustments.

[0058] Relying solely on the permission verification response index may not fully reflect the security and stability of the system, especially when the efficiency of the security verification is low. By inputting the security verification completion index, the convolutional neural network can more accurately evaluate the impact of the security verification on the overall system performance, thereby improving the prediction accuracy of resource scheduling and ensuring that additional security resources can be activated when needed.

[0059] Even if the permission verification response index is good, if the security verification completion index is low, the system may face high security risks. By combining these two indices and inputting them into the convolutional neural network, it can be ensured that the system does not neglect the effectiveness of the security verification even when the permission response is efficient. In this way, the network not only focuses on performance issues but also on security, ensuring a balance between the performance and security of the system. By inputting these key metrics, the convolutional neural network can automatically learn and adjust the resource allocation strategy, not only considering the timeliness of the permission response but also effectively managing the security of the verification, intelligently completing resource scheduling, and avoiding errors caused by human intervention.

[0060] The total duration value of enabling temporary cloud resources is determined by the following logic: Obtain the security verification completion index and the permission verification response index , and then substitute them into the resource scheduling duration estimation formula: ; and are both preset non-zero conversion coefficients. These two coefficients are used to adjust the influence degree of the security verification completion index and the permission verification response index on the total duration value. Through these parameters, the influence of the security verification and the permission verification can be adjusted according to the specific requirements and priorities of the system. It may reflect the impact of security verification on the resource scheduling duration, while this reflects the impact of permission verification response. Adjusting these two coefficients enables the system to respond dynamically to different situations. is the preset resource scheduling buffer duration, and is the total duration value for enabling temporary cloud resources. Through the prediction and dynamic scheduling of system resource requirements, it ensures that cloud resources are enabled when needed and reasonably plans their usage duration according to the preset parameters to optimize resource utilization and cost management. By integrating the efficiency of security verification and permission verification with resource scheduling, it ensures that the utilization of resources is targeted and avoids unnecessary resource waste. By precisely calculating the duration of enabling cloud resources, the system can manage cloud resources more effectively, reducing costs while improving performance.

[0061] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0062] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0063] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0064] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0065] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for verifying the use authority of electric welding machines based on big data, characterized in that: The following steps are involved: Step 1: When local resources are used, in a fixed time window, the welder use permission verification data and permission request response data are collected through the log records in the preset welder use user identity authentication system to form a large log data set; Step 2: Perform permission request response analysis and security verification analysis based on the large log data set, and perform local resource performance evaluation based on the results of the permission request response analysis and security verification analysis; Step 3: Decide whether to enable temporary cloud resources based on the local resource performance evaluation results, and when enabling temporary cloud resources, estimate the resource scheduling duration based on the results of permission request response analysis and security verification analysis to obtain the total duration value of enabling temporary cloud resources; Step 4: After the total duration of enabling temporary cloud resources is reached, close the temporary cloud resources, restore to local resource use, and perform steps 1 to 3 again.

2. The method for verifying the use authority of an electric welder based on big data according to claim 1, characterized in that: The preset welding machine user identity authentication system is used to verify the authority based on the identity information input by the user and the preset authority rules, determine whether the user has the authority to use the welding machine, and control the enabling or disabling of the welding machine based on the determination result.

3. The method for verifying the use authority of an electric welder based on big data according to claim 2, characterized in that: When performing permission request response analysis based on a large log data set, the result obtained is the permission verification response index. When performing security verification analysis based on a large log data set, the result obtained is the security verification completion index.

4. The method for verifying the use authority of an electric welding machine based on big data according to claim 3 is characterized in that: The logic for obtaining the permission verification response index is: In a fixed time window, obtain the time series data corresponding to the response time of each request. In order to emphasize the response efficiency within the time window, calculate the response time impact value corresponding to the time series data: ; represents the standard deviation of the time series data, Indicates the time series data The response time for a request, Indicates the total number of requests corresponding to the time series data. Indicates the response time impact value corresponding to the time series data, represents a preset non-zero constant; Get the number of successfully processed permission verification requests, take into account the penalty for failed requests, and calculate the penalty factor: ; Indicates the number of successfully processed permission verification requests. represents the penalty factor; Get the number of requests received by each preset request subwindow, and calculate the load impact value to emphasize the impact of load on response: ; Represents the average number of requests received by all request sub-windows, represents the standard deviation of the number of requests received by all request sub-windows, Indicates the load impact value; The calculation formula of the authority verification response index is: ; is the preset non-zero adjustment coefficient, , are all preset non-zero influence coefficients, Indicates the permission verification response index.

5. The method for verifying the use authority of a welding machine based on big data according to claim 4 is characterized in that: The logic for obtaining the safety verification completion index is: Get the total number of verifications for all requests within a fixed time window , the number of successful verifications completed, the number of failed verifications , the number of timed verifications , and the average response time for verification Then calculate the ratio of the number of successful verifications completed to the total number of verifications requested to obtain the verification completion rate within the time window. ; The penalty item reflects the impact of failed and timed verification on the overall safety verification efficiency. The corresponding formula for the penalty item is: ; is the penalty item, is the failure penalty value, is the timeout penalty value; ; ; , are all preset non-zero penalty factors, and ; Considering the impact of response time, an adjustment function is introduced : ; is the preset maximum verification acceptance response time, It is the preset adjustment coefficient, and its value is between 0 and 1; The calculation formula for the safety verification completion index is: ; Complete index for safety verification.

6. The method for verifying the use authority of an electric welder based on big data according to claim 5, characterized in that: Performing a local resource performance assessment means: The security verification completion index and the authority verification response index are substituted into the pre-trained convolutional neural network model. The output result of the convolutional neural network model is 0 or 1. When the result is 0, it is determined that the local resource performance meets the preset operating conditions of the user identity authentication system for the welding machine. When the result is 1, it is determined that the local resource performance does not meet the preset operating conditions of the user identity authentication system for the welding machine. When the result is 1, temporary cloud resources are enabled.

7. The method for verifying the use authority of a welding machine based on big data according to claim 6, characterized in that: The total duration for enabling temporary cloud resources is determined by the following logic: Get the safety verification completion index , Authority Verification Response Index , and then substitute it into the resource scheduling duration estimation formula: ; , are all preset non-zero conversion coefficients, Schedule buffer duration for preset resources, The total duration for enabling temporary cloud resources.

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