Digitalized operation monitoring management platform

Abstract

The application relates to the field of digital operation monitoring management, in particular to a digital operation monitoring management platform, which comprises a personnel authorization module, a data acquisition module, a network data analysis module, an equipment management module and an early warning processing module; the equipment management module analyzes operation state parameters of each operation equipment received; the network data analysis module is used for receiving and analyzing each network data information; the personnel authorization module distributes permissions according to personal information of each staff; the digital operation monitoring management platform can provide comprehensive operation data and in-depth analysis results, and provide scientific and accurate basis for enterprise decision-making, so as to improve the quality and success rate of decision-making.

Description

Technical Field

[0001] This invention relates to the field of digital operation monitoring and management, specifically to a digital operation monitoring and management platform. Background Technology

[0002] In today's highly competitive market environment, enterprises need to continuously enhance their competitiveness. Traditional operation and management methods often rely on manual operation and experience-based judgment, which are inefficient and prone to errors. With the development of digital technology, enterprises realize that digital transformation can optimize operational processes, improve decision-making speed and accuracy, and thus stand out in market competition. Traditional manufacturing enterprises face challenges such as cost control, production efficiency improvement, and product quality improvement. Digital operation monitoring and management platforms can comprehensively monitor and optimize production equipment, material supply, and personnel operations, helping enterprises to meet these challenges. They integrate data from various departments within the enterprise onto a unified platform, enabling data sharing and collaborative work. Traditional operation and management methods can usually only perform post-event analysis, making it difficult to monitor and provide early warnings of operational processes in real time. For example, equipment failures are often only discovered after the equipment has stopped operating, leading to production interruptions, increased maintenance costs, and other consequences. Digital operation monitoring and management platforms, leveraging modern information technology, can monitor equipment operating status and business process execution in real time. Once an anomaly occurs, they can immediately issue an alert, enabling enterprises to take timely measures to avoid losses. Summary of the Invention

[0003] To address the technical problems raised in the background, this invention provides a digital operation monitoring and management platform.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] This invention relates to a digital operation monitoring and management platform, comprising a personnel authorization module, a data acquisition module, a network data analysis module, an equipment management module, an early warning processing module, and a platform data center.

[0006] The personnel authorization module assigns permissions based on the personal information of each staff member and logs into the management platform using a numeric password. The specific method for obtaining the password is as follows:

[0007] The output of the personnel authorization module connects to the input of the platform data center. Each staff member uploads their personal information to the platform data center, including their name, job title, and preset initial password. Upon receiving the personal information, the platform data center generates a personal information database and assigns permissions to each staff member based on their job title. Administrators have the highest authority and can manage the platform comprehensively, including user management, permission allocation, and system settings. Department managers have the authority to manage their department's business data and personnel, and can view and analyze relevant monitoring data and reports. Ordinary employees are granted specific data access and operation permissions based on their job responsibilities and can only view and process information related to their business. A corresponding operation interface is generated based on the platform operation permissions granted to each staff member. Each staff member enters their personal initial password via computer or mobile phone, retrieves the preset initial password from the platform data center, and matches their personal initial password with the preset initial password. If the match is successful, the user enters the corresponding platform operation interface; otherwise, the user exits the interface. If the user enters the wrong password more than three times, the interface is locked.

[0008] The data acquisition module collects various data information based on the connection information port, and the specific acquisition method is as follows:

[0009] By using a resource monitor, the memory usage of the IT system can be monitored over a specific period of time, and various memory usage parameters can be collected for that specific period of time.

[0010] The network traffic is monitored in real time within a specific period by a traffic analyzer, and various network traffic parameters are collected in real time.

[0011] High-definition cameras are used to monitor each stage of the production line in real time and obtain the operating status parameters of each piece of equipment.

[0012] The output of the data acquisition module is connected to the input of the network data analysis module and the device management module. It sends memory usage parameters and network traffic parameters to the network data analysis module and the operating status parameters of each running device to the device management module.

[0013] The network data analysis module is used to receive various data and analyze them separately. The specific acquisition process is as follows:

[0014] The input end of the network data analysis module is connected to the output end of the platform data center, and the output end of the network data analysis module is connected to the input end of the early warning processing module to analyze various network traffic parameters, specifically:

[0015] D1: Monitor the current network bandwidth in real time, obtain the current actual network bandwidth value, mark it as KL, extract the total network bandwidth value KA of the platform data center, and calculate using the formula. The bandwidth utilization rate (KJ) was obtained.

[0016] D2: Set the data packet size to GH, record the transmission time from the sender to the receiver, and stop recording the data packet transmission time. Subtract the transmission and reception times to obtain the data transmission time, denoted as DN. Calculate the data transmission time using the formula. Obtain the data packet transmission rate GD;

[0017] D3: Extract network traffic parameters from the data acquisition module within a specific time period, calculate the average traffic value by averaging the obtained network traffic parameters, and label it as KP;

[0018] D4: Calculate the obtained bandwidth utilization, packet transmission rate, and average traffic value using the formula... The traffic operation value GPI is obtained, where F1, F2 and F3 are preset proportional coefficients. The preset traffic operation value of the data center is extracted, and the actual traffic operation value is compared with the preset traffic operation value. If the actual traffic operation value is less than the preset traffic operation value, a traffic anomaly signal is generated and sent to the early warning processing module.

[0019] Analyze various financial information items, extract historical cost parameter values ​​from each item, divide these historical cost parameter values ​​into several time-sharing cost parameter values, arrange these time-sharing cost parameter values ​​in chronological order, and connect them sequentially to obtain a cost change graph. Based on the cost change graph, obtain the maximum and minimum cost values, labeled HF and HK respectively. Calculate the average cost value from the obtained time-sharing cost parameter values, labeled HD. Extract the start time point and end time point of the platform data center's historical costs, labeled as the end time point. Calculate the cost runtime by subtracting the start and end times, labeled HS. Calculate the maximum, minimum, average, and cost runtime values ​​using a formula. The cost fluctuation value is obtained, where D1, D2, D3 and D4 are preset proportional coefficients. The preset cost fluctuation threshold of the data center is extracted. The actual cost fluctuation value is compared with the preset cost fluctuation threshold. If the actual cost fluctuation value is greater than the preset cost fluctuation threshold, it indicates that the cost fluctuation is large. A fluctuation signal is generated and sent to the corresponding early warning processing module.

[0020] The equipment management module analyzes the operating status parameters received from each operating device, and the specific method for obtaining these parameters is as follows:

[0021] The output of the equipment management module connects to the input of the early warning processing module. It establishes files for each piece of equipment, including equipment name, end-of-life, maintenance period, production date, purchase date, and installation location. This file content is sent to the platform data center. The platform data center receives the file content and categorizes the equipment into production equipment, office equipment, and testing equipment. Based on the file content of each equipment type, a unique code is generated. The production date and end-of-life corresponding to each equipment type are extracted from the platform data center. These values ​​are then added together to obtain the end-of-life time for each equipment type, up to the current system time. For each type of equipment, the current time point is subtracted from the corresponding scrap time point to obtain the scrap value. If the scrap value is positive, it means that the corresponding equipment has exceeded its scrap life and is marked as a piece of equipment to be scrapped. A processing signal is generated and sent to the early warning processing module to obtain the unique code corresponding to the piece of equipment to be scrapped. Based on the unique code, the piece of equipment to be scrapped is removed from the platform center. The maintenance time period and the last maintenance time point of each type of equipment set in the platform data center are extracted. The maintenance time period and the last maintenance time point of each type of equipment are added together to obtain the current maintenance date of each type of equipment. Each type of equipment that needs maintenance is marked as maintenance equipment and a maintenance signal is generated and sent to the corresponding early warning processing module.

[0022] The early warning processing module processes each signal sent by other modules, and the specific acquisition process is as follows:

[0023] The output of the early warning processing module is connected to the input of the platform's data center. Upon receiving a traffic anomaly signal, the early warning processing module sends the signal to the monitoring and management platform. The monitoring and management platform obtains the current external network congestion traffic value through a traffic analyzer, extracts the preset external network congestion traffic threshold of the data center, and compares the actual external network congestion traffic value with the preset threshold. If the actual external network congestion traffic value is greater than the preset threshold, it is marked as a traffic congestion point. The monitoring and management platform generates a clearing command and sends it to the corresponding processing terminal. The processing terminal reduces the current data transmission rate, restricts access from specific IP addresses, and alleviates the current network pressure.

[0024] Upon receiving a fluctuation signal, the fluctuation signal is sent to the monitoring and management platform. The monitoring and management platform generates control instructions and sends them to the corresponding cost control terminal. Upon receiving the control instructions, the cost control terminal takes cost control measures, including reducing unnecessary expenses, optimizing the procurement process, and reducing inventory levels.

[0025] Upon receiving a maintenance signal, the signal is sent to the monitoring and management platform. The platform obtains the location information of the corresponding device through the positioning sensor, uses the device location as the origin, obtains the mobile terminal location of each maintenance personnel within the radius, connects the origin and the mobile terminal location of each maintenance personnel to obtain the maintenance distance, selects the minimum distance value, marks the corresponding staff member as the person to be repaired, and generates a maintenance instruction and sends it to the corresponding person to be repaired.

[0026] Compared with existing technologies, the beneficial effects of this invention are as follows: It dynamically adjusts bandwidth allocation to different business departments based on network traffic conditions at different times; it rationally allocates human resources based on the manpower needs analysis of each project, ensuring maximum resource utilization and avoiding resource idleness or overuse. The digital operation monitoring and management platform helps to digitize and standardize business processes. By setting standard processes and automated monitoring mechanisms, it reduces errors and delays that may be caused by manual intervention. Real-time monitoring and fault prediction of equipment and systems enable timely preventative maintenance. Compared with traditional repairs after a failure occurs, preventative maintenance can significantly reduce losses caused by sudden equipment failures, including production interruption losses, equipment repair costs, and safety accident losses. Rational resource utilization can directly reduce costs. Precise inventory management reduces inventory backlog and lowers inventory costs. Optimized network bandwidth allocation avoids unnecessary bandwidth leasing fees. Standardized and automated business processes reduce reliance on manpower. Some repetitive and routine tasks can be completed automatically by the platform. Enterprises can streamline personnel or reallocate human resources to more valuable positions, thereby reducing labor costs. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not drawn to scale according to the actual size, but are intended to illustrate the main idea of ​​the present invention.

[0028] Figure 1 This is a schematic diagram of the principle of the present invention.

[0029] Figure 2 This is a cost variation diagram for the present invention. Detailed Implementation

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are also within the scope of protection of the present invention.

[0031] like Figure 1-2 As shown, the present invention is a digital operation monitoring and management platform, including a personnel authorization module, a data acquisition module, a network data analysis module, an equipment management module, an early warning processing module, and a platform data center.

[0032] The personnel authorization module assigns permissions based on the personal information of each staff member and logs into the management platform using a numeric password. The specific method for obtaining the password is as follows:

[0033] The output of the personnel authorization module connects to the input of the platform data center. Each staff member uploads their personal information to the platform data center, including their name, job title, and preset initial password. Upon receiving the personal information, the platform data center generates a personal information database and assigns permissions to each staff member based on their job title. Administrators have the highest authority and can manage the platform comprehensively, including user management, permission allocation, and system settings. Department managers have the authority to manage their department's business data and personnel, and can view and analyze relevant monitoring data and reports. Ordinary employees are granted specific data access and operation permissions based on their job responsibilities and can only view and process information related to their business. A corresponding operation interface is generated based on the platform operation permissions granted to each staff member. Each staff member enters their personal initial password via computer or mobile phone, retrieves the preset initial password from the platform data center, and matches their personal initial password with the preset initial password. If the match is successful, the user enters the corresponding platform operation interface; otherwise, the user exits the interface. If the user enters the wrong password more than three times, the interface is locked.

[0034] The data acquisition module collects various data information based on the connection information port, and the specific acquisition method is as follows:

[0035] By using a resource monitor, the memory usage of the IT system can be monitored over a specific period of time, and various memory usage parameters can be collected for that specific period of time.

[0036] The network traffic is monitored in real time within a specific period by a traffic analyzer, and various network traffic parameters are collected in real time.

[0037] High-definition cameras are used to monitor each stage of the production line in real time and obtain the operating status parameters of each piece of equipment.

[0038] The output of the data acquisition module is connected to the input of the network data analysis module and the device management module. It sends memory usage parameters and network traffic parameters to the network data analysis module and the operating status parameters of each running device to the device management module.

[0039] The network data analysis module is used to receive various data and analyze them separately. The specific acquisition process is as follows:

[0040] The input end of the network data analysis module is connected to the output end of the platform data center, and the output end of the network data analysis module is connected to the input end of the early warning processing module to analyze various network traffic parameters, specifically:

[0041] D1: Monitor the current network bandwidth in real time, obtain the current actual network bandwidth value, mark it as KL, extract the total network bandwidth value KA of the platform data center, and calculate using the formula. The bandwidth utilization rate (KJ) was obtained.

[0042] D2: Set the data packet size to GH, record the transmission time from the sender to the receiver, and stop recording the data packet transmission time. Subtract the transmission and reception times to obtain the data transmission time, denoted as DN. Calculate the data transmission time using the formula. Obtain the data packet transmission rate GD;

[0043] D3: Extract network traffic parameters from the data acquisition module within a specific time period, calculate the average traffic value by averaging the obtained network traffic parameters, and label it as KP;

[0044] D4: Calculate the obtained bandwidth utilization, packet transmission rate, and average traffic value using the formula... The traffic operation value GPI is obtained, where F1, F2 and F3 are preset proportional coefficients. The preset traffic operation value of the data center is extracted, and the actual traffic operation value is compared with the preset traffic operation value. If the actual traffic operation value is less than the preset traffic operation value, a traffic anomaly signal is generated and sent to the early warning processing module.

[0045] Analyze various financial information items, extract historical cost parameter values ​​from each item, divide these historical cost parameter values ​​into several time-sharing cost parameter values, arrange these time-sharing cost parameter values ​​in chronological order, and connect them sequentially to obtain a cost change graph. Based on the cost change graph, obtain the maximum and minimum cost values, labeled HF and HK respectively. Calculate the average cost value from the obtained time-sharing cost parameter values, labeled HD. Extract the start time point and end time point of the platform data center's historical costs, labeled as the end time point. Calculate the cost runtime by subtracting the start and end times, labeled HS. Calculate the maximum, minimum, average, and cost runtime values ​​using a formula. The cost fluctuation value is obtained, where D1, D2, D3 and D4 are preset proportional coefficients. The preset cost fluctuation threshold of the data center is extracted. The actual cost fluctuation value is compared with the preset cost fluctuation threshold. If the actual cost fluctuation value is greater than the preset cost fluctuation threshold, it indicates that the cost fluctuation is large. A fluctuation signal is generated and sent to the corresponding early warning processing module.

[0046] The equipment management module analyzes the operating status parameters received from each operating device, and the specific method for obtaining these parameters is as follows:

[0047] The output of the equipment management module connects to the input of the early warning processing module. It establishes files for each piece of equipment, including equipment name, end-of-life, maintenance period, production date, purchase date, and installation location. This file content is sent to the platform data center. The platform data center receives the file content and categorizes the equipment into production equipment, office equipment, and testing equipment. Based on the file content of each equipment type, a unique code is generated. The production date and end-of-life corresponding to each equipment type are extracted from the platform data center. These values ​​are then added together to obtain the end-of-life time for each equipment type, up to the current system time. For each type of equipment, the current time point is subtracted from the corresponding scrap time point to obtain the scrap value. If the scrap value is positive, it means that the corresponding equipment has exceeded its scrap life and is marked as a piece of equipment to be scrapped. A processing signal is generated and sent to the early warning processing module to obtain the unique code corresponding to the piece of equipment to be scrapped. Based on the unique code, the piece of equipment to be scrapped is removed from the platform center. The maintenance time period and the last maintenance time point of each type of equipment set in the platform data center are extracted. The maintenance time period and the last maintenance time point of each type of equipment are added together to obtain the current maintenance date of each type of equipment. Each type of equipment that needs maintenance is marked as maintenance equipment and a maintenance signal is generated and sent to the corresponding early warning processing module.

[0048] The early warning processing module processes each signal sent by other modules, and the specific acquisition process is as follows:

[0049] The output of the early warning processing module is connected to the input of the platform's data center. Upon receiving a traffic anomaly signal, the early warning processing module sends the signal to the monitoring and management platform. The monitoring and management platform obtains the current external network congestion traffic value through a traffic analyzer, extracts the preset external network congestion traffic threshold of the data center, and compares the actual external network congestion traffic value with the preset threshold. If the actual external network congestion traffic value is greater than the preset threshold, it is marked as a traffic congestion point. The monitoring and management platform generates a clearing command and sends it to the corresponding processing terminal. The processing terminal reduces the current data transmission rate, restricts access from specific IP addresses, and alleviates the current network pressure.

[0050] Upon receiving a fluctuation signal, the fluctuation signal is sent to the monitoring and management platform. The monitoring and management platform generates control instructions and sends them to the corresponding cost control terminal. Upon receiving the control instructions, the cost control terminal takes cost control measures, including reducing unnecessary expenses, optimizing the procurement process, and reducing inventory levels.

[0051] Upon receiving a maintenance signal, the signal is sent to the monitoring and management platform. The platform obtains the location information of the corresponding device through the positioning sensor, uses the device location as the origin, obtains the mobile terminal location of each maintenance personnel within the radius, connects the origin and the mobile terminal location of each maintenance personnel to obtain the maintenance distance, selects the minimum distance value, marks the corresponding staff member as the person to be repaired, and generates a maintenance instruction and sends it to the corresponding person to be repaired.

[0052] The foregoing description is illustrative of the invention and should not be construed as limiting it. Although several exemplary embodiments of the invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the invention. Therefore, all such modifications are intended to be included within the scope of the invention as defined in the claims. It should be understood that the foregoing description is illustrative of the invention and should not be construed as limiting it to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.

Claims

1. A digital operation monitoring and management platform, comprising a personnel authorization module, a data acquisition module, a network data analysis module, an early warning processing module, and a platform data center, characterized in that: It also includes an equipment management module; The equipment management module analyzes the operating status parameters received from each piece of equipment. Its output connects to the input of the early warning processing module, creating files for each piece of equipment and sending the file content to the platform data center. The platform data center receives the file content, categorizes the equipment into different types, generates a unique code based on the file content of each type, extracts the production date and scrap age corresponding to each type of equipment from the platform data center, adds the production date and scrap age for each type of equipment to obtain the corresponding scrap age point, and then compares the current time and the corresponding scrap age point for each type of equipment up to the current system time. The scrap value is obtained by subtracting the values. If the scrap value is positive, it means that the corresponding equipment has exceeded its scrap life. It is marked as equipment to be scrapped, and a processing signal is generated and sent to the early warning processing module. The unique code corresponding to the equipment to be scrapped is obtained. Based on the unique code, the equipment to be scrapped is removed from the platform center. The maintenance period and the last maintenance time point of each type of equipment set in the platform data center are extracted. The maintenance period and the last maintenance time point of each type of equipment are added together to obtain the current maintenance date of each type of equipment. Each type of equipment that needs maintenance is marked as maintenance equipment, and a maintenance signal is generated and sent to the corresponding early warning processing module. The types include production equipment, office equipment, and testing equipment. The network data analysis module is used to receive various data and analyze them separately. The specific process is as follows: The input end of the network data analysis module is connected to the output end of the platform data center, and the output end of the network data analysis module is connected to the input end of the early warning processing module to analyze various network traffic parameters, specifically: D1: Monitor the current network bandwidth in real time, obtain the current actual network bandwidth value, mark it as KL, extract the total network bandwidth value KA of the platform data center, and calculate using the formula. The bandwidth utilization rate (KJ) was obtained. D2: Set the data packet size to GH, record the transmission time from the sender to the receiver, and stop recording the data packet transmission time. Subtract the transmission and reception times to obtain the data transmission time, denoted as DN. Calculate the data transmission time using the formula. Obtain the data packet transmission rate GD; D3: Extract network traffic parameters from the data acquisition module within a specific time period, calculate the average traffic value by averaging the obtained network traffic parameters, and label it as KP; D4: Calculate the obtained bandwidth utilization, packet transmission rate, and average traffic value using the formula... The traffic operation value GPI is obtained, where F1, F2 and F3 are preset proportional coefficients. The preset traffic operation value of the data center is extracted. The actual traffic operation value is compared with the preset traffic operation value. If the actual traffic operation value is less than the preset traffic operation value, a traffic anomaly signal is generated and sent to the early warning processing module. Analyze various financial information items, extract historical cost parameter values ​​from each item, divide these historical cost parameter values ​​into several time-sharing cost parameter values, arrange these time-sharing cost parameter values ​​in chronological order, and connect them sequentially to obtain a cost change graph. Based on the cost change graph, obtain the maximum and minimum cost values, labeled HF and HK respectively. Calculate the average cost value from the obtained time-sharing cost parameter values, labeled HD. Extract the start time point and end time point of the platform data center's historical costs, labeled as the end time point. Calculate the cost runtime by subtracting the start and end times, labeled HS. Calculate the maximum, minimum, average, and cost runtime values ​​using a formula. The cost fluctuation value is obtained, where D1, D2, D3 and D4 are preset proportional coefficients. The preset cost fluctuation threshold of the data center is extracted. The actual cost fluctuation value is compared with the preset cost fluctuation threshold. If the actual cost fluctuation value is greater than the preset cost fluctuation threshold, it indicates that the cost fluctuation is large. A fluctuation signal is generated and sent to the corresponding early warning processing module. The data acquisition module collects various data information based on the connection information port, and the specific method is as follows: The memory usage of the IT system is monitored in a specific period by a resource monitor, and various memory usage parameters are collected during that period. The network traffic is monitored in real time by a traffic analyzer, and various network traffic parameters are collected in real time. The operation status parameters of each operating device are obtained by real-time monitoring of each link in the production line by a high-definition camera. The output of the data acquisition module is connected to the input of the network data analysis module and the device management module. It sends memory usage parameters and network traffic parameters to the network data analysis module and the operating status parameters of each running device to the device management module.

2. The digital operation monitoring and management platform according to claim 1, characterized in that, The early warning processing module processes each signal sent by the other modules, and the specific process is as follows: The output of the early warning processing module is connected to the input of the platform's data center. Upon receiving a traffic anomaly signal, the early warning processing module sends the signal to the monitoring and management platform. The monitoring and management platform obtains the current external network congestion traffic value through a traffic analyzer, extracts the preset external network congestion traffic threshold of the data center, and compares the actual external network congestion traffic value with the preset threshold. If the actual external network congestion traffic value is greater than the preset threshold, it is marked as a traffic congestion point. The monitoring and management platform generates a clearing command and sends it to the corresponding processing terminal. The processing terminal reduces the current data transmission rate and restricts access from specific IP addresses. Upon receiving a fluctuation signal, the fluctuation signal is sent to the monitoring and management platform. The monitoring and management platform generates control instructions and sends them to the corresponding cost control terminal. Upon receiving the control instructions, the cost control terminal takes cost control measures, including reducing unnecessary expenses, optimizing the procurement process, and reducing inventory levels. Upon receiving a maintenance signal, the signal is sent to the monitoring and management platform. The platform obtains the location information of the corresponding device through the positioning sensor, uses the device location as the origin, obtains the mobile terminal location of each maintenance personnel within the radius, connects the origin and the mobile terminal location of each maintenance personnel to obtain the maintenance distance, selects the minimum distance value, marks the corresponding staff member as the person to be repaired, and generates a maintenance instruction and sends it to the corresponding person to be repaired.

3. The digital operation monitoring and management platform according to claim 1, characterized in that, The personnel authorization module assigns permissions based on the personal information of each staff member and allows them to log in to the management platform using a digital password, as follows: The output of the personnel authorization module connects to the input of the platform data center. Each staff member uploads their personal information to the platform data center, including their name, job title, and preset initial password. Upon receiving the personal information, the platform data center generates a personal information database and assigns permissions to each staff member based on their job title. The administrator has the highest authority and manages the platform comprehensively, including user management, permission allocation, and system settings. Department managers are responsible for managing their department's business data and personnel permissions, and viewing and analyzing relevant monitoring data and reports. Ordinary employees are granted specific data access and operation permissions based on their job responsibilities and can only view and process information related to their business. A corresponding operation interface is generated based on the platform operation permissions granted to each staff member. Each staff member enters their personal initial password via computer or mobile phone, retrieves the preset initial password from the platform data center, and matches their personal initial password with the preset initial password. If the match is successful, the user enters the corresponding platform operation interface; otherwise, the user exits the interface. If the user enters the wrong password more than three times, the interface is locked.

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