Cloud side end monitoring system based on Internet of Things data

Abstract

The invention discloses a cloud side end monitoring system based on Internet of Things data, which relates to the technical field of cloud side ends and comprises a data transmission acquisition module, a performance information analysis module, a data safety monitoring module, an extension information analysis module, a platform operation monitoring module, a state prompt and a database. The real-time evaluation coefficient and the performance evaluation coefficient corresponding to the target cloud side end are obtained through analysis, then the high-efficiency evaluation coefficient corresponding to the target cloud side end is better obtained, the safety evaluation coefficient and the expansibility evaluation coefficient corresponding to the target cloud side end are analyzed, and the intelligence evaluation coefficient corresponding to the target cloud side end is obtained. According to the technical scheme of the invention, the target cloud side end is extracted, and the intelligence corresponding to the target cloud side end is judged, so that the stronger and more efficient cloud side end service capability is provided, the security and privacy of data transmission are better guaranteed, and the intelligence of the cloud side end technology is better displayed.

Description

Technical Field

[0001] This invention relates to the field of cloud-edge-device technology, and more specifically to a cloud-edge-device monitoring system based on Internet of Things (IoT) data. Background Technology

[0002] With the widespread application of cloud-edge-device technology, not only is modern life more intelligent, but cloud-edge-device technology can also better reduce data transmission costs and distribute load, effectively enhance data privacy and security, effectively support offline work and improve decision-making efficiency, provide more powerful and efficient capabilities for IoT data, reduce dependence on basic servers, and improve data processing speed and real-time performance.

[0003] Current technology for data processing is relatively simplistic and lacks comprehensive analysis, which creates a risk of single points of failure. This not only negatively impacts data transmission and analysis but also hinders final completion and efficiency. It leads to data transmission delays, affecting real-time performance and user experience, consumes significant bandwidth resources, and makes data vulnerable to hacker attacks and information theft during transmission, potentially causing the leakage and loss of important information. Furthermore, it lacks flexibility and cannot meet ever-changing task requirements, significantly delaying task completion, greatly reducing efficiency, and wasting effort. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a cloud-edge-device monitoring system based on Internet of Things (IoT) data.

[0005] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a cloud-edge-device monitoring system based on Internet of Things data, including: a data transmission acquisition module, used to acquire data transmission information corresponding to the target cloud-edge device, wherein the data transmission information includes transmission rate and response time, and then analyzes and obtains the real-time evaluation coefficient corresponding to the target cloud-edge device;

[0006] The performance information analysis module is used to obtain the performance information corresponding to the target cloud edge, including bandwidth consumption rate, network speed, and network latency. Then, it analyzes and obtains the performance evaluation coefficient corresponding to the target cloud edge, and based on the real-time evaluation coefficient corresponding to the target cloud edge, it analyzes and obtains the efficiency evaluation coefficient corresponding to the target cloud edge.

[0007] The data security monitoring module is used to analyze the identity compliance evaluation coefficient of the user in the target cloud edge based on the login account information corresponding to the login address of the target cloud edge user, and obtain the login duration of the target cloud edge, and then analyze and obtain the security evaluation coefficient of the target cloud edge.

[0008] The extended information analysis module is used to obtain the capacity information corresponding to the target cloud edge device. The capacity information includes the number of devices, the number of interfaces, the data demand, and the space capacity, and then analyzes and obtains the scalability evaluation coefficient corresponding to the target cloud edge device.

[0009] The platform operation monitoring module is used to analyze the efficiency evaluation coefficient, security evaluation coefficient, and scalability evaluation coefficient of the target cloud edge device, and then obtain the intelligence evaluation coefficient of the target cloud edge device. Based on the intelligence evaluation coefficient of the target cloud edge device, the intelligence of the target cloud edge device is judged.

[0010] Status prompts are used to issue early warnings when the intelligence of the target cloud edge fails to meet the requirements.

[0011] Preferably, the analysis yields the real-time performance evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows:

[0012] Through calculation formula The analysis yields the real-time evaluation coefficient Τ corresponding to the target cloud edge, κ1 and κ2 represent the weighting factors of the set transmission rate and response time, respectively, φ″ represents the set transmission rate, γ″ represents the set response time, Δφ represents the set transmission rate allowable difference, Δγ represents the set response time allowable difference, φ represents the transmission rate corresponding to the target cloud edge, and γ represents the response time corresponding to the target cloud edge.

[0013] Preferably, the analysis yields the performance evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows:

[0014] Through calculation formula The analysis yields the performance evaluation coefficient λ corresponding to the target cloud edge, where v1, v2, and ν3 represent the weighting factors for the set bandwidth consumption rate, network speed, and network latency, respectively, and μ″ represents the set bandwidth consumption rate. θ″ represents the set network speed, θ″ represents the set network latency, and Δμ represents the set bandwidth consumption rate allowable difference. Δθ represents the set network rate allowance difference, Δθ represents the set network latency allowance difference, and μ represents the bandwidth consumption rate corresponding to the target cloud-edge endpoint. θ represents the network rate corresponding to the target cloud edge, and θ represents the network latency corresponding to the target cloud edge.

[0015] Preferably, the analysis yields the efficiency evaluation coefficient corresponding to the target cloud edge, and the specific analysis process is as follows:

[0016] Through calculation formula The analysis yielded the efficiency evaluation coefficient corresponding to the target cloud edge. υ1 and υ2 represent the weighting factors of the real-time evaluation coefficient and performance evaluation coefficient corresponding to the target cloud edge, respectively; Τ represents the real-time evaluation coefficient corresponding to the target cloud edge; and λ represents the performance evaluation coefficient corresponding to the target cloud edge.

[0017] Preferably, the identity of the user in the target cloud-edge endpoint matches the evaluation coefficient, and the specific analysis process is as follows:

[0018] Extract the target login account information from the login account corresponding to the target cloud-edge user login address, and compare it with the login account information range corresponding to the target cloud-edge user login address. If the target login account information corresponding to the user login address is within the login account information range corresponding to the user login address, then the login compliance evaluation coefficient corresponding to the target cloud-edge user is recorded as ε1, otherwise it is recorded as ε2. In this way, the identity compliance evaluation coefficient ψ corresponding to the target cloud-edge user is obtained, where ψ takes the value of ε1 or ε2, and ε1>ε2.

[0019] Preferably, the analysis yields the security evaluation coefficient corresponding to the target cloud edge, and the specific analysis process is as follows:

[0020] Through calculation formula The analysis yields the security evaluation coefficient β corresponding to the target cloud edge device, τ1 represents the weight factor of the login compliance evaluation coefficient corresponding to the target cloud edge device user, τ2 represents the weight factor of the set login duration, λ″ represents the set login duration, λ represents the login duration corresponding to the target cloud edge device, and ψ represents the identity compliance evaluation coefficient corresponding to the target cloud edge device user.

[0021] Preferably, the analysis yields the extended evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows:

[0022] Through calculation formula The analysis yields the scalability evaluation coefficient A corresponding to the target cloud-edge endpoint. ρ1, ρ2, ρ3, and ρ4 represent the weighting factors for the set number of devices, number of interfaces, data requirements, and space capacity, respectively, and η″ represents the set number of devices. η represents the set number of interfaces, M″ represents the set data requirement, ω″ represents the set space capacity, and η represents the number of devices corresponding to the target cloud-edge endpoint. M represents the number of interfaces corresponding to the target cloud edge, M represents the data requirement corresponding to the target cloud edge, and ω represents the space capacity corresponding to the target cloud edge.

[0023] Preferably, the analysis yields the intelligence evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows:

[0024] Through calculation formula The analysis yields the intelligence evaluation coefficient ξ corresponding to the target cloud-edge endpoint. σ1, σ2, and σ3 represent the weighting factors for the efficiency evaluation coefficient, security evaluation coefficient, and scalability evaluation coefficient corresponding to the target cloud-edge endpoint, respectively. β represents the efficiency evaluation coefficient corresponding to the target cloud-edge terminal, A represents the security evaluation coefficient corresponding to the target cloud-edge terminal, and A represents the scalability evaluation coefficient corresponding to the target cloud-edge terminal.

[0025] Preferably, the process for determining the intelligence of the target cloud edge is as follows:

[0026] The intelligence evaluation coefficient threshold corresponding to the target cloud edge is compared with the intelligence evaluation coefficient threshold stored in the database. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is less than the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed unqualified. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is equal to the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed qualified. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is greater than the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed excellent.

[0027] Preferably, the system further includes a database for storing transmission information, performance information, login account information, capacity information, and intelligence evaluation coefficient thresholds.

[0028] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a cloud-edge-device monitoring system based on IoT data. By acquiring the data transmission information corresponding to the target cloud-edge device, a better analysis is obtained to obtain the real-time evaluation coefficient of the target cloud-edge device. By acquiring the performance information corresponding to the target cloud-edge device, a better analysis is obtained to obtain the performance evaluation coefficient of the target cloud-edge device. By adding the real-time evaluation coefficient and the performance evaluation coefficient, a better efficiency evaluation coefficient of the target cloud-edge device is obtained. By acquiring the security information corresponding to the target cloud-edge device, a better analysis is obtained to obtain the security evaluation coefficient of the target cloud-edge device. By acquiring the capacity information corresponding to the target cloud-edge device, a better analysis is obtained to obtain the security evaluation coefficient of the target cloud-edge device. By acquiring and analyzing the scalability evaluation coefficients corresponding to the target cloud-edge-device, and then adding these coefficients together, the intelligence evaluation coefficients corresponding to the target cloud-edge-device are obtained. This process determines the intelligence of the target cloud-edge-device, thereby providing more powerful and efficient cloud-edge-device service capabilities. It addresses the shortcomings of current technologies, better ensures the security and privacy of data transmission, rapidly improves the completion rate and efficiency of task data, reduces the burden of tasks, and to some extent facilitates people's lives, better meeting any task needs of users. It comprehensively protects users' experience with cloud-edge-device data services, enriches data life, and better demonstrates the intelligence and efficiency of cloud-edge-device collaboration. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0031] The technical solutions of 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 within the scope of protection of the present invention.

[0032] Please see Figure 1As shown, a cloud storage server system for a smart lock includes a data transmission acquisition module, a performance information analysis module, a data security monitoring module, an extended information analysis module, a platform operation monitoring module, status prompts, and a database.

[0033] The data transmission acquisition module is connected to the performance information analysis module and the platform operation monitoring module, respectively. The data security monitoring module is connected to the extended information analysis module and the platform operation monitoring module, respectively. The platform operation monitoring module is connected to the status prompt and the database, respectively.

[0034] The data transmission acquisition module is used to acquire the data transmission information corresponding to the target cloud edge, including the transmission rate and response time, and then analyzes and obtains the real-time evaluation coefficient corresponding to the target cloud edge.

[0035] It should be noted that the transmission data volume and transmission time corresponding to the target cloud edge are obtained from the database. Then, the transmission rate is obtained by dividing the transmission data volume by the transmission time, and this is used as the transmission rate corresponding to the target cloud edge. The response time corresponding to the target cloud edge is obtained from the database.

[0036] As an optional implementation, the analysis yields the real-time performance evaluation coefficients corresponding to the target cloud edge. The specific analysis process is as follows:

[0037] Through calculation formula The analysis yields the real-time evaluation coefficient Τ corresponding to the target cloud edge, κ1 and κ2 represent the weighting factors of the set transmission rate and response time, respectively, φ″ represents the set transmission rate, γ″ represents the set response time, Δφ represents the set transmission rate allowable difference, Δγ represents the set response time allowable difference, φ represents the transmission rate corresponding to the target cloud edge, and γ represents the response time corresponding to the target cloud edge.

[0038] The performance information analysis module is used to obtain the performance information corresponding to the target cloud edge, including bandwidth consumption rate, network speed, and network latency. Then, it analyzes and obtains the performance evaluation coefficient corresponding to the target cloud edge, and based on the real-time evaluation coefficient corresponding to the target cloud edge, it analyzes and obtains the efficiency evaluation coefficient corresponding to the target cloud edge.

[0039] It should be noted that the total bandwidth is obtained from the database, and the actual bandwidth used is obtained from the cloud-edge operation center. Then, the bandwidth consumption rate is obtained by dividing the actual bandwidth used by the total bandwidth. This is used as the bandwidth consumption rate of the target cloud-edge. The network speed and network latency of the target cloud-edge are obtained through network testing tools.

[0040] As an optional implementation, the analysis yields the performance evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows:

[0041] Through calculation formula The analysis yields the performance evaluation coefficient λ corresponding to the target cloud edge, where v1, v2, and ν3 represent the weighting factors for the set bandwidth consumption rate, network speed, and network latency, respectively, and μ″ represents the set bandwidth consumption rate. θ″ represents the set network speed, θ″ represents the set network latency, and Δμ represents the set bandwidth consumption rate allowable difference. Δθ represents the set network rate allowance difference, Δθ represents the set network latency allowance difference, and μ represents the bandwidth consumption rate corresponding to the target cloud-edge endpoint. θ represents the network rate corresponding to the target cloud edge, and θ represents the network latency corresponding to the target cloud edge.

[0042] As an optional implementation, the analysis yields the efficiency evaluation coefficient corresponding to the target cloud-edge endpoint. The specific analysis process is as follows:

[0043] Through calculation formula The analysis yielded the efficiency evaluation coefficient corresponding to the target cloud edge. υ1 and υ2 represent the weighting factors of the real-time evaluation coefficient and performance evaluation coefficient corresponding to the target cloud edge, respectively; Τ represents the real-time evaluation coefficient corresponding to the target cloud edge; and λ represents the performance evaluation coefficient corresponding to the target cloud edge.

[0044] The data security monitoring module is used to analyze the identity compliance evaluation coefficient of the user in the target cloud edge based on the login account information corresponding to the login address of the target cloud edge user, and obtain the login duration of the target cloud edge, and then analyze and obtain the security evaluation coefficient of the target cloud edge.

[0045] It should be noted that the corresponding login duration is obtained from the background operation center of the cloud edge.

[0046] As an optional implementation, the identity of the user in the target cloud-edge terminal meets the evaluation coefficient, and the specific analysis process is as follows:

[0047] Extract the target login account information from the login account corresponding to the target cloud-edge user login address, and compare it with the login account information range corresponding to the target cloud-edge user login address. If the target login account information corresponding to the user login address is within the login account information range corresponding to the user login address, then the login compliance evaluation coefficient corresponding to the target cloud-edge user is recorded as ε1, otherwise it is recorded as ε2. In this way, the identity compliance evaluation coefficient ψ corresponding to the target cloud-edge user is obtained, where ψ takes the value of ε1 or ε2, and ε1>ε2.

[0048] As an optional implementation, the analysis yields the security evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows:

[0049] Through calculation formula The analysis yields the security evaluation coefficient β corresponding to the target cloud edge device, τ1 represents the weight factor of the login compliance evaluation coefficient corresponding to the target cloud edge device user, τ2 represents the weight factor of the set login duration, λ″ represents the set login duration, λ represents the login duration corresponding to the target cloud edge device, and ψ represents the identity compliance evaluation coefficient corresponding to the target cloud edge device user.

[0050] The extended information analysis module is used to obtain the capacity information corresponding to the target cloud edge device. The capacity information includes the number of devices, the number of interfaces, the data demand, and the space capacity, and then analyzes and obtains the scalability evaluation coefficient corresponding to the target cloud edge device.

[0051] As an optional implementation, the analysis yields the extended evaluation coefficients corresponding to the target cloud edge. The specific analysis process is as follows:

[0052] Through calculation formula The analysis yields the scalability evaluation coefficient A corresponding to the target cloud-edge endpoint. ρ1, ρ2, ρ3, and ρ4 represent the weighting factors for the set number of devices, number of interfaces, data requirements, and space capacity, respectively, and η″ represents the set number of devices. η represents the set number of interfaces, M″ represents the set data requirement, ω″ represents the set space capacity, and η represents the number of devices corresponding to the target cloud-edge endpoint. M represents the number of interfaces corresponding to the target cloud edge, M represents the data requirement corresponding to the target cloud edge, and ω represents the space capacity corresponding to the target cloud edge.

[0053] It should be noted that the corresponding capacity information is obtained from the database.

[0054] The platform operation monitoring module is used to analyze the efficiency evaluation coefficient, security evaluation coefficient, and scalability evaluation coefficient of the target cloud edge device, and then obtain the intelligence evaluation coefficient of the target cloud edge device. Based on the intelligence evaluation coefficient of the target cloud edge device, the intelligence of the target cloud edge device is judged.

[0055] As an optional implementation, the analysis yields the intelligence evaluation coefficients corresponding to the target cloud-edge endpoint. The specific analysis process is as follows:

[0056] Through calculation formula The analysis yields the intelligence evaluation coefficient ξ corresponding to the target cloud-edge endpoint. σ1, σ2, and σ3 represent the weighting factors for the efficiency evaluation coefficient, security evaluation coefficient, and scalability evaluation coefficient corresponding to the target cloud-edge endpoint, respectively. β represents the efficiency evaluation coefficient corresponding to the target cloud-edge terminal, A represents the security evaluation coefficient corresponding to the target cloud-edge terminal, and A represents the scalability evaluation coefficient corresponding to the target cloud-edge terminal.

[0057] As an optional implementation, the specific process for determining the intelligence of the target cloud-edge device is as follows:

[0058] The intelligence evaluation coefficient threshold corresponding to the target cloud edge is compared with the intelligence evaluation coefficient threshold stored in the database. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is less than the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed unqualified. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is equal to the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed qualified. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is greater than the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed excellent.

[0059] As an optional implementation, the system also includes a database for storing transmission information, performance information, login account information, capacity information, and intelligence evaluation coefficient thresholds.

[0060] Status prompts are used to issue early warnings when the intelligence of the target cloud edge fails to meet the requirements.

[0061] This invention provides a cloud-edge-device monitoring system based on IoT data. By acquiring data transmission information of the target cloud-edge device, a real-time performance evaluation coefficient is obtained. Similarly, by acquiring performance information of the target cloud-edge device, a performance evaluation coefficient is obtained. Adding the real-time evaluation coefficient and the performance evaluation coefficient yields a higher efficiency evaluation coefficient. Furthermore, by acquiring security information of the target cloud-edge device, a security evaluation coefficient is obtained. Finally, by acquiring capacity information of the target cloud-edge device, a better analysis is conducted. The scalability evaluation coefficients corresponding to the target cloud-edge device are obtained by analysis, and the coefficients corresponding to the target cloud-edge device are added together to obtain the intelligence evaluation coefficients corresponding to the target cloud-edge device. The intelligence of the target cloud-edge device is then judged, thereby providing more powerful and efficient cloud-edge device service capabilities. This solves the shortcomings of current technologies, better protects the security and privacy of data transmission, rapidly improves the completion rate and efficiency of task data, reduces the burden of tasks, and at the same time, facilitates people's lives to a certain extent, better meets any task needs of users, and comprehensively protects users' experience of cloud-edge device data services, enriches data life, and better demonstrates the intelligence and efficiency of cloud-edge device collaboration.

[0062] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cloud-edge-device monitoring system based on Internet of Things (IoT) data, characterized in that, include: The data transmission acquisition module is used to acquire the data transmission information corresponding to the target cloud edge, including the transmission rate and response time, and then analyzes and obtains the real-time evaluation coefficient corresponding to the target cloud edge. The performance information analysis module is used to obtain the performance information corresponding to the target cloud edge, including bandwidth consumption rate, network speed, and network latency. Then, it analyzes and obtains the performance evaluation coefficient corresponding to the target cloud edge, and based on the real-time evaluation coefficient corresponding to the target cloud edge, it analyzes and obtains the efficiency evaluation coefficient corresponding to the target cloud edge. The data security monitoring module is used to analyze the identity compliance evaluation coefficient of the user in the target cloud edge based on the login account information corresponding to the login address of the target cloud edge user, and obtain the login duration of the target cloud edge, and then analyze and obtain the security evaluation coefficient of the target cloud edge. The extended information analysis module is used to obtain the capacity information corresponding to the target cloud edge device. The capacity information includes the number of devices, the number of interfaces, the data demand, and the space capacity, and then analyzes and obtains the scalability evaluation coefficient corresponding to the target cloud edge device. The platform operation monitoring module is used to analyze the efficiency evaluation coefficient, security evaluation coefficient, and scalability evaluation coefficient of the target cloud edge device, and then obtain the intelligence evaluation coefficient of the target cloud edge device. Based on the intelligence evaluation coefficient of the target cloud edge device, the intelligence of the target cloud edge device is judged. Status prompts are used to issue early warnings when the intelligence of the target cloud edge fails to meet the requirements.

2. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The analysis yields the real-time performance evaluation coefficients for the target cloud edge, and the specific analysis process is as follows: Through calculation formula The analysis yields the real-time evaluation coefficient Τ corresponding to the target cloud edge, κ1 and κ2 represent the weighting factors of the set transmission rate and response time, respectively, φ″ represents the set transmission rate, γ″ represents the set response time, Δφ represents the set transmission rate allowable difference, Δγ represents the set response time allowable difference, φ represents the transmission rate corresponding to the target cloud edge, and γ represents the response time corresponding to the target cloud edge.

3. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The analysis yields the performance evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows: Through calculation formula The analysis yields the performance evaluation coefficient λ corresponding to the target cloud edge, where v1, v2, and ν3 represent the weighting factors for the set bandwidth consumption rate, network speed, and network latency, respectively; μ″ represents the set bandwidth consumption rate; θ″ represents the set network speed; θ″ represents the set network latency; Δμ represents the set permissible difference in bandwidth consumption rate; Δθ represents the set permissible difference in network speed; Δθ represents the set permissible difference in network latency; μ represents the bandwidth consumption rate corresponding to the target cloud edge; θ represents the network speed corresponding to the target cloud edge; and θ represents the network latency corresponding to the target cloud edge.

4. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The analysis yields the efficiency evaluation coefficient for the target cloud-edge endpoint. The specific analysis process is as follows: Through calculation formula The analysis yielded the efficiency evaluation coefficient corresponding to the target cloud edge. Let T and λ represent the weighting factors of the real-time evaluation coefficient and performance evaluation coefficient corresponding to the target cloud edge, respectively. Let T represent the real-time evaluation coefficient corresponding to the target cloud edge, and λ represent the performance evaluation coefficient corresponding to the target cloud edge.

5. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The user's identity in the target cloud-edge-device region matches the evaluation coefficient. The specific analysis process is as follows: Extract the target login account information from the login account corresponding to the target cloud-edge user login address, and compare it with the login account information range corresponding to the target cloud-edge user login address. If the target login account information corresponding to the user login address is within the login account information range corresponding to the user login address, then the login compliance evaluation coefficient corresponding to the target cloud-edge user is recorded as ε1, otherwise it is recorded as ε2. In this way, the identity compliance evaluation coefficient ψ corresponding to the target cloud-edge user is obtained, where ψ takes the value of ε1 or ε2, and ε1>ε2.

6. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The analysis yields the security evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows: Through calculation formula The analysis yields the security evaluation coefficient β corresponding to the target cloud edge device, τ1 represents the weight factor of the login compliance evaluation coefficient corresponding to the target cloud edge device user, τ2 represents the weight factor of the set login duration, λ″ represents the set login duration, λ represents the login duration corresponding to the target cloud edge device, and ψ represents the identity compliance evaluation coefficient corresponding to the target cloud edge device user.

7. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The analysis yields the extended evaluation coefficients corresponding to the target cloud edge, and the specific analysis process is as follows: Through calculation formula The analysis yields the scalability evaluation coefficient A corresponding to the target cloud-edge endpoint. ρ1, ρ2, ρ3, and ρ4 represent the weighting factors for the set number of devices, number of interfaces, data requirements, and space capacity, respectively, and η″ represents the set number of devices. η represents the set number of interfaces, M″ represents the set data requirement, ω″ represents the set space capacity, η represents the number of devices corresponding to the target cloud edge, ζ represents the number of interfaces corresponding to the target cloud edge, M represents the data requirement corresponding to the target cloud edge, and ω represents the space capacity corresponding to the target cloud edge.

8. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The analysis yields the intelligence evaluation coefficients corresponding to the target cloud-edge endpoints. The specific analysis process is as follows: Through calculation formula The analysis yields the intelligence evaluation coefficient ξ corresponding to the target cloud-edge endpoint. σ1, σ2, and σ3 represent the weighting factors for the efficiency evaluation coefficient, security evaluation coefficient, and scalability evaluation coefficient corresponding to the target cloud-edge endpoint, respectively. β represents the efficiency evaluation coefficient corresponding to the target cloud-edge terminal, A represents the security evaluation coefficient corresponding to the target cloud-edge terminal, and A represents the scalability evaluation coefficient corresponding to the target cloud-edge terminal.

9. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The specific process for determining the intelligence of the target cloud edge device is as follows: The intelligence evaluation coefficient threshold corresponding to the target cloud edge is compared with the intelligence evaluation coefficient threshold stored in the database. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is less than the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed unqualified. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is equal to the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed qualified. If the intelligence evaluation coefficient threshold corresponding to the target cloud edge is greater than the intelligence evaluation coefficient threshold stored in the database, the intelligence of the target cloud edge is deemed excellent.

10. The cloud-edge-device monitoring system based on IoT data as described in claim 1, characterized in that, The system also includes a database for storing transmission information, performance information, login account information, capacity information, and intelligence evaluation coefficient thresholds.

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