Intelligent remote industrial personal computer debugging method and system

Abstract

The invention discloses an intelligent remote industrial personal computer debugging method and system. The method comprises the following steps: connecting a to-be-debugged target industrial personal computer with a remote desktop client and debugging; a prediction algorithm is introduced to predict future network delay, and a client instruction is sent in advance according to the future network delay; the network quality is evaluated, and when the score of the network quality is smaller than a first threshold value, reminding is carried out on the remote desktop client; and evaluating the health state of the equipment, and when the health state of the equipment is smaller than a second threshold value, reminding on the remote desktop client.

Description

Technical Field

[0001] The present invention relates to the field of industrial computer debugging, and in particular to an intelligent remote industrial computer debugging method and system. Background Art

[0002] With the continuous improvement of industrial automation, the demand for remote debugging and maintenance of industrial control equipment (i.e. industrial computers) is also growing. Traditional remote debugging methods often have problems such as high network security risks, complex operations, and low efficiency. In order to solve these problems, virtualization technology, VPN technology, etc. have been widely used in the field of remote access and management in recent years.

[0003] In the prior art, common remote debugging methods include: Remote access based on the Remote Desktop Protocol (RDP): Remote control of a remote computer can be achieved through the RDP protocol, but its security is low and it is vulnerable to network attacks. Remote login based on the SSH protocol: The SSH protocol provides a more secure remote login method, but its operation interface is relatively simple and may not be intuitive enough for complex debugging tasks. Remote access based on VPN technology: VPN technology can establish a secure private network, but its configuration is complex and usually requires port mapping on the firewall.

[0004] Although the above-mentioned prior art solves the problem of remote debugging to a certain extent, it still has the following shortcomings: Low efficiency: Traditional remote debugging methods often reduce debugging efficiency due to network delays. Summary of the invention

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention discloses an intelligent remote industrial computer debugging method, comprising: Connect the target industrial computer to be debugged to the remote desktop client and perform debugging; Introduce a prediction algorithm to predict future network delays and send client instructions in advance based on future network delays; Evaluate the network quality, and when the network quality score is less than a first threshold, issue a reminder on the remote desktop client; The health status of the device is evaluated, and when the health status of the device is less than a second threshold, a reminder is given on the remote desktop client.

[0006] Furthermore, connecting the target industrial computer to be debugged with the remote desktop includes: Virtualization environment construction; Virtual machine deployment; Port mapping configuration, including configuring network forwarding rules on the virtualized operating system to forward traffic from external access port 3389 to port 3389 of Windows Server to implement access to Windows Server remote desktop; Configure the network and enable remote connection.

[0007] Furthermore, configuring the network and realizing the remote connection includes: Connect the intranet port of the industrial computer to the remote environment so that the industrial computer can access the external network; The industrial computer is connected to the Internet via a 4G network module to provide a backup network connection; The industrial computer connects to the central VPN server and obtains the assigned fixed IP address (IP1); The client connects to the central VPN server and obtains the assigned IP address (IP2); The client remotely connects to the VPN IP address (IP1) of the industrial computer through the 3389 protocol; The client connects to the Windows Server's Remote Desktop.

[0008] Furthermore, the network quality evaluation includes: NEDS=(Rt×Et×Mt)^(1 / 3)×(1+α×Δm); Among them, Rt represents the real-time performance index, Et represents the experience quality index, Mt represents the network resilience index, α represents the timing adjustment coefficient, and Δm represents the performance improvement.

[0009] Furthermore, the method for obtaining Rt includes: Rt=(response speed×transmission efficiency)^(1 / 2), where response speed=log(1000 / average delay) and transmission efficiency=(actual bandwidth / maximum bandwidth)^2.

[0010] Furthermore, the method for obtaining Et is as follows: Et=(1-stuttering rate)^3×(1-error rate)^2×(continuity index), where continuity index=min(1, continuous stable working time / expected stable time).

[0011] Furthermore, the method for obtaining Mt is as follows: Mt=(1+backup link coefficient)×(1-peak volatility)×restoration capability index; The recovery capability index = min(1, benchmark recovery time / actual recovery time).

[0012] Furthermore, the evaluation of the health status of the device includes: Health = Σ(wi*xi)+AnomalyScore; Among them, AnomalyScore: anomaly score, calculated by anomaly detection algorithm.

[0013] wi: weight coefficient, which is assigned according to the degree of influence of different indicators on the health status of the device.

[0014] xi: the i-th index of the device.

[0015] Furthermore, predicting future network delays includes: D(t+1)=βD(t)+(1-β)Δt, where D(t) is the delay at time t, Δt is the current delay, and β is the smoothing factor.

[0016] Instruction adjustment: T'=T±ΔD, where T' is the adjusted instruction sending time, T is the original sending time, and ΔD is the predicted delay.

[0017] On the other hand, the present invention provides an intelligent remote industrial computer debugging system, comprising: A connection module is used to connect the target industrial computer to be debugged with the remote desktop client and perform debugging; The delay prediction module is used to introduce a prediction algorithm to predict future network delays and send client instructions in advance based on the future network delays; A network quality evaluation module, used to evaluate the network quality, and when the network quality score is less than a first threshold, a reminder is given on the remote desktop client; The device health status evaluation module is used to evaluate the device health status and to issue a reminder on the remote desktop client when the device health status is less than a second threshold.

[0018] By adopting the above technical solution and introducing a prediction algorithm, the future network delay can be accurately predicted, and the client command sending time can be dynamically adjusted, which effectively reduces the impact of network delay on remote operation and significantly improves the operation response speed. The network quality is monitored in real time, and reasonable thresholds are set. Once the network quality deteriorates, the system can issue an alarm in time to remind users to take corresponding measures to avoid debugging interruption or data loss due to network problems. The health status of industrial computers and other equipment is monitored in real time. Once an abnormality is found, the system can issue an alarm in time to facilitate users to take maintenance measures in advance and avoid production interruption caused by equipment failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0020] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 The embodiment of the present invention provides an intelligent remote industrial computer debugging method, comprising the following steps: S1: Connect the target industrial computer to be debugged to the remote desktop client and perform debugging; S2: Introduce a prediction algorithm to predict future network delays and send client instructions in advance based on the future network delays; S3: Evaluate the network quality, and when the network quality score is less than a first threshold, issue a reminder on the remote desktop client; S4: Evaluate the health status of the device, and when the health status of the device is less than a second threshold, issue a reminder on the remote desktop client.

[0023] The step of connecting the target industrial computer to be debugged to the remote desktop includes: Virtualization environment construction; Virtual machine deployment; Port mapping configuration, including configuring network forwarding rules on the virtualized operating system to forward traffic from external access port 3389 to port 3389 of Windows Server to implement access to Windows Server remote desktop; Configure the network and enable remote connection.

[0024] Wherein, configuring the network and realizing remote connection includes: Connect the intranet port of the industrial computer to the remote environment so that the industrial computer can access the external network; The industrial computer is connected to the Internet via a 4G network module to provide a backup network connection; The industrial computer connects to the central VPN server and obtains the assigned fixed IP address (IP1); The client connects to the central VPN server and obtains the assigned IP address (IP2); The client remotely connects to the VPN IP address (IP1) of the industrial computer through the 3389 protocol; The client connects to the Windows Server's Remote Desktop.

[0025] The predicted future network delay includes: D(t+1)=βD(t)+(1-β)Δt, where D(t) is the delay at time t, Δt is the current delay, and β is the smoothing factor.

[0026] Instruction adjustment: T'=T±ΔD, where T' is the adjusted instruction sending time, T is the original sending time, and ΔD is the predicted delay.

[0027] As mentioned above, future network delays are predicted based on historical data, and instructions are sent based on future network delays, which improves debugging efficiency. By introducing a prediction algorithm, future network delays are accurately predicted, and the client instruction sending time is dynamically adjusted, effectively reducing the impact of network delays on remote operations and significantly improving the operation response speed.

[0028] The network quality assessment includes: NEDS=(Rt×Et×Mt)^(1 / 3)×(1+α×Δm); Among them, Rt represents the real-time performance index, Et represents the experience quality index, Mt represents the network resilience index, α represents the timing adjustment coefficient, and Δm represents the performance improvement.

[0029] Rt=(response speed×transmission efficiency)^(1 / 2), where response speed=log(1000 / average delay) and transmission efficiency=(actual bandwidth / maximum bandwidth)^2.

[0030] Et=(1-stuttering rate)^3×(1-error rate)^2×(continuity index), where continuity index=min(1, continuous stable working time / expected stable time).

[0031] Mt=(1+backup link coefficient)×(1-peak volatility)×restoration capability index; The recovery capability index = min(1, benchmark recovery time / actual recovery time).

[0032] During a certain network transmission, the response speed is calculated to be 0.85 (50ms delay), the transmission efficiency is 0.75 (actual bandwidth / maximum bandwidth=0.866), the jam rate is 0.01, the error rate is 0.001, the continuity index is 0.95, the backup link coefficient is 0.2, the peak fluctuation rate is 0.05, the recovery capability index is 0.9, the performance improvement is 0.1, and α is 0.2 The final score is: Rt=0.933.

[0033] The first threshold is set by those skilled in the art according to actual conditions and will not be described in detail here.

[0034] As mentioned above, by real-time monitoring of network quality and setting reasonable thresholds, once the network quality deteriorates, the system can issue an alarm in time to remind users to take corresponding measures to avoid debugging interruption or data loss due to network problems.

[0035] The evaluation of the equipment health status includes: Health = Σ(wi*xi)+AnomalyScore; Among them, AnomalyScore: anomaly score, calculated by anomaly detection algorithm.

[0036] wi: weight coefficient, which is assigned according to the degree of influence of different indicators on the health status of the device. It is set by technical personnel in this field according to actual needs and will not be described in detail here.

[0037] xi: the i-th index of the device.

[0038] The preferred indicators in this embodiment include temperature, vibration frequency, vibration amplitude, current, voltage, CPU utilization, memory usage, network traffic, and may also include other commonly used equipment health status assessment indicators well known to those skilled in the art. The second threshold is set by those skilled in the art according to actual conditions and will not be repeated here.

[0039] As mentioned above, the health status of industrial computers and other equipment is monitored in real time. Once an abnormality is found, the system can issue an alarm in time, allowing users to take maintenance measures in advance to avoid production interruptions caused by equipment failures.

[0040] The present invention also provides an intelligent remote industrial computer debugging system, comprising: A connection module is used to connect the target industrial computer to be debugged with the remote desktop client and perform debugging; The delay prediction module is used to introduce a prediction algorithm to predict future network delays and send client instructions in advance based on the future network delays; A network quality evaluation module, used to evaluate the network quality, and when the network quality score is less than a first threshold, a reminder is given on the remote desktop client; The device health status evaluation module is used to evaluate the device health status and issue a reminder on the remote desktop client when the device health status is less than the second threshold. Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0041] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0042] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent remote industrial computer debugging method, characterized in that: include: Connect the target industrial computer to be debugged to the remote desktop client and perform debugging; Introduce a prediction algorithm to predict future network delays and send client instructions in advance based on future network delays; Evaluate the network quality, and when the network quality score is less than a first threshold, issue a reminder on the remote desktop client; The health status of the device is evaluated, and when the health status of the device is less than a second threshold, a reminder is given on the remote desktop client.

2. The method according to claim 1, characterized in that The step of connecting the target industrial computer to be debugged to the remote desktop includes: Virtualization environment construction; Virtual machine deployment; Port mapping configuration, including configuring network forwarding rules on the virtualized operating system to forward traffic from external access port 3389 to port 3389 of Windows Server to implement access to Windows Server remote desktop; Configure the network and enable remote connection.

3. The method according to claim 2, characterized in that Configuring the network and achieving remote connection includes: Connect the intranet port of the industrial computer to the remote environment so that the industrial computer can access the external network; The industrial computer is connected to the Internet via a 4G network module to provide a backup network connection; The industrial computer connects to the central VPN server and obtains the assigned fixed IP address (IP1); The client connects to the central VPN server and obtains the assigned IP address (IP2); The client remotely connects to the VPN IP address (IP1) of the industrial computer through the 3389 protocol; The client connects to the Windows Server's Remote Desktop.

4. The method according to claim 1, characterized in that: The network quality assessment includes: NEDS=(Rt×Et×Mt)^(1 / 3)×(1+α×Δm); Among them, Rt represents the real-time performance index, Et represents the experience quality index, Mt represents the network resilience index, α represents the timing adjustment coefficient, and Δm represents the performance improvement.

5. The method according to claim 4, characterized in that The method of obtaining Rt includes: Rt=(response speed×transmission efficiency)^(1 / 2), where response speed=log(1000 / average delay) and transmission efficiency=(actual bandwidth / maximum bandwidth)^2.

6. The method according to claim 4, characterized in that The method for obtaining Et is: Et=(1-stuttering rate)^3×(1-error rate)^2×(continuity index), where continuity index=min(1, continuous stable working time / expected stable time).

7. The method according to claim 4, characterized in that The method for obtaining the Mt is as follows: Mt=(1+backup link coefficient)×(1-peak volatility)×restoration capability index; The recovery capability index = min(1, benchmark recovery time / actual recovery time).

8. The method according to claim 1, characterized in that The evaluation of the equipment health status includes: Health = Σ(wi*xi)+AnomalyScore; Among them, AnomalyScore: anomaly score, calculated by anomaly detection algorithm; wi: weight coefficient, which is assigned according to the degree of influence of different indicators on the health status of the device; xi: the i-th index of the device.

9. The method according to claim 1, characterized in that: The predicted future network delay includes: D(t+1)=βD(t)+(1-β)Δt, where D(t) is the delay at time t, Δt is the current delay, and β is the smoothing factor; Instruction adjustment: T'=T±ΔD, where T' is the adjusted instruction sending time, T is the original sending time, and ΔD is the predicted delay.

10. An intelligent remote industrial computer debugging system, characterized in that: include: A connection module is used to connect the target industrial computer to be debugged with the remote desktop client and perform debugging; The delay prediction module is used to introduce a prediction algorithm to predict future network delays and send client instructions in advance based on the future network delays; A network quality evaluation module, used to evaluate the network quality, and when the network quality score is less than a first threshold, a reminder is given on the remote desktop client; The device health status evaluation module is used to evaluate the device health status and to issue a reminder on the remote desktop client when the device health status is less than a second threshold.

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