Intelligent robot for realizing seamless joint between systems

Abstract

The invention discloses an intelligent robot for realizing seamless connection between systems, and relates to the technical field of computers, the intelligent robot comprises a behavior regulation and control module, an information calling module, a central processing unit, a fault monitoring module and an alarm processing module, the behavior regulation and control module is used for carrying out business connection work, and the information calling module is used for collecting operation information; the central processing unit carries out data processing on the operation information, generates a system fault monitoring report and an alarm signal, carries out visual monitoring presentation through the fault monitoring module, generates an optimization management instruction through the alarm processing module, and feeds back the optimization management instruction to the behavior regulation and control module for timely abnormity processing; therefore, the stability of communication transmission and data docking is continuously improved, the integrity and safety of data storage are ensured, finally, normal operation of an intelligent robot system is ensured through fault monitoring and exception handling of service docking work, and the reliability of seamless docking of service work between systems is achieved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an intelligent robot capable of realizing seamless connection between systems. Background Art

[0002] With the rapid development of artificial intelligence technology, robotics technology has also made significant progress. Robots transform the wisdom of artificial intelligence into practical actions by perceiving the environment, performing tasks and interacting with people. In practical applications, in order to achieve more efficient and intelligent robot operations, seamless connection between systems becomes particularly important.

[0003] For example, on industrial production lines, intelligent robots need to work closely with various production equipment, sensors, and management systems to ensure the smooth progress of the production process. In addition, with the increasing application of intelligent robots in medical, educational, service and other fields, the demand for seamless connection between systems is becoming more urgent.

[0004] However, when a fault occurs in an existing intelligent robot that seamlessly connects between systems, the complexity of multi-system connection means that fault detection and recovery may be difficult, affecting the reliability of the system. In addition, the types of faults in intelligent robots are diverse and it is difficult to conduct risk quantitative assessment, resulting in problems such as untimely abnormal monitoring and incomplete data processing during the business connection between robots and users. As a result, the robot system is difficult to operate stably and cannot achieve continuous seamless connection of business work between multiple systems. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art that the failure types of intelligent robots are diverse and it is difficult to conduct quantitative risk assessment, resulting in untimely abnormal monitoring and incomplete data processing during the business docking process between robots and users, and the resulting robot system is difficult to operate stably and cannot achieve continuous and seamless docking of business work between multiple systems.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: An intelligent robot for realizing seamless connection between systems, comprising a behavior control module, an information retrieval module, a central processing unit, a fault monitoring module and an alarm processing module, wherein the behavior control module, the information retrieval module, the central processing unit, the fault monitoring module and the alarm processing module are electrically connected; The behavior control module is used to accept user requests and perform corresponding business docking work: the user inputs the business request to the intelligent robot, and the behavior control module accepts the user request and generates corresponding keyboard and mouse operation instructions to perform the business docking work requested by the user; The information retrieval module is used to collect the operation information of the business docking work: set the information collection cycle Tc to collect the operation information regularly, and the operation information includes communication abnormality parameters, data conflict parameters and structural fault parameters; The central processing unit is used to process the operation information: by constructing a preliminary analysis model to process the communication anomaly parameters, data conflict parameters and structural fault parameters, the communication anomaly degree, data conflict degree and structural fault degree of the intelligent robot are monitored in turn, and then the comprehensive fault degree of the intelligent robot in business docking is comprehensively analyzed and evaluated, and a system fault monitoring report and corresponding alarm signals are generated; The fault monitoring module is used to receive system fault monitoring reports and perform corresponding visual presentation; The alarm processing module is used to receive alarm signals and perform corresponding exception processing: by receiving alarm signals and performing refined analysis, it generates optimized management instructions for exception processing, and feeds back to the behavior control module to re-perform the business docking work requested by the user.

[0007] Furthermore, the specific process of the preliminary analysis model is as follows: Input a number set Ψe of parameter E into the preliminary analysis model, wherein the number set Ψe includes data values ​​of parameter E corresponding to N0 information collection cycles, and any data value of the number set Ψe is marked as φe; Set the standard interval of parameter E[ , ], when the data value φe of parameter E is in the standard interval [ , ], the data of parameter E is normal; otherwise, when the data value φe of parameter E is not within the standard interval [ , ], the data of parameter E is judged to be abnormal; By the set Ψe of parameter E and the standard interval [ , ] to obtain the evaluation coefficient EVAe of parameter E: , where ω0 is the correction coefficient and ω0 is greater than 0, is the coefficient of deviation of parameter E and ; Preliminary analysis of the evaluation coefficient EVAe of the model output parameter E.

[0008] Furthermore, the specific process of monitoring the degree of communication anomaly of the intelligent robot is as follows: Communication anomaly parameters include page response time a1, data loss rate a2, data request success rate a3, and concurrent access request number a4; Through the communication anomaly parameters corresponding to N0 information collection cycles Tc, the sets of page response time a1, data loss rate a2, data request success rate a3 and concurrent access request number a4 are constructed respectively, and marked as Ψa1, Ψa2, Ψa3, Ψa4 in turn; Substitute the number set Ψa1 of page response time a1, the number set Ψa2 of data loss rate a2, the number set Ψa3 of data request success rate a3 and the number set Ψa4 of concurrent access request number a4 into the preliminary analysis model respectively, so as to output the evaluation coefficient EVAa1 of page response time a1, the evaluation coefficient EVAa2 of data loss rate a2, the evaluation coefficient EVAa3 of data request success rate a3 and the evaluation coefficient EVAa4 of concurrent access request number a4 in turn, and then comprehensively obtain the communication anomaly evaluation index Zcom; Set the evaluation interval of the communication anomaly evaluation index Zcom, and evaluate the degree of communication anomaly of the intelligent robot through interval comparison.

[0009] Furthermore, the specific process of monitoring the data conflict degree of the intelligent robot is as follows: Data conflict parameters include transaction execution consistency index b1, data format consistency index b2, and data security risk index b3; Through the data conflict parameters corresponding to N0 information collection cycles Tc, the number sets of execution consistency index b1, data format consistency index b2 and data security risk index b3 are constructed respectively, and marked as Ψb1, Ψb2, Ψb3 respectively; Substituting the number set Ψb1 of the execution consistency index b1, the number set Ψb2 of the data format consistency index b2 and the number set Ψb3 of the data security risk index b3 into the preliminary analysis model in turn, the evaluation coefficient EVAb1 of the execution consistency index b1, the evaluation coefficient EVAb2 of the data format consistency index b2 and the evaluation coefficient EVAb3 of the data security risk index b3 are output in turn, and then the data conflict evaluation index Zdac is comprehensively obtained; Set the evaluation interval of the data conflict assessment index Zdac, and evaluate the level of data conflict of the intelligent robot through interval comparison.

[0010] Furthermore, the specific process of monitoring the structural failure degree of the intelligent robot is as follows: The structural fault parameters include data interface failure rate c1, database storage failure rate c2, data leakage quantity c3 and abnormal access quantity c4; Through the structural fault parameters corresponding to N0 information collection cycles Tc, the data interface failure rate c1, database storage failure rate c2, data leakage number c3 and abnormal access number c4 are constructed respectively, and marked as Ψc1, Ψc2, Ψc3, Ψc4 respectively; Substitute the data interface failure rate c1 number set Ψc1, the database storage failure rate c2 number set Ψc2, the data leakage number c3 number set Ψc3 and the abnormal access number c4 number set Ψc4 into the preliminary analysis model respectively, so as to output the evaluation coefficient EVAc1 of the data interface failure rate c1, the evaluation coefficient EVAc2 of the database storage failure rate c2, the evaluation coefficient EVAc3 of the data leakage number c3 and the evaluation coefficient EVAc4 of the abnormal access number c4 in turn, and then comprehensively obtain the structural fault evaluation index Zstr; The evaluation interval of the structural fault evaluation index Zstr is set, and the level of the structural fault degree of the intelligent robot is evaluated by interval comparison.

[0011] Furthermore, the specific process of evaluating the comprehensive failure degree of the intelligent robot is as follows: Set the data calculation period Ts to perform regular calculations on the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr to obtain the historical data of the corresponding assessment indexes; then construct the dynamic curves of the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr, and mark them as Sa, Sb, and Sc respectively; Construct a curve analysis model to analyze the dynamic curve. The specific process is as follows: Input the curve Sf to the curve analysis model, extract n0 discrete points of the curve Sf, mark any point of the curve Sf as p (Xp, Yp), and mark the adjacent points of point p as q (Xq, Yq), where the horizontal coordinate is the period of the curve Sf and the vertical coordinate is the data value of the curve Sf; obtain the overall coefficient η1 of the curve Sf through the average value of the vertical coordinates of the n0 points of the curve Sf; obtain the slope value Kp of the point p through the coordinates of point p and its adjacent point q; obtain the average increase rate Kf of the curve Sf through the average value of the slope values ​​of the n0 points of the curve Sf; then calculate the standard deviation of the slope values ​​of the n0 points of the curve Sf to obtain the increase rate variation coefficient σf of the curve Sf; obtain the stability coefficient η2 of the curve Sf by combining the average increase rate Kf of the curve Sf and the increase rate variation coefficient σf; obtain the risk coefficient Rf of the curve Sf by combining the overall coefficient η1 and the stability coefficient η2 of the curve Sf; the curve analysis model outputs the risk coefficient Rf of the curve Sf; The dynamic curves Sa, Sb and Sc are respectively substituted into the curve analysis model to output the risk coefficients Ra, Rb and Rc in turn; the comprehensive failure risk index Risk of the intelligent robot is obtained by combining the risk coefficient Ra of the dynamic curve Sa, the risk coefficient Rb of the dynamic curve Sb and the risk coefficient Rc of the dynamic curve Sc.

[0012] Furthermore, an evaluation interval of the comprehensive fault risk index Risk is set, and the comprehensive fault level of the intelligent robot in business docking work is evaluated through interval comparison, and a system fault monitoring report and corresponding alarm signals are generated; The number of evaluation intervals marked with the comprehensive fault risk index Risk is N1, and any evaluation interval is marked as Gi, where i is the serial number of the evaluation interval and 0<i≤N1. When the comprehensive fault risk index Risk is in the evaluation interval Gi, the comprehensive fault degree of the intelligent robot is determined to be level i, and a level i alarm signal and a system fault monitoring report are generated.

[0013] Furthermore, the specific process of fine-tuning the alarm signal is as follows: Set the comparison interval of the alarm signal level to [M0, M1]; When the alarm signal level i<M0, no processing is performed; When the level i of the alarm signal is greater than M1, a No. 4 optimization management instruction is generated; When the level i of the alarm signal is in the comparison interval [M0, M1], refined analysis and targeted processing are performed. The specific process is as follows: Set the comparison factors of the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr, and mark them as λa, λb, and λc respectively; When the communication anomaly evaluation index Zcom is higher than the comparison factor λa, a No. 1 optimization management instruction is generated; When the data conflict evaluation index Zdac is higher than the comparison factor λb, the second optimization management instruction is generated; When the structural fault assessment index Zstr is higher than the comparison factor λc, the third optimization management instruction is generated.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention accepts user requests and performs corresponding business docking work through the behavior control module, collects the operation information of the business docking work through the information retrieval module, and then processes the operation information through the central processing unit, thereby monitoring the communication anomaly degree, data conflict degree and structural failure degree of the intelligent robot, and then comprehensively analyzes and evaluates the comprehensive failure degree of the intelligent robot in the business docking work, and generates a system failure monitoring report and an alarm signal to ensure the comprehensiveness and comprehensiveness of data monitoring; The present invention receives system fault monitoring reports through the fault monitoring module and performs visual monitoring and presentation, and receives alarm signals through the alarm processing module and generates optimization management instructions and feeds back to the behavior control module for timely exception handling, thereby continuously improving the stability of communication transmission and data docking, ensuring the integrity and security of data storage, and finally, through fault monitoring and exception handling of business docking work, ensuring the normal operation of the intelligent robot system and achieving the reliability of seamless docking of business work between systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A connection diagram of the system modules of the present invention is shown; Figure 2 A schematic flow chart of the overall solution of the present invention is shown. DETAILED DESCRIPTION

[0016] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: like Figure 1-Figure 2 As shown, an intelligent robot for realizing seamless connection between systems includes a behavior control module, an information retrieval module, a central processing unit, a fault monitoring module and an alarm processing module, wherein the behavior control module, the information retrieval module, the central processing unit, the fault monitoring module and the alarm processing module are electrically connected; The working steps are as follows: S1, the behavior control module accepts user requests and performs corresponding business docking work: the user inputs the business request to the intelligent robot, the behavior control module accepts the user request, and generates corresponding keyboard and mouse operation instructions, thereby performing the business docking work requested by the user.

[0018] S2, the information retrieval module collects the operation information of the business docking work: sets the information collection cycle Tc to collect the operation information regularly, and the operation information includes communication abnormality parameters, data conflict parameters and structural fault parameters.

[0019] The operation information of business connection work is collected through the system monitoring tool. The specific process of collecting the operation information of business connection work is as follows: S2-1, communication anomaly parameters include page response time, data loss rate, data request success rate and number of concurrent access requests; among them, page response time is the time interval between each user request and system response, which is used to monitor the delay of API calls; data loss rate is the proportion of information lost or failed to arrive during data transmission; data request success rate refers to the calculation of the successful response rate of data requests, which is used to evaluate the reliability of the interface; the number of concurrent access requests refers to the number of requests of the system under high concurrency conditions.

[0020] S2-2, data conflict parameters include transaction execution consistency index, data format consistency index and data security risk index; among them, transaction execution consistency index refers to the proportion of operations executed as expected in all actual operation steps in a multi-step transaction, which is used to verify whether all operations in a multi-step transaction are executed as expected and the results are consistent; data format consistency index refers to the proportion of data that conforms to the expected standard format in all docking data, which is used to check whether the data fields and structures conform to the expected standard format; data security risk index refers to the number of potential security attack events, which is used to assess the potential risk level of data security offensiveness.

[0021] S2-3, structural failure parameters include data interface failure rate, database storage failure rate, number of data leakage and number of abnormal access; among them, the data interface failure rate refers to the proportion of errors in the API (application programming interface) of data exchange; the database storage failure rate refers to the proportion of failures in the database or storage system; the number of data leakage refers to the number of data leakages; the number of abnormal access refers to the number of abnormal access attempts.

[0022] S3, the central processor processes the operation information: by constructing a preliminary analysis model to process the communication anomaly parameters, data conflict parameters and structural fault parameters, the communication anomaly degree, data conflict degree and structural fault degree of the intelligent robot are monitored in turn, and then the comprehensive fault degree of the intelligent robot in business docking is comprehensively analyzed and evaluated, and a system fault monitoring report and corresponding alarm signals are generated; The communication anomaly parameters, data conflict parameters and structural fault parameters are processed by constructing a preliminary analysis model.

[0023] S3-1, the specific process of the preliminary analysis model is: Input a number set Ψe of parameter E into the preliminary analysis model, wherein the number set Ψe includes data values ​​of parameter E corresponding to N0 information collection cycles, and any data value of the number set Ψe is marked as φe; Set the standard interval of parameter E[ , ], when the data value φe of parameter E is in the standard interval [ , ], the data of parameter E is normal; otherwise, when the data value φe of parameter E is not within the standard interval [ , ], the data of parameter E is judged to be abnormal; By the set Ψe of parameter E and the standard interval [ , ] to obtain the evaluation coefficient EVAe of parameter E: , where ω0 is the correction coefficient and ω0 is greater than 0. The correction coefficient ω0 is to ensure A preset constant value that is always greater than 1; is the coefficient of deviation of parameter E: ; When the data value φe of parameter E deviates from the standard interval [ , ] is higher, and the deviation coefficient of parameter E is When it is higher, the evaluation coefficient EVAe of parameter E is higher, and thus the abnormality of evaluation parameter E is higher; Preliminary analysis of the evaluation coefficient EVAe of the model output parameter E.

[0024] S3-201, the specific process of monitoring the degree of communication anomaly of the intelligent robot is as follows: Communication anomaly parameters include page response time a1, data loss rate a2, data request success rate a3, and concurrent access request number a4; Through the communication anomaly parameters corresponding to N0 information collection cycles Tc, the sets of page response time a1, data loss rate a2, data request success rate a3 and concurrent access request number a4 are constructed respectively, and marked as Ψa1, Ψa2, Ψa3, Ψa4 in turn; Substitute the data set Ψa1 of the page response time a1 into the preliminary analysis model to obtain and output the evaluation coefficient EVAa1 of the page response time a1; Substitute the data loss rate a2 data set Ψa2 into the preliminary analysis model to obtain and output the evaluation coefficient EVAa2 of the data loss rate a2; Substitute the data set Ψa3 of the data request success rate a3 into the preliminary analysis model to obtain and output the evaluation coefficient EVAa3 of the data request success rate a3; Substituting the number set Ψa4 of the concurrent access request number a4 into the preliminary analysis model, obtaining and outputting the evaluation coefficient EVAa4 of the concurrent access request number a4; The communication anomaly evaluation index Zcom is obtained by combining the evaluation coefficient EVAa1 of the page response time a1, the evaluation coefficient EVAa2 of the data loss rate a2, the evaluation coefficient EVAa3 of the data request success rate a3, and the evaluation coefficient EVAa4 of the concurrent access request number a4: ; Among them, α1, α2, α3, and α4 are conversion coefficients of page response time a1, data loss rate a2, data request success rate a3, and concurrent access request number a4, respectively, and α1, α2, α3, and α4 are all greater than 1. The conversion coefficients are preset and obtained after being measured by a large amount of experimental data; when the evaluation coefficient EVAa1 of page response time a1, the evaluation coefficient EVAa2 of data loss rate a2, the evaluation coefficient EVAa3 of data request success rate a3, and the evaluation coefficient EVAa4 of concurrent access request number a4 are higher, the higher the communication anomaly evaluation index Zcom is, and the higher the degree of communication anomaly of the intelligent robot is evaluated; Set the evaluation interval of the communication anomaly evaluation index Zcom, and evaluate the degree of communication anomaly of the intelligent robot through interval comparison.

[0025] S3-202, the specific process of monitoring the data conflict degree of the intelligent robot is: Data conflict parameters include transaction execution consistency index b1, data format consistency index b2, and data security risk index b3; Through the data conflict parameters corresponding to N0 information collection cycles Tc, the number sets of execution consistency index b1, data format consistency index b2 and data security risk index b3 are constructed respectively, and marked as Ψb1, Ψb2, Ψb3 respectively; Substituting the number set Ψb1 of the execution consistency index b1 into the preliminary analysis model, obtaining and outputting the evaluation coefficient EVAb1 of the execution consistency index b1; Substitute the data set Ψb2 of the data format consistency index b2 into the preliminary analysis model to obtain and output the evaluation coefficient EVAb2 of the data format consistency index b2; Substitute the data security risk index b3 number set Ψb3 into the preliminary analysis model to obtain and output the evaluation coefficient EVAb3 of the data security risk index b3; The data conflict evaluation index Zdac is obtained by combining the evaluation coefficient EVAb1 of the consistency index b1, the evaluation coefficient EVAb2 of the data format consistency index b2, and the evaluation coefficient EVAb3 of the data security risk index b3: ; Among them, β1, β2, and β3 are the conversion coefficients of the transaction execution consistency index b1, the data format consistency index b2, and the data security risk index b3, respectively, and β1, β2, and β3 are all greater than 1; when the evaluation coefficient EVAb1 of the execution consistency index b1, the evaluation coefficient EVAb2 of the data format consistency index b2, and the evaluation coefficient EVAb3 of the data security risk index b3 are higher, the data conflict evaluation index Zdac is higher, and the data conflict degree of the intelligent robot is evaluated higher; Set the evaluation interval of the data conflict assessment index Zdac, and evaluate the level of data conflict of the intelligent robot through interval comparison.

[0026] S3-203, the specific process of monitoring the structural failure degree of the intelligent robot is: The structural fault parameters include data interface failure rate c1, database storage failure rate c2, data leakage quantity c3 and abnormal access quantity c4; Through the structural fault parameters corresponding to N0 information collection cycles Tc, the data interface failure rate c1, database storage failure rate c2, data leakage number c3 and abnormal access number c4 are constructed respectively, and marked as Ψc1, Ψc2, Ψc3, Ψc4 respectively; Substitute the data set Ψc1 of the data interface failure rate c1 into the preliminary analysis model to obtain and output the evaluation coefficient EVAc1 of the data interface failure rate c1; Substitute the number set Ψc2 of the database storage failure rate c2 into the preliminary analysis model to obtain and output the evaluation coefficient EVAc2 of the database storage failure rate c2; Substitute the data set Ψc3 of the number of data leaks c3 into the preliminary analysis model to obtain and output the evaluation coefficient EVAc3 of the number of data leaks c3; Substitute the data set Ψc4 of the number of abnormal accesses c4 into the preliminary analysis model to obtain and output the evaluation coefficient EVAc4 of the number of abnormal accesses c4; The structural fault evaluation index Zstr is obtained by combining the evaluation coefficient EVAc1 of the data interface failure rate c1, the evaluation coefficient EVAc2 of the database storage failure rate c2, the evaluation coefficient EVAc3 of the data leakage number c3, and the evaluation coefficient EVAc4 of the abnormal access number c4: ; Among them, γ1, γ2, γ3, and γ4 are the conversion coefficients of the data interface failure rate c1, the database storage failure rate c2, the number of data leakage c3, and the number of abnormal access c4, respectively, and γ1, γ2, γ3, and γ4 are all greater than 1; when the evaluation coefficient EVAc1 of the data interface failure rate c1, the evaluation coefficient EVAc2 of the database storage failure rate c2, the evaluation coefficient EVAc3 of the number of data leakage c3, and the evaluation coefficient EVAc4 of the number of abnormal access c4 are higher, the higher the structural fault evaluation index Zstr is, and the higher the degree of structural fault of the intelligent robot is evaluated; The evaluation interval of the structural fault evaluation index Zstr is set, and the level of the structural fault degree of the intelligent robot is evaluated by interval comparison.

[0027] S3-3, the specific process of evaluating the comprehensive failure degree of the intelligent robot is as follows: S3-301, set a data calculation period Ts to perform regular calculations on the communication anomaly evaluation index Zcom, the data conflict evaluation index Zdac, and the structural fault evaluation index Zstr, and obtain historical data of the corresponding evaluation indexes; Then, the dynamic curves of the communication anomaly assessment index Zcom, the data conflict assessment index Zdac and the structural fault assessment index Zstr are constructed respectively and marked as Sa, Sb and Sc respectively.

[0028] S3-302, construct a curve analysis model to analyze the dynamic curve. The specific process is as follows: Input the curve Sf to the curve analysis model, extract n0 discrete points of the curve Sf, mark any point of the curve Sf as p (Xp, Yp), and mark the adjacent points of point p as q (Xq, Yq), where the horizontal axis is the period of the curve Sf and the vertical axis is the data value of the curve Sf; The overall coefficient η1 of the curve Sf is obtained by the average value of the ordinates of n0 points of the curve Sf: ; Obtain the slope value Kp of point p through the coordinates of point p and its adjacent point q: ; Then, the average increase rate Kf of the curve Sf is obtained by averaging the slope values ​​of n0 points of the curve Sf: ; Then calculate the standard deviation of the slope values ​​of n0 points of the curve Sf, and obtain the rate of change coefficient σf of the curve Sf: ; By combining the average increase rate Kf and the increase rate variation coefficient σf of the curve Sf, the stability coefficient η2 of the curve Sf is obtained: ; Among them, μ1 and μ2 are the weight coefficients of the average increase rate Kf and the increase rate variation coefficient σf, respectively, and both μ1 and μ2 are greater than 0, μ1+μ2=1; when the square value of the average increase rate Kf of the curve Sf is higher and the increase rate variation coefficient σf is higher, the stability coefficient η2 of the curve Sf is lower, and the stability of the evaluation curve Sf is worse; By combining the overall coefficient η1 and the stability coefficient η2 of the curve Sf, the risk coefficient Rf of the curve Sf is obtained: ; Among them, υ1 and υ2 are weight indexes of the overall coefficient η1 and the stability coefficient η2, respectively, and υ1 and υ2 are both greater than 0; when the overall coefficient η1 of the curve Sf is higher and the stability coefficient η2 is lower, the risk coefficient Rf of the curve Sf is higher, indicating that the overall level of the curve Sf is high and the stability is poor, so the risk level of the curve Sf is evaluated to be higher; The curve analysis model outputs the risk factor Rf of the curve Sf.

[0029] S3-303, substituting the dynamic curve Sa between the communication anomaly assessment index Zcom and the data measurement period Ts into the curve analysis model, thereby outputting the risk coefficient Ra of the dynamic curve Sa; Substitute the dynamic curve Sb between the data conflict assessment index Zdac and the data calculation period Ts into the curve analysis model, so as to output the risk coefficient Rb of the dynamic curve Sb; The dynamic curve Sc between the structural fault assessment index Zstr and the data measurement period Ts is substituted into the curve analysis model to output the risk coefficient Rc of the dynamic curve Sc.

[0030] S3-304, by combining the risk coefficient Ra of the dynamic curve Sa, the risk coefficient Rb of the dynamic curve Sb and the risk coefficient Rc of the dynamic curve Sc, the comprehensive failure risk index Risk of the intelligent robot is obtained: ; When the risk coefficient Ra of the dynamic curve Sa, the risk coefficient Rb of the dynamic curve Sb, and the risk coefficient Rc of the dynamic curve Sc are higher, the comprehensive failure risk index Risk of the intelligent robot is higher, and the comprehensive failure risk degree of the intelligent robot is higher.

[0031] S3-305, setting an evaluation interval for the comprehensive fault risk index Risk, evaluating the comprehensive fault level of the intelligent robot in business docking through interval comparison, and generating a system fault monitoring report and corresponding alarm signals; The number of evaluation intervals marked with the comprehensive fault risk index Risk is N1, and any evaluation interval is marked as Gi, where i is the serial number of the evaluation interval and 0<i≤N1. When the comprehensive fault risk index Risk is in the evaluation interval Gi, the comprehensive fault degree of the intelligent robot is determined to be level i, and an i-level alarm signal and a system fault monitoring report are generated; the higher the comprehensive fault risk index Risk is, the higher the level i of the alarm signal is.

[0032] S4, the fault monitoring module receives the system fault monitoring report and performs corresponding visual presentation; The system fault monitoring report includes the following data: Comprehensive failure risk index Risk and comprehensive failure degree level i; Communication anomaly assessment index Zcom, communication anomaly level Na and risk factor Ra; Data conflict assessment index Zdac, data conflict level Nb and risk factor Rb; Structural failure assessment index Zstr, structural failure degree level Nc and risk factor Rc; Through the system fault monitoring report, corresponding visual monitoring prompts are provided to the managers of the intelligent robot.

[0033] S5, the alarm processing module receives the alarm signal and performs corresponding exception processing: by receiving the alarm signal and performing refined analysis, it generates optimized management instructions for exception processing, and feeds back to the behavior control module to re-perform the business docking work requested by the user; Set the comparison interval of the alarm signal level to [M0, M1]; S5-1, when the level i of the alarm signal is less than M0, no processing is performed; S5-2, when the level i of the alarm signal is in the comparison interval [M0, M1], a refined analysis and targeted processing are performed. The specific process is as follows: Set the comparison factors of the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr, and mark them as λa, λb, and λc respectively; When the communication anomaly evaluation index Zcom is higher than the comparison factor λa, a No. 1 optimization management instruction is generated and the communication transmission anomaly is processed; When the data conflict assessment index Zdac is higher than the comparison factor λb, the second optimization management instruction is generated and the data storage anomaly is processed; When the structural fault evaluation index Zstr is higher than the comparison factor λc, the third optimization management instruction is generated and the hardware structural abnormality is processed.

[0034] S5-3, when the level i of the alarm signal is greater than M1, a fourth optimization management instruction is generated and comprehensive management is performed, which includes communication transmission exception handling, data storage exception handling and hardware structure exception handling; Among them, the exception handling operation is designed in combination with the actual application of the intelligent robot, for example: Communication transmission anomaly handling includes checking physical connections and troubleshooting external interference. By checking whether physical equipment such as network cables, routers, switches, etc. are intact and whether the connection is stable, and checking whether there are malicious attacks or other external factors interfering with normal communication, such as DDoS attacks and phishing, the stability of communication transmission is continuously improved. Data storage anomaly handling includes assessing the system virus situation and replacing new storage devices. If it is detected that the data storage anomaly is caused by virus infection, antivirus software is used to remove viruses from the files in the storage device, and data recovery software is used to restore the files immediately to ensure data integrity and security. By replacing new storage devices, storage stability is guaranteed during subsequent data docking. Hardware structure anomaly handling includes hardware replacement and upgrade. By checking damaged hardware devices, such as CPU, memory, hard disk, interface and other key components, hardware with insufficient performance or aging is replaced in time to ensure the normal operation of the system and achieve seamless connection between systems.

[0035] In summary, the present invention accepts user requests and performs corresponding business docking work through the behavior control module, collects the operation information of the business docking work through the information retrieval module, and then processes the operation information through the central processing unit, thereby monitoring the communication anomaly degree, data conflict degree and structural failure degree of the intelligent robot, and then comprehensively analyzes and evaluates the comprehensive failure degree of the intelligent robot in the business docking work, and generates a system fault monitoring report and an alarm signal to ensure the comprehensiveness and comprehensiveness of data monitoring; The present invention receives system fault monitoring reports through the fault monitoring module and performs visual monitoring and presentation, and receives alarm signals through the alarm processing module and generates optimization management instructions and feeds back to the behavior control module for timely exception handling, thereby continuously improving the stability of communication transmission and data docking, ensuring the integrity and security of data storage, and finally, through fault monitoring and exception handling of business docking work, ensuring the normal operation of the intelligent robot system and achieving the reliability of seamless docking of business work between systems.

[0036] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0037] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent robot that realizes seamless connection between systems, characterized in that: It includes a behavior control module, an information retrieval module, a central processing unit, a fault monitoring module and an alarm processing module, wherein the behavior control module, the information retrieval module, the central processing unit, the fault monitoring module and the alarm processing module are electrically connected; The behavior control module is used to accept user requests and perform corresponding business docking work: the user inputs the business request to the intelligent robot, and the behavior control module accepts the user request and generates corresponding keyboard and mouse operation instructions to perform the business docking work requested by the user; The information retrieval module is used to collect the operation information of the business docking work: set the information collection cycle Tc to collect the operation information regularly, and the operation information includes communication abnormality parameters, data conflict parameters and structural fault parameters; The central processing unit is used to process the operation information: by constructing a preliminary analysis model to process the communication anomaly parameters, data conflict parameters and structural fault parameters, the communication anomaly degree, data conflict degree and structural fault degree of the intelligent robot are monitored in turn, and then the comprehensive fault degree of the intelligent robot in business docking is comprehensively analyzed and evaluated, and a system fault monitoring report and corresponding alarm signals are generated; The fault monitoring module is used to receive system fault monitoring reports and perform corresponding visual presentation; The alarm processing module is used to receive alarm signals and perform corresponding exception processing: by receiving alarm signals and performing refined analysis, it generates optimized management instructions for exception processing, and feeds back to the behavior control module to re-perform the business docking work requested by the user.

2. The intelligent robot for realizing seamless connection between systems according to claim 1, characterized in that: The specific process of the preliminary analysis model is: Input a number set Ψe of parameter E into the preliminary analysis model, wherein the number set Ψe includes data values ​​of parameter E corresponding to N0 information collection cycles, and any data value of the number set Ψe is marked as φe; Set the standard interval of parameter E[ , ], when the data value φe of parameter E is in the standard interval [ , ], the data of parameter E is judged to be normal; otherwise, the data of parameter E is judged to be abnormal; By the set Ψe of parameter E and the standard interval [ , ] to obtain the evaluation coefficient EVAe of parameter E: , where ω0 is the correction coefficient and ω0 is greater than 0, is the coefficient of deviation of parameter E and ; Preliminary analysis of the evaluation coefficient EVAe of the model output parameter E.

3. The intelligent robot for realizing seamless connection between systems according to claim 2, characterized in that: The specific process of monitoring the degree of communication anomaly of the intelligent robot is as follows: Communication anomaly parameters include page response time a1, data loss rate a2, data request success rate a3, and concurrent access request number a4; Through the communication anomaly parameters corresponding to N0 information collection cycles Tc, the sets of page response time a1, data loss rate a2, data request success rate a3 and concurrent access request number a4 are constructed respectively, and marked as Ψa1, Ψa2, Ψa3, Ψa4 in turn; Substitute the number set Ψa1 of page response time a1, the number set Ψa2 of data loss rate a2, the number set Ψa3 of data request success rate a3 and the number set Ψa4 of concurrent access request number a4 into the preliminary analysis model respectively, so as to output the evaluation coefficient EVAa1 of page response time a1, the evaluation coefficient EVAa2 of data loss rate a2, the evaluation coefficient EVAa3 of data request success rate a3 and the evaluation coefficient EVAa4 of concurrent access request number a4 in turn, and then comprehensively obtain the communication anomaly evaluation index Zcom; Set the evaluation interval of the communication anomaly evaluation index Zcom, and evaluate the degree of communication anomaly of the intelligent robot through interval comparison.

4. The intelligent robot for realizing seamless connection between systems according to claim 3, characterized in that: The specific process of monitoring the data conflict degree of the intelligent robot is as follows: Data conflict parameters include transaction execution consistency index b1, data format consistency index b2, and data security risk index b3; Through the data conflict parameters corresponding to N0 information collection cycles Tc, the number sets of execution consistency index b1, data format consistency index b2 and data security risk index b3 are constructed respectively, and marked as Ψb1, Ψb2, Ψb3 respectively; Substituting the number set Ψb1 of the execution consistency index b1, the number set Ψb2 of the data format consistency index b2 and the number set Ψb3 of the data security risk index b3 into the preliminary analysis model in turn, the evaluation coefficient EVAb1 of the execution consistency index b1, the evaluation coefficient EVAb2 of the data format consistency index b2 and the evaluation coefficient EVAb3 of the data security risk index b3 are output in turn, and then the data conflict evaluation index Zdac is comprehensively obtained; Set the evaluation interval of the data conflict assessment index Zdac, and evaluate the level of data conflict of the intelligent robot through interval comparison.

5. The intelligent robot for realizing seamless connection between systems according to claim 4, characterized in that: The specific process of monitoring the structural failure degree of the intelligent robot is: The structural fault parameters include data interface failure rate c1, database storage failure rate c2, data leakage quantity c3 and abnormal access quantity c4; Through the structural fault parameters corresponding to N0 information collection cycles Tc, the data interface failure rate c1, database storage failure rate c2, data leakage number c3 and abnormal access number c4 are constructed respectively, and marked as Ψc1, Ψc2, Ψc3, Ψc4 respectively; Substitute the data interface failure rate c1 number set Ψc1, the database storage failure rate c2 number set Ψc2, the data leakage number c3 number set Ψc3 and the abnormal access number c4 number set Ψc4 into the preliminary analysis model respectively, so as to output the evaluation coefficient EVAc1 of the data interface failure rate c1, the evaluation coefficient EVAc2 of the database storage failure rate c2, the evaluation coefficient EVAc3 of the data leakage number c3 and the evaluation coefficient EVAc4 of the abnormal access number c4 in turn, and then comprehensively obtain the structural fault evaluation index Zstr; The evaluation interval of the structural fault evaluation index Zstr is set, and the level of the structural fault degree of the intelligent robot is evaluated by interval comparison.

6. The intelligent robot for realizing seamless connection between systems according to claim 5, characterized in that: The specific process of evaluating the comprehensive failure degree of intelligent robots is as follows: Set the data calculation period Ts to perform regular calculations on the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr to obtain the historical data of the corresponding assessment indexes; then construct the dynamic curves of the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr, and mark them as Sa, Sb, and Sc respectively; Construct a curve analysis model to analyze the dynamic curve. The specific process is as follows: Input the curve Sf to the curve analysis model, extract n0 discrete points of the curve Sf, mark any point of the curve Sf as p (Xp, Yp), and mark the adjacent points of point p as q (Xq, Yq), where the horizontal coordinate is the period of the curve Sf and the vertical coordinate is the data value of the curve Sf; obtain the overall coefficient η1 of the curve Sf through the average value of the vertical coordinates of the n0 points of the curve Sf; obtain the slope value Kp of the point p through the coordinates of point p and its adjacent point q; obtain the average increase rate Kf of the curve Sf through the average value of the slope values ​​of the n0 points of the curve Sf; then calculate the standard deviation of the slope values ​​of the n0 points of the curve Sf to obtain the increase rate variation coefficient σf of the curve Sf; obtain the stability coefficient η2 of the curve Sf by combining the average increase rate Kf of the curve Sf and the increase rate variation coefficient σf; obtain the risk coefficient Rf of the curve Sf by combining the overall coefficient η1 and the stability coefficient η2 of the curve Sf; the curve analysis model outputs the risk coefficient Rf of the curve Sf; The dynamic curves Sa, Sb and Sc are respectively substituted into the curve analysis model to output the risk coefficients Ra, Rb and Rc in turn; the comprehensive failure risk index Risk of the intelligent robot is obtained by combining the risk coefficient Ra of the dynamic curve Sa, the risk coefficient Rb of the dynamic curve Sb and the risk coefficient Rc of the dynamic curve Sc.

7. The intelligent robot for realizing seamless connection between systems according to claim 6, characterized in that: Set the evaluation interval of the comprehensive fault risk index Risk, evaluate the comprehensive fault level of the intelligent robot in business docking through interval comparison, and generate a system fault monitoring report and corresponding alarm signals; The number of evaluation intervals marked with the comprehensive fault risk index Risk is N1, and any evaluation interval is marked as Gi, where i is the serial number of the evaluation interval and 0<i≤N1. When the comprehensive fault risk index Risk is in the evaluation interval Gi, the comprehensive fault degree of the intelligent robot is determined to be level i, and a level i alarm signal and a system fault monitoring report are generated.

8. The intelligent robot for realizing seamless connection between systems according to claim 7, characterized in that: The specific process of fine-grained analysis of alarm signals is as follows: Set the comparison interval of the alarm signal level to [M0, M1]; When the alarm signal level i<M0, no processing is performed; When the level i of the alarm signal is greater than M1, a No. 4 optimization management instruction is generated; When the level i of the alarm signal is in the comparison interval [M0, M1], refined analysis and targeted processing are performed. The specific process is as follows: Set the comparison factors of the communication anomaly assessment index Zcom, the data conflict assessment index Zdac, and the structural fault assessment index Zstr, and mark them as λa, λb, and λc respectively; When the communication anomaly evaluation index Zcom is higher than the comparison factor λa, a No. 1 optimization management instruction is generated; When the data conflict evaluation index Zdac is higher than the comparison factor λb, the second optimization management instruction is generated; When the structural fault assessment index Zstr is higher than the comparison factor λc, the third optimization management instruction is generated.

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