Collaborative robot interaction method based on multi-dimensional data analysis

Abstract

The invention discloses a collaborative robot interaction method based on multi-dimensional data analysis, and belongs to the technical field of collaborative interaction. Implementing local reliability analysis of a local complete interaction reaction dimension according to all complete cooperation segmented curves in the complete interaction cooperation curve set to obtain a local interaction reaction reliability processing sequence; implementing local reliability analysis of the local part interaction reaction dimension according to all the part interaction subsection curves in the part interaction subsection curve set to obtain a local interaction disappearance reliability processing sequence; according to the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence which are obtained through processing, interaction reliability processing analysis of the overall dimension is carried out on interaction of the collaborative robot, and dynamic optimization and upgrading are carried out on interaction of the collaborative robot in a self-adaptive mode; the method is used for solving the technical problems that in an existing scheme, the data processing and analysis effect of the collaborative robot interaction overall dimension is poor, and the autonomous dynamic optimization and upgrading effect of the overall dimension is poor.

Description

Technical Field

[0001] The present invention relates to the field of collaborative interaction technology, and in particular to a collaborative robot interaction method based on multidimensional data analysis. Background Art

[0002] Collaborative robot interaction refers to how the robot interacts with human workers and how the robot understands and responds to changes in the environment; this interaction is designed to ensure that tasks are completed safely and efficiently.

[0003] When existing collaborative robot interaction solutions are implemented, the regulatory analysis of the collaborative robot interaction process still remains at the level of local monitoring and analysis in different aspects, and thus can only implement local optimization control in the corresponding aspects. It is impossible to use the results of different local monitoring and analysis to implement an overall dimension of interaction reliability expansion analysis of the collaborative robot interaction, and to adaptively and dynamically optimize and upgrade the collaborative robot interaction based on the processing and analysis results. This results in poor data processing and analysis effects in the overall dimension of collaborative robot interaction, and poor effects of autonomous dynamic optimization and upgrading in the overall dimension. Summary of the invention

[0004] The purpose of the present invention is to provide a collaborative robot interaction method based on multidimensional data analysis, which is used to solve the technical problems of poor data processing and analysis effect of the overall dimension of collaborative robot interaction and poor autonomous dynamic optimization and upgrading effect of the overall dimension in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions: Collaborative robot interaction method based on multi-dimensional data analysis, including: The running trajectory curves corresponding to the collaborative robot detected by the detection sensor are divided and classified by using a pre-constructed three-dimensional trajectory coordinate system, and a number of running trajectory segment curves and their corresponding curve sets are obtained; the curve set includes a complete interactive collaborative curve set and a partial interactive collaborative curve set; According to the complete interaction synergy curve set and the partial interaction synergy curve set, data processing of different interaction reaction dimensions and local abnormal data analysis are respectively performed, and the analysis results obtained by the corresponding processing of different interaction reaction dimensions are sorted and combined to obtain the corresponding local interaction reaction reliable processing sequence and local interaction disappearance reliable processing sequence; Among them, the local interactive response reliable processing sequence is obtained by sorting and combining the local interactive response reliable identifiers obtained by all processing and all local complete collaborative abnormalities; The local interaction disappearance reliable processing sequence is obtained by sorting and combining the local interaction disappearance reliabilities obtained from all processing; The interaction reliability processing and analysis of the collaborative robot's interaction is performed in an overall dimension based on the obtained local interaction response reliable processing sequence and the local interaction disappearance reliable processing sequence, and the collaborative robot's interaction is adaptively dynamically optimized and upgraded based on the processing and analysis results; the dynamic optimization and upgrade includes local optimization and upgrade of the collaborative robot's interaction or overall optimization and upgrade of the collaborative robot's interaction.

[0006] Preferably, the real-time operation coordinates, real-time operation timestamps and real-time operation forces corresponding to the operation of the collaborative robot are displayed and connected through a pre-constructed three-dimensional trajectory coordinate system to obtain an operation trajectory curve; When dividing the running track curve, two adjacent real-time operating forces with values ​​of 0 are obtained, and the running track curve between the two adjacent real-time operating forces with values ​​of 0 is marked as a running track segmented curve; When analyzing and marking the types of different running track segment curves in sequence according to the operation time sequence, the external force detection values ​​and the duration of external force detection corresponding to the different running track segment curves are obtained in sequence, and the external force detection values ​​and the duration of external force detection are analyzed by the running interaction type recognition function and the corresponding running interaction type identification JLj is output; The running interaction type identifier includes a value of 0 or 1, indicating that the corresponding running interaction type is a complete interaction collaboration type or a partial interaction collaboration type.

[0007] Preferably, the expression for running the interaction type identification function is: ; In the formula, j is the sorted segmented curves of different operation trajectories, j=1, 2, 3, ..., m; m is a positive integer; WJj and TWj are the total external force detection values ​​and the total duration of external force continuous detection corresponding to the segmented curves of different operation trajectories; WTj and TTj are the total operating force values ​​and the total duration of operating force corresponding to the segmented curves of different operation trajectories of the collaborative robot.

[0008] Preferably, according to the operation interaction type identifier with a value of 0, the corresponding motion trajectory segment curve is marked as a complete collaborative segment curve; According to the operation interaction type identifier with a value of 1, the corresponding motion trajectory segment curve is marked as a partial cooperative segment curve; According to the operation interaction type identifier with a value of 0, all the corresponding complete collaborative segmented curves are sorted and combined to obtain the corresponding complete interaction collaborative curve set; and according to the operation interaction type identifier with a value of 1, all the corresponding partial collaborative segmented curves are sorted and combined to obtain the corresponding partial interaction collaborative curve set.

[0009] Preferably, a local reliability analysis of the local complete interaction reaction dimension is performed according to all the complete collaborative segmented curves in the complete interaction collaborative curve set, and the external force detection value detection timestamp, collaborative robot reaction timestamp, external force detection instantaneous value, and operation reaction instantaneous value corresponding to different complete collaborative segmented curves in the complete interaction collaborative curve set are obtained in turn and sorted and combined to obtain the local passive interaction monitoring sequences corresponding to different complete collaborative segmented curves; When the local interaction reaction corresponding to the complete collaborative segmented curve is reliably processed according to the local passive interaction monitoring sequence, all the data in the local passive interaction monitoring sequence are analyzed by the local interaction reaction identification function, and the corresponding local interaction reaction reliable identification FKi is output; The local interaction reaction reliability flag includes a value of 0 or 1, indicating that the local interaction reaction reliability state corresponding to the complete synergy segmented curve is normal or abnormal.

[0010] Preferably, the expression of the local interaction reaction identification function is: ; In the formula, i is a different complete collaborative segmented curve in the passive drag anomaly analysis set, i=1, 2, 3, ..., n; n is a positive integer; TFi and LFi are the collaborative interaction reaction time difference value and collaborative interaction reaction implementation force difference value corresponding to different complete collaborative segmented curves respectively; TFi0 and LFi0 are the collaborative interaction reaction time difference standard value and collaborative interaction reaction implementation force difference standard value respectively.

[0011] Preferably, according to the local reaction reliable flag with a value of 1, the local passive interaction monitoring sequence is passed through the formula Calculate and obtain the local complete collaborative anomaly degree JWi corresponding to the complete collaborative segmented curve; where a and b are different calculation weight coefficients, and 0<a≤b<1; The reliable identifications of all local interaction reactions obtained by processing and all local complete collaborative abnormalities are sorted and combined to obtain a reliable processing sequence of local interaction reactions.

[0012] Preferably, a local reliability analysis of the local partial interaction reaction dimension is performed according to all partial cooperative segmented curves in the partial interaction cooperative curve set, and the drag disappearance timestamp t and the first monitoring coordinate corresponding to the target monitoring point of the cooperative robot when the real-time drag force is 0 in different partial cooperative segmented curves are obtained in turn. , and obtain the second monitoring coordinates corresponding to the target monitoring point of the collaborative robot after t0 seconds , and by the formula Calculate and obtain the local interaction disappearance reliability XKk corresponding to the real-time drag force of different parts of the collaborative segmented curve being 0; where k is the target monitoring point of the collaborative robot when the real-time drag force of different parts of the collaborative segmented curve is 0, k=1, 2, 3, ..., p; p is a positive integer; The local interaction disappearance reliability contains a value of 0 or non-0, indicating whether the local interaction disappearance reliability state corresponding to the corresponding part of the collaborative segment curve is normal or abnormal; The local interaction disappearance reliabilities obtained from all processes are sorted and combined to obtain a local interaction disappearance reliable processing sequence.

[0013] Preferably, the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence are traversed respectively, and the elements with non-zero values ​​are counted and analyzed; If there are non-zero elements in the reliable processing sequence of local interaction reaction and / or the reliable processing sequence of local interaction disappearance, then the formula Calculate and obtain the interaction anomaly integration degree JZ of collaborative robot interaction; where β is the local dimension influence weight, and 1<β; JWi´, XYk´ are the local complete collaborative anomaly degree and local interaction disappearance reliability corresponding to all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; i´, k´ are all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; n´, p´ are the total number of all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; n´<n, p´<p; Conduct data analysis on the integration degree of interaction anomalies and adaptively optimize and upgrade the interaction of collaborative robots dynamically.

[0014] Preferably, if the interaction abnormality integration degree is less than or equal to the interaction abnormality integration threshold, a prompt is given to perform local optimization and upgrade of the collaborative robot interaction; If the interaction anomaly integration degree is greater than the interaction anomaly integration threshold, it is prompted to optimize and upgrade the overall interaction of the collaborative robot.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention can provide multi-dimensional data support for subsequent reliable state analysis of different local interactive reactions by performing data monitoring, statistics, and processing combinations on different local interactive reaction dimensions from the dimensions of reaction time and reaction change force, thereby improving the reliability and comprehensiveness of data processing and analysis.

[0016] The present invention implements local reliability analysis of local complete interaction reaction dimensions according to all complete collaborative segmented curves in the complete interaction collaborative curve set, and sorts and combines the processing and analysis results to obtain a local interaction reaction reliable processing sequence, and implements local reliability analysis of local partial interaction reaction dimensions according to all partial collaborative segmented curves in the partial interaction collaborative curve set, and sorts and combines the processing and analysis results to obtain a local interaction disappearance reliable processing sequence, which can provide reliable complete interaction reaction dimension supervision processing data support and partial interaction reaction dimension supervision data support for the subsequent interactive reliability processing analysis of the overall dimension of collaborative robot interaction.

[0017] The present invention performs interaction reliability processing and analysis on the overall dimension of the interaction of the collaborative robot based on the acquired local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, and adaptively performs dynamic optimization and upgrading on the interaction of the collaborative robot based on the processing and analysis results, thereby realizing the expansion, integration and mining analysis of the early supervision and processing data of different local interaction reaction dimensions, and adaptively performs dynamic optimization and upgrading on the interaction of the collaborative robot based on the analysis results, thereby improving the data processing and analysis effect of the overall dimension of the interaction of the collaborative robot and the autonomous dynamic optimization and upgrading effect of the overall dimension. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings.

[0019] Figure 1 This is a flowchart of the collaborative robot interaction method based on multidimensional data analysis of the present invention.

[0020] Figure 2 This is a schematic diagram of the working structure of the collaborative robot in 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] like Figure 1-Figure 2 As shown, the present invention is a collaborative robot interaction method based on multidimensional data analysis, comprising: The running trajectory curve corresponding to the collaborative robot when it is running is detected and obtained by the detection sensor using a pre-constructed three-dimensional trajectory coordinate system, and the running trajectory curve is divided and classified and numbered to obtain a number of running trajectory segment curves and their corresponding curve sets; including: The detection sensor includes an end effector sensor and an end force sensor; in the embodiment of the present invention, the position and / or posture sensor installed on the end of the robot or the tool, such as a six-dimensional force / torque sensor, can monitor the position and direction changes of the end effector in space and record the dragging path; the contact force during the dragging process can be directly measured by the force sensor installed at the end of the robot, and the real-time dragging force corresponds to the contact force during the dragging process measured by the force sensor at the end of the robot; In addition, the three-dimensional trajectory coordinate system is constructed with the collaborative robot as the coordinate origin and the preset x-axis direction, y-axis direction, z-axis direction, and coordinate spacing. Different axis directions and coordinate spacing can be customized according to actual application scenarios and requirements. The real-time operation coordinates, real-time operation timestamps and real-time operation forces corresponding to the collaborative robot during operation are displayed and connected through a pre-built three-dimensional trajectory coordinate system to obtain an operation trajectory curve; When dividing the running track curve, two adjacent real-time operating forces with values ​​of 0 are obtained, and the running track curve between the two adjacent real-time operating forces with values ​​of 0 is marked as a running track segmented curve; Among them, the real-time operating force can be detected and obtained based on the torque sensor built into the end of the robot; When analyzing and marking the types of different running track segment curves in sequence according to the operation time sequence, the external force detection values ​​and the duration of external force detection corresponding to the different running track segment curves are obtained in sequence, and the external force detection values ​​and the duration of external force detection are analyzed by the running interaction type recognition function and the corresponding running interaction type identification JLj is output; The external force detection value may specifically be a real-time drag force; the duration of the external force detection may be counted by a timer, and the corresponding unit is seconds; In addition, the expression for running the interaction type identification function is ; In the formula, j is the sorted segmented curves of different running trajectories, j=1, 2, 3, ..., m; m is a positive integer; WJj, TWj are the total external force detection values ​​and the total duration of external force continuous detection corresponding to the segmented curves of different running trajectories; WTj, TTj are the total operating force values ​​and the total duration of operating force of the collaborative robot corresponding to the segmented curves of different running trajectories; The running interaction type identifier includes a value of 0 or 1, indicating that the corresponding running interaction type is a complete interaction collaboration type or a partial interaction collaboration type; According to the operation interaction type identifier with a value of 0, the corresponding motion trajectory segment curve is marked as a complete collaborative segment curve; According to the operation interaction type identifier with a value of 1, the corresponding motion trajectory segment curve is marked as a partial cooperative segment curve; In an embodiment of the present invention, data calculation is performed using the total external force detection value and the total duration of external force continuous detection corresponding to different running trajectory segmented curves to obtain the running interaction type identifiers corresponding to the different running trajectory segmented curves. The running interaction type identifiers obtained by calculation can not only realize the digital representation of the curve type corresponding to the running trajectory segmented curve, but also provide reliable data support for subsequent data processing and analysis of different interaction reaction dimensions.

[0023] According to the operation interaction type identifier with a value of 0, all the corresponding complete collaborative segmented curves are sorted and combined to obtain a corresponding complete interactive collaborative curve set; and according to the operation interaction type identifier with a value of 1, all the corresponding partial collaborative segmented curves are sorted and combined to obtain a corresponding partial interactive collaborative curve set; In an embodiment of the present invention, the running trajectory curves acquired through monitoring are divided and classified and numbered to obtain a number of running trajectory segmented curves of different types and their corresponding curve sets, thereby realizing different ways of processing the monitoring data and providing reliable multi-dimensional data support for subsequent data processing and analysis of different interactive reaction dimensions and overall dimensions.

[0024] According to the complete interaction synergy curve set and the partial interaction synergy curve set, data processing of different interaction reaction dimensions and local abnormal data analysis are respectively performed, and the analysis results obtained by corresponding processing of different interaction reaction dimensions are sorted and combined to obtain the corresponding local interaction reaction reliable processing sequence and local interaction disappearance reliable processing sequence; including: According to all the complete collaborative segmented curves in the complete interactive collaborative curve set, local reliability analysis of the local complete interactive reaction dimension is carried out, and the external force detection value detection timestamp, collaborative robot reaction timestamp, external force detection instantaneous value, and operation reaction instantaneous value corresponding to different complete collaborative segmented curves in the complete interactive collaborative curve set are obtained in turn and sorted and combined to obtain the local passive interactive monitoring sequence corresponding to different complete collaborative segmented curves; Among them, the units of the external force detection value detection timestamp and the collaborative robot reaction timestamp are accurate to seconds; the external force detection instantaneous value and the operation reaction instantaneous value are the values ​​corresponding to the first change of the real-time drag force and the real-time operation force respectively; In the embodiment of the present invention, by performing data monitoring statistics and processing combinations on different local interactive reaction dimensions from the reaction time dimension and the reaction change force dimension, multi-dimensional data support can be provided for subsequent reliable state analysis of different local interactive reactions, thereby improving the reliability and comprehensiveness of data processing and analysis.

[0025] When the local interaction reaction corresponding to the complete collaborative segmented curve is reliably processed according to the local passive interaction monitoring sequence, all the data in the local passive interaction monitoring sequence are analyzed by the local interaction reaction identification function, and the corresponding local interaction reaction reliable identification FKi is output; Among them, the expression of the local interaction reaction identification function is: ; In the formula, i is a different complete collaborative segmented curve in the passive drag anomaly analysis set, i=1, 2, 3, ..., n; n is a positive integer; TFi and LFi are the collaborative interaction reaction time difference value and the collaborative interaction reaction implementation force difference value corresponding to different complete collaborative segmented curves respectively; TFi0 and LFi0 are the collaborative interaction reaction time difference standard value and the collaborative interaction reaction implementation force difference standard value respectively. The collaborative interaction reaction time difference standard value and the collaborative interaction reaction implementation force difference standard value can be determined according to the design requirement data of the collaborative robot interaction, or according to the preliminary test data of the collaborative robot interaction; The local interaction reaction reliability flag includes a value of 0 or 1, indicating that the local interaction reaction reliability state corresponding to the complete synergy segment curve is normal or abnormal; In the embodiment of the present invention, by analyzing all data in the local passive interaction monitoring sequence through the local interaction reaction identification function and outputting the corresponding local interaction reaction reliable identification, it is possible to digitally represent the local interaction reaction reliable states corresponding to different complete collaborative segmented curves, and provide reliable data support for the subsequent retrospective analysis of the local complete collaborative abnormal states corresponding to different complete collaborative segmented curves; According to the local reaction reliable flag with a value of 1, the local passive interaction monitoring sequence is passed through the formula Calculate and obtain the local complete collaborative anomaly degree JWi corresponding to the complete collaborative segmented curve; where a and b are different calculation weight coefficients, and 0<a≤b<1, and the specific value is not limited and can be customized according to the actual application requirements of the actual application scenario; The reliable identifications of all local interaction reactions obtained by processing and all local complete coordination abnormalities are sorted and combined to obtain a reliable processing sequence of local interaction reactions; In an embodiment of the present invention, a local reliability analysis of the local complete interaction reaction dimension is implemented based on all complete collaborative segmented curves in the complete interaction collaborative curve set, and the processing and analysis results are sorted and combined to obtain a local interaction reaction reliable processing sequence, which can provide reliable complete interaction reaction dimension supervision processing data support for the subsequent interaction reliability processing analysis of the overall dimension of collaborative robot interaction.

[0026] According to all partial cooperative segmented curves in the partial interactive cooperative curve set, the local reliability analysis of the local partial interactive reaction dimension is carried out, and the drag disappearance timestamp t and the first monitoring coordinate corresponding to the target monitoring point of the cooperative robot when the real-time drag force is 0 in different partial cooperative segmented curves are obtained in turn. , and obtain the second monitoring coordinates corresponding to the target monitoring point of the collaborative robot after t0 seconds , and by the formula Calculate and obtain the local interaction disappearance reliability XKk corresponding to the real-time drag force of different parts of the collaborative segmented curve being 0; wherein k is the target monitoring point of the collaborative robot when the real-time drag force of different parts of the collaborative segmented curve is 0, k=1, 2, 3, ..., p; p is a positive integer; the target monitoring point of the collaborative robot can be determined according to the existing monitoring requirement data, and can also be customized according to the actual application requirements of the actual application scenario; The purpose of the target monitoring point is to determine whether the change in the corresponding real-time operating force meets the design requirements of the collaborative robot interaction when the real-time drag force suddenly disappears; The local interaction disappearance reliability contains a value of 0 or non-0, indicating whether the local interaction disappearance reliability state corresponding to the corresponding part of the collaborative segment curve is normal or abnormal; It should be noted that when the operator suddenly lets go or pauses during the drag interaction, the collaborative robot will usually stop all movements immediately to ensure safety. Therefore, targeted monitoring and analysis is required to determine whether the collaborative robot will subsequently pause operations, to prevent the collaborative robot from continuing to move due to inertia and failing to decelerate in time, which may cause damage. The local interaction disappearance reliabilities obtained by all processing are sorted and combined to obtain a local interaction disappearance reliable processing sequence; In an embodiment of the present invention, a local reliability analysis of the local partial interaction reaction dimension is implemented according to all partial coordination segmented curves in the partial interaction coordination curve set, and the processing and analysis results are sorted and combined to obtain a reliable processing sequence of local interaction disappearance, which can provide reliable partial interaction reaction dimension supervision data support for the subsequent interaction reliability processing analysis of the overall dimension of collaborative robot interaction.

[0027] According to the obtained reliable processing sequence of local interaction reaction and reliable processing sequence of local interaction disappearance, the interaction of the collaborative robot is analyzed in the overall dimension of interaction reliability, and the interaction of the collaborative robot is dynamically optimized and upgraded according to the processing and analysis results; including: The local interaction response reliable processing sequence and the local interaction disappearance reliable processing sequence are traversed respectively, and the elements with non-zero values ​​are counted and analyzed; If all elements in the reliable processing sequence of local interaction response and the reliable processing sequence of local interaction disappearance are 0, then the overall normal instruction of collaborative robot interaction is generated and prompted; If there are non-zero elements in the reliable processing sequence of local interaction reaction and / or the reliable processing sequence of local interaction disappearance, then the formula Calculate and obtain the interaction anomaly integration degree JZ of collaborative robot interaction; where β is the local dimension influence weight, and 1<β, specifically 2.317; JWi´, XYk´ are the local complete collaborative anomaly degree and local interaction disappearance reliability corresponding to all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; i´, k´ are all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; n´, p´ are the total number of all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; n´<n, p´<p; In an embodiment of the present invention, by integrating and calculating the complete interaction reaction dimension supervision processing data and the partial interaction reaction dimension supervision processing data obtained in the previous processing, the interaction anomaly integration degree corresponding to the overall dimension of the collaborative robot interaction is obtained, thereby realizing the digital representation of the interaction anomaly integration state of the collaborative robot interaction.

[0028] Conduct data analysis on the abnormal integration of interactions and adaptively optimize and upgrade the interactions of collaborative robots dynamically; If the interaction abnormality integration degree is less than or equal to the interaction abnormality integration threshold, an overall mild abnormality instruction for the collaborative robot interaction is generated, and a prompt is given to perform local optimization and upgrade of the collaborative robot interaction; the interaction abnormality integration threshold can be determined based on the design requirement data of the collaborative robot interaction, or based on the preliminary test data of the collaborative robot interaction; If the interaction abnormality integration degree is greater than the interaction abnormality integration threshold, an overall severe abnormality instruction for the collaborative robot interaction is generated, and a prompt is given to optimize and upgrade the overall interaction of the collaborative robot; Among them, the local optimization and upgrading of collaborative robot interaction can specifically be the implementation of targeted local optimization and upgrading of local abnormal interactions existing in the complete interaction reaction dimension or partial interaction reaction dimension; The overall optimization and upgrade of collaborative robot interactions can be specifically to implement overall optimization and upgrade of all interactive response dimensions of collaborative robot interactions.

[0029] In the embodiment of the present invention, the interaction reliability processing and analysis of the collaborative robot's interaction is performed in an overall dimension based on the obtained local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, and the collaborative robot's interaction is adaptively dynamically optimized and upgraded based on the processing and analysis results, thereby achieving expansion, integration and mining analysis of the early supervision and processing data of different local interaction reaction dimensions, and adaptively dynamically optimizing and upgrading the interaction of the collaborative robot based on the analysis results, thereby improving the data processing and analysis effect of the overall dimension of the collaborative robot's interaction and the autonomous dynamic optimization and upgrading effect of the overall dimension.

[0030] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0031] In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0032] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0033] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0034] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A collaborative robot interaction method based on multidimensional data analysis, characterized in that: include: The running trajectory curve corresponding to the collaborative robot when it is running is detected and obtained by the detection sensor using a pre-constructed three-dimensional trajectory coordinate system, and the running trajectory curve is divided and classified and numbered to obtain a number of running trajectory segment curves and their corresponding curve sets; The curve set includes a complete interactive collaborative curve set and a partial interactive collaborative curve set; According to the complete interaction synergy curve set and the partial interaction synergy curve set, data processing of different interaction reaction dimensions and local abnormal data analysis are respectively performed, and the analysis results obtained by the corresponding processing of different interaction reaction dimensions are sorted and combined to obtain the corresponding local interaction reaction reliable processing sequence and local interaction disappearance reliable processing sequence; Among them, the local interactive response reliable processing sequence is obtained by sorting and combining the local interactive response reliable identifiers obtained by all processing and all local complete collaborative abnormalities; The local interaction disappearance reliable processing sequence is obtained by sorting and combining the local interaction disappearance reliabilities obtained from all processing; The interaction reliability processing and analysis of the collaborative robot's interaction is performed in an overall dimension based on the obtained local interaction response reliable processing sequence and the local interaction disappearance reliable processing sequence, and the collaborative robot's interaction is adaptively dynamically optimized and upgraded based on the processing and analysis results; the dynamic optimization and upgrade includes local optimization and upgrade of the collaborative robot's interaction or overall optimization and upgrade of the collaborative robot's interaction.

2. The collaborative robot interaction method based on multidimensional data analysis according to claim 1, characterized in that: The real-time operation coordinates, real-time operation timestamps and real-time operation forces corresponding to the collaborative robot during operation are displayed and connected through a pre-built three-dimensional trajectory coordinate system to obtain an operation trajectory curve; When dividing the running track curve, two adjacent real-time operating forces with values ​​of 0 are obtained, and the running track curve between the two adjacent real-time operating forces with values ​​of 0 is marked as a running track segmented curve; When analyzing and marking the types of different running track segment curves in sequence according to the operation time sequence, the external force detection values ​​and the duration of external force detection corresponding to the different running track segment curves are obtained in sequence, and the external force detection values ​​and the duration of external force detection are analyzed by the running interaction type recognition function and the corresponding running interaction type identification JLj is output; The running interaction type identifier includes a value of 0 or 1, indicating that the corresponding running interaction type is a complete interaction collaboration type or a partial interaction collaboration type.

3. The collaborative robot interaction method based on multidimensional data analysis according to claim 2, characterized in that: The expression for running the interaction type identification function is; ; In the formula, j is the segmented curve of different running trajectories, j=1, 2, 3, ..., m; m is a positive integer; WJj and TWj are the total external force detection values ​​and the total duration of external force continuous detection corresponding to different running trajectory segmented curves; WTj and TTj are the total operating force values ​​and the total duration of operating force of the collaborative robot corresponding to different running trajectory segmented curves.

4. The collaborative robot interaction method based on multidimensional data analysis according to claim 2, characterized in that: According to the operation interaction type identifier with a value of 0, the corresponding motion trajectory segment curve is marked as a complete collaborative segment curve; According to the operation interaction type identifier with a value of 1, the corresponding motion trajectory segment curve is marked as a partial cooperative segment curve; According to the operation interaction type identifier with a value of 0, all the corresponding complete collaborative segmented curves are sorted and combined to obtain the corresponding complete interaction collaborative curve set; and according to the operation interaction type identifier with a value of 1, all the corresponding partial collaborative segmented curves are sorted and combined to obtain the corresponding partial interaction collaborative curve set.

5. The collaborative robot interaction method based on multidimensional data analysis according to claim 4, characterized in that: According to all the complete collaborative segmented curves in the complete interactive collaborative curve set, local reliability analysis of the local complete interactive reaction dimension is carried out, and the external force detection value detection timestamp, collaborative robot reaction timestamp, external force detection instantaneous value, and operation reaction instantaneous value corresponding to different complete collaborative segmented curves in the complete interactive collaborative curve set are obtained in turn and sorted and combined to obtain the local passive interactive monitoring sequence corresponding to different complete collaborative segmented curves; When the local interaction reaction corresponding to the complete collaborative segmented curve is reliably processed according to the local passive interaction monitoring sequence, all the data in the local passive interaction monitoring sequence are analyzed by the local interaction reaction identification function, and the corresponding local interaction reaction reliable identification FKi is output; The local interaction reaction reliability flag includes a value of 0 or 1, indicating that the local interaction reaction reliability state corresponding to the complete synergy segmented curve is normal or abnormal.

6. The collaborative robot interaction method based on multidimensional data analysis according to claim 5, characterized in that: The expression of the local interaction reaction identification function is: ; In the formula, i is a different complete collaborative segmented curve in the passive drag anomaly analysis set, i=1, 2, 3, ..., n; n is a positive integer; TFi and LFi are the collaborative interaction reaction time difference value and collaborative interaction reaction implementation force difference value corresponding to different complete collaborative segmented curves respectively; TFi0 and LFi0 are the collaborative interaction reaction time difference standard value and collaborative interaction reaction implementation force difference standard value respectively.

7. The collaborative robot interaction method based on multidimensional data analysis according to claim 5, characterized in that: According to the local reaction reliable flag with a value of 1, the local passive interaction monitoring sequence is passed through the formula Calculate and obtain the local complete collaborative anomaly degree JWi corresponding to the complete collaborative segmented curve; where a and b are different calculation weight coefficients, and 0<a≤b<1; The reliable identifications of all local interaction reactions obtained by processing and all local complete collaborative abnormalities are sorted and combined to obtain a reliable processing sequence of local interaction reactions.

8. The collaborative robot interaction method based on multidimensional data analysis according to claim 7, characterized in that: According to all partial cooperative segmented curves in the partial interactive cooperative curve set, the local reliability analysis of the local partial interactive reaction dimension is carried out, and the drag disappearance timestamp t and the first monitoring coordinate corresponding to the target monitoring point of the cooperative robot when the real-time drag force is 0 in different partial cooperative segmented curves are obtained in turn. , and obtain the second monitoring coordinates corresponding to the target monitoring point of the collaborative robot after t0 seconds , and by the formula Calculate and obtain the local interaction disappearance reliability XKk corresponding to the real-time drag force of different parts of the collaborative segmented curve being 0; where k is the target monitoring point of the collaborative robot when the real-time drag force of different parts of the collaborative segmented curve is 0, k=1, 2, 3, ..., p; p is a positive integer; The local interaction disappearance reliability contains a value of 0 or non-0, indicating whether the local interaction disappearance reliability state corresponding to the corresponding part of the collaborative segment curve is normal or abnormal; The local interaction disappearance reliabilities obtained from all processes are sorted and combined to obtain a local interaction disappearance reliable processing sequence.

9. The collaborative robot interaction method based on multidimensional data analysis according to claim 8, characterized in that: The local interaction response reliable processing sequence and the local interaction disappearance reliable processing sequence are traversed respectively, and the elements with non-zero values ​​are counted and analyzed; If there are non-zero elements in the reliable processing sequence of local interaction reaction and / or the reliable processing sequence of local interaction disappearance, then the formula Calculate and obtain the interaction anomaly integration degree JZ of collaborative robot interaction; where β is the local dimension influence weight, and 1<β; JWi´, XYk´ are the local complete collaborative anomaly degree and local interaction disappearance reliability corresponding to all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; i´, k´ are all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; n´, p´ are the total number of all non-zero elements in the local interaction reaction reliable processing sequence and the local interaction disappearance reliable processing sequence, respectively; n´<n, p´<p; Conduct data analysis on the integration degree of interaction anomalies and adaptively optimize and upgrade the interaction of collaborative robots dynamically.

10. The collaborative robot interaction method based on multidimensional data analysis according to claim 9, characterized in that: If the interaction abnormality integration degree is less than or equal to the interaction abnormality integration threshold, a prompt is given to perform local optimization and upgrade of the collaborative robot interaction; If the interaction anomaly integration degree is greater than the interaction anomaly integration threshold, it is prompted to optimize and upgrade the overall interaction of the collaborative robot.

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