Comprehensive efficiency statistical system for industrial robot and industrial equipment

Abstract

The invention provides a comprehensive efficiency statistical system for an industrial robot and industrial equipment. Sensing data and production data are collected; the overall equipment efficiency of the industrial robot and the industrial equipment is obtained through calculation based on sensing data and production data, three evaluation indexes including availability, performance efficiency and production quality are included, production efficiency loss points are obtained through analysis based on the evaluation indexes in combination with the production processes of the industrial robot and the industrial equipment, and the overall equipment efficiency of the industrial robot and the industrial equipment is obtained based on the production efficiency loss points. By combining historical data and determining potential efficiency influence factors of the industrial robot and the industrial equipment, the operation state of the equipment can be comprehensively known, the root of production efficiency loss can be found in time, corresponding measures are taken to improve the production efficiency and optimize resource allocation, industrial modernization is realized, and the production efficiency and quality control are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment efficiency analysis, and in particular to a comprehensive efficiency statistics system for industrial robots and industrial equipment. Background Art

[0002] With the development of science and technology, automated factories are becoming more and more widely used. Industrial robots and industrial equipment, as production tools of automated factories, have brought extraordinary significance to the production efficiency of factories.

[0003] The significance of the comprehensive efficiency statistics of industrial equipment lies in measuring the ratio of the actual production capacity of the equipment to its theoretical production capacity. As an independent measurement tool, it can reflect the actual production capacity of the equipment and is an important indicator for evaluating equipment performance.

[0004] Overall Equipment Effectiveness (OEE) is not only an index to measure the effective operation of production facilities, but its calculation results are universal and can even be compared across industries. Overall equipment efficiency can also be used as a key performance indicator and efficiency indicator for lean production. Through OEE statistics, the actual value of OEE can be fully demonstrated, while improving production efficiency, reducing visible and invisible losses, and achieving cost control and quality control. For example, through predictive equipment maintenance and operation, the factory's equipment production efficiency will increase by more than 50%. This ensures that every piece of production equipment and every production link operates in the best state within its scope.

[0005] In addition, with the large-scale promotion and popularization of the Industrial Internet of Things, the rapid realization of the value of equipment OEE will depend entirely on whether the enterprise has implemented the equipment Internet of Things and connected all factory equipment with information.

[0006] To sum up, the comprehensive efficiency statistics of industrial equipment is not only an important tool to measure equipment performance, but also a key means to achieve industrial modernization, improve production efficiency and quality control. Therefore, the comprehensive efficiency statistics of industrial robots and industrial equipment are particularly important. Summary of the invention

[0007] The present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, so as to solve the problems raised in the background technology.

[0008] A comprehensive efficiency statistics system for industrial robots and industrial equipment, including:

[0009] Data acquisition module, used to connect with sensors and controllers of industrial robots and industrial equipment to collect sensor data and production data;

[0010] The indicator evaluation module is used to calculate the overall equipment efficiency of industrial robots and industrial equipment based on sensor data and production data, including three evaluation indicators: availability, performance efficiency and production quality;

[0011] A loss analysis module, used to analyze and obtain production efficiency loss points based on the evaluation indicators and in combination with the production processes of industrial robots and industrial equipment;

[0012] The factor determination module is used to determine the potential efficiency influencing factors of industrial robots and industrial equipment based on production efficiency loss points combined with historical data.

[0013] Preferably, the data acquisition module comprises:

[0014] A parameter determination unit, used to determine data acquisition parameters for sensors based on sensors of industrial robots and industrial equipment, and to determine data acquisition parameters for controllers based on sensors of industrial robots and industrial equipment;

[0015] An instruction generation unit, used to generate an acquisition instruction for the sensor according to the data acquisition parameters of the sensor and the acquisition requirements, and to generate an acquisition instruction for the controller according to the data acquisition parameters of the controller and the acquisition requirements;

[0016] The data acquisition unit is used to collect data from the sensor and the controller based on the collection instructions for the sensor and the collection instructions for the controller to obtain sensor data and production data.

[0017] Preferably, the indicator evaluation module includes:

[0018] A standardization unit is used to determine the data standard features of the sensor data and the production data based on the calculation algorithm of the three evaluation indicators of availability, performance efficiency and production quality, and to standardize the sensor data and the production data based on the data standard features to obtain standard data;

[0019] The calculation unit is used to obtain the availability, performance efficiency and production quality of industrial robots and industrial equipment based on standard data and combined with calculation algorithms.

[0020] Preferably, the computing unit comprises:

[0021] A processing unit, for processing the standard data in chronological order according to the data attributes based on the calculation algorithm to obtain a target standard value;

[0022] The calculation unit is used to calculate the target standard value according to the calculation algorithm to obtain the availability, performance efficiency and production quality of the industrial robot and industrial equipment.

[0023] Preferably, the loss analysis module includes:

[0024] A comparison unit is used to compare the availability, performance efficiency and production quality with the preset evaluation criteria to obtain the availability difference, performance efficiency difference and production quality difference;

[0025] A process determination unit, for determining local production processes corresponding to availability, performance efficiency and production quality from the production processes of industrial robots and industrial equipment;

[0026] A suspicious determination unit, configured to determine a first suspicious point based on availability difference in combination with a local production process, determine a second suspicious point based on performance efficiency difference in combination with the local production process, and determine a third suspicious point based on production quality difference in combination with the local production process;

[0027] A weight determination unit, configured to obtain a common suspicious point from the first suspicious point, the second suspicious point, and the third suspicious point, and determine an availability impact weight, a performance efficiency impact weight, and a production quality impact weight based on the difference in availability, performance efficiency, and production quality;

[0028] An adjustment unit is used to determine the loss type of the common suspicious point, and to perform weighted adjustment on the analysis and identification model based on the availability impact weight, the performance efficiency impact weight, and the production quality impact weight to obtain a target analysis and identification model;

[0029] The selection unit is used to input the evaluation index data corresponding to the common suspicious points into the target analysis and recognition model for verification, obtain the recognition results of the common suspicious points, and select the production efficiency loss points from the common suspicious points based on the recognition results.

[0030] Preferably, the adjustment unit comprises:

[0031] A parameter acquisition unit, used to select an analysis and identification model corresponding to the loss type from the data model library, and obtain model parameters related to availability, performance efficiency and production quality in the analysis and identification model;

[0032] The weighted adjustment unit is used to perform weighted adjustment on the model parameters based on the availability impact weight, the performance efficiency impact weight and the production quality impact weight to obtain a target analysis and identification model.

[0033] Preferably, the selection unit includes:

[0034] A judging unit, configured to determine an efficiency loss value for a common suspicious point in the recognition result, and to judge whether the efficiency loss value is greater than a preset loss value;

[0035] If so, determining the common suspicious point as a production efficiency loss point;

[0036] Otherwise, it is determined that the common suspicious point is not a production efficiency loss point.

[0037] Preferably, the factor determination module includes:

[0038] a correlation acquisition unit, configured to acquire first production operation data having a first correlation with the production efficiency loss point, acquire second production operation data having a second correlation with the production efficiency loss point, and acquire first historical data related to the first production operation data from historical data, and acquire second historical data related to the second production operation data;

[0039] a matching analysis unit, configured to divide the first historical data in chronological order to obtain an operation data sequence, obtain a change trend of the operation data sequence, match the change trend with an efficiency loss feature of a production efficiency loss point, and determine whether the change trend exceeds the efficiency loss feature according to a matching result, and if so, assign a first weight to the change trend, otherwise, assign a second weight to the change trend;

[0040] a weighted processing unit, configured to perform weighted processing on the first production operation data based on a weight of a change trend to obtain first target production operation data, and based on an operation association between the first production operation data and the second production operation data, process the second production operation data according to the first target production operation data to obtain second target production data;

[0041] A factor determination unit is used to obtain a target change trend of the second historical data, and determine the influence of the second target production data on the trend direction of the target change trend; based on the data characteristics of the second target production data, determine the potential efficiency influencing factors of industrial robots and industrial equipment; based on the target change trend, determine the potential occurrence probability; based on the influence on the trend direction of the target change trend, verify and adjust the potential occurrence probability to obtain the target potential occurrence probability.

[0042] Preferably, the weighted processing unit processes the second production operation data according to the first target production operation data based on the operation association between the first production operation data and the second production operation data to obtain the second target production data, including:

[0043] Based on the operation association, a data expansion rule is determined, and according to the data expansion rule, the second production operation data is expanded based on the first target production operation data to obtain the second target production data.

[0044] Preferably, in the factor determination unit, based on the influence on the trend of the target change trend, the potential probability of occurrence is verified and adjusted to obtain the target potential probability of occurrence, including:

[0045] Determine whether the impact situation is within the predicted trend of the target change trend;

[0046] If so, determine the potential probability of occurrence as the target potential probability of occurrence;

[0047] Otherwise, the trend difference between the predicted trend and the target change trend is determined, and based on the trend difference, an adjustment value for the potential occurrence probability is determined to obtain the target potential occurrence probability.

[0048] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0049] By collecting sensor data and production data; based on the sensor data and production data, the overall equipment efficiency of industrial robots and industrial equipment is calculated, including three evaluation indicators: availability, performance efficiency and production quality; based on the evaluation indicators, combined with the production process of industrial robots and industrial equipment, the production efficiency loss points are analyzed; based on the production efficiency loss points, combined with historical data, the potential efficiency influencing factors of industrial robots and industrial equipment are determined, so as to fully understand the equipment operation status, timely discover the root cause of production efficiency loss, take corresponding measures to improve production efficiency and optimize resource allocation, realize industrial modernization, improve production efficiency and quality control.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 This is a structural diagram of a comprehensive efficiency statistics system for industrial robots and industrial equipment in an embodiment of the present invention;

[0054] Figure 2 is a structural diagram of the data acquisition module described in an embodiment of the present invention;

[0055] Figure 3 It is a structural diagram of the indicator evaluation module described in the embodiment of the present invention. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0057] Embodiment 1:

[0058] The embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, such as Figure 1 As shown, including:

[0059] Data acquisition module, used to connect with sensors and controllers of industrial robots and industrial equipment to collect sensor data and production data;

[0060] The indicator evaluation module is used to calculate the overall equipment efficiency of industrial robots and industrial equipment based on sensor data and production data, including three evaluation indicators: availability, performance efficiency and production quality;

[0061] A loss analysis module, used to analyze and obtain production efficiency loss points based on the evaluation indicators and in combination with the production processes of industrial robots and industrial equipment;

[0062] The factor determination module is used to determine the potential efficiency influencing factors of industrial robots and industrial equipment based on production efficiency loss points combined with historical data.

[0063] In this embodiment, the sensor data and production data include operation and downtime, cycle time, quantity output, etc.

[0064] In this embodiment, the production efficiency loss points include, for example, equipment failure, operating error, slow production rhythm, etc.

[0065] In this embodiment, the potential efficiency influencing factors are factors that may become production efficiency loss points.

[0066] The beneficial effects of the above design scheme are: by collecting sensor data and production data; based on the sensor data and production data, the overall equipment efficiency of industrial robots and industrial equipment is calculated, including three evaluation indicators: availability, performance efficiency and production quality; based on the evaluation indicators, combined with the production process of industrial robots and industrial equipment, the production efficiency loss points are analyzed; based on the production efficiency loss points, combined with historical data, the potential efficiency influencing factors of industrial robots and industrial equipment are determined, which can fully understand the equipment operation status, timely discover the root cause of production efficiency loss, take corresponding measures to improve production efficiency and optimize resource allocation, realize industrial modernization, improve production efficiency and quality control.

[0067] Embodiment 2:

[0068] Based on Example 1, the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, such as Figure 2 As shown, the data acquisition module includes:

[0069] A parameter determination unit, used to determine data acquisition parameters for sensors based on sensors of industrial robots and industrial equipment, and to determine data acquisition parameters for controllers based on sensors of industrial robots and industrial equipment;

[0070] An instruction generation unit, used to generate an acquisition instruction for the sensor according to the data acquisition parameters of the sensor and the acquisition requirements, and to generate an acquisition instruction for the controller according to the data acquisition parameters of the controller and the acquisition requirements;

[0071] The data acquisition unit is used to collect data from the sensor and the controller based on the collection instructions for the sensor and the collection instructions for the controller to obtain sensor data and production data.

[0072] In this embodiment, the data collection parameters include collection frequency, collection time point, etc.

[0073] In this embodiment, the collection requirements include data accuracy and the like.

[0074] The beneficial effects of the above design scheme are: by generating collection instructions for sensors according to the data collection parameters of sensors and the collection requirements, generating collection instructions for controllers according to the data collection parameters of controllers and the collection requirements, based on the collection instructions for sensors and the collection instructions for controllers, data collection is performed on sensors and controllers to obtain sensor data and production data, providing a data basis for comprehensive efficiency statistics.

[0075] Embodiment 3:

[0076] Based on Example 1, the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, such as Figure 3 As shown, the indicator evaluation module includes:

[0077] A standardization unit is used to determine the data standard features of the sensor data and the production data based on the calculation algorithm of the three evaluation indicators of availability, performance efficiency and production quality, and to standardize the sensor data and the production data based on the data standard features to obtain standard data;

[0078] The calculation unit is used to obtain the availability, performance efficiency and production quality of industrial robots and industrial equipment based on standard data and combined with calculation algorithms.

[0079] In this embodiment, the calculation algorithms of the three evaluation indicators of availability, performance efficiency and production quality are preset according to historical data.

[0080] The beneficial effects of the above design scheme are: through the calculation algorithm based on the three evaluation indicators of availability, performance efficiency and production quality, the data standard characteristics of the sensor data and production data are determined, and the sensor data and production data are standardized based on the data standard characteristics to obtain standard data. From the aspect of data quality, the calculation efficiency and accuracy are improved, providing a high-quality data foundation for the calculation of indicators.

[0081] Embodiment 4:

[0082] Based on Example 3, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, wherein the calculation unit includes:

[0083] A processing unit, for processing the standard data in chronological order according to the data attributes based on the calculation algorithm to obtain a target standard value;

[0084] The calculation unit is used to calculate the target standard value according to the calculation algorithm to obtain the availability, performance efficiency and production quality of the industrial robot and industrial equipment.

[0085] In this embodiment, the target standard value is, for example, an average value, a median value, etc. in a time series.

[0086] The beneficial effect of the above design scheme is: by processing the standard data in chronological order according to the data attributes based on the calculation algorithm, the target standard value is obtained, and the target standard value is calculated according to the calculation algorithm to obtain the availability, performance efficiency and production quality of industrial robots and industrial equipment, thereby ensuring the correctness of the data in the indicator process and thus ensuring the correctness of the indicator.

[0087] Embodiment 5:

[0088] Based on Example 1, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, wherein the loss analysis module includes:

[0089] A comparison unit is used to compare the availability, performance efficiency and production quality with the preset evaluation criteria to obtain the availability difference, performance efficiency difference and production quality difference;

[0090] A process determination unit, for determining local production processes corresponding to availability, performance efficiency and production quality from the production processes of industrial robots and industrial equipment;

[0091] A suspicious determination unit, configured to determine a first suspicious point based on availability difference in combination with a local production process, determine a second suspicious point based on performance efficiency difference in combination with the local production process, and determine a third suspicious point based on production quality difference in combination with the local production process;

[0092] A weight determination unit, configured to obtain a common suspicious point from the first suspicious point, the second suspicious point, and the third suspicious point, and determine an availability impact weight, a performance efficiency impact weight, and a production quality impact weight based on the difference in availability, performance efficiency, and production quality;

[0093] An adjustment unit is used to determine the loss type of the common suspicious point, and to perform weighted adjustment on the analysis and identification model based on the availability impact weight, the performance efficiency impact weight, and the production quality impact weight to obtain a target analysis and identification model;

[0094] The selection unit is used to input the evaluation index data corresponding to the common suspicious points into the target analysis and recognition model for verification, obtain the recognition results of the common suspicious points, and select the production efficiency loss points from the common suspicious points based on the recognition results.

[0095] In this embodiment, the generation process includes operation data, production process data and production result data.

[0096] In this embodiment, the common suspicious point may be a loss efficiency point and needs further judgment.

[0097] In this embodiment, analyzing and identifying the model for weighted adjustment specifically includes determining the loss function of the model, determining the validation set, the number of model layers, etc.

[0098] The beneficial effects of the above design scheme are: by analyzing based on availability, performance efficiency and production quality, adjusting the model parameters according to the analysis results to ensure the applicability of the obtained model, and finally inputting the evaluation index data corresponding to the common suspicious points into the target analysis and recognition model for verification, obtaining the recognition results of the common suspicious points, and selecting the production efficiency loss points from the common suspicious points based on the recognition results, so as to achieve accurate judgment of the production efficiency loss points.

[0099] Embodiment 6:

[0100] Based on Example 5, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, wherein the adjustment unit includes:

[0101] A parameter acquisition unit, used to select an analysis and identification model corresponding to the loss type from the data model library, and obtain model parameters related to availability, performance efficiency and production quality in the analysis and identification model;

[0102] The weighted adjustment unit is used to perform weighted adjustment on the model parameters based on the availability impact weight, the performance efficiency impact weight and the production quality impact weight to obtain a target analysis and identification model.

[0103] In this embodiment, the data model library includes analysis and identification models corresponding to various loss types, which are pre-trained through machine algorithms and historical data.

[0104] The beneficial effect of the above design scheme is: by analyzing based on availability, performance efficiency and production quality, the model parameters are adjusted according to the analysis results to ensure the applicability of the obtained model.

[0105] Embodiment 7:

[0106] Based on Example 5, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, wherein the selection unit includes:

[0107] A judging unit, configured to determine an efficiency loss value for a common suspicious point in the recognition result, and to judge whether the efficiency loss value is greater than a preset loss value;

[0108] If so, determining the common suspicious point as a production efficiency loss point;

[0109] Otherwise, it is determined that the common suspicious point is not a production efficiency loss point.

[0110] In this embodiment, the greater the efficiency loss, the greater the corresponding efficiency loss value.

[0111] In this embodiment, the preset loss value is preset according to actual conditions.

[0112] The beneficial effect of the above design scheme is: by determining the efficiency loss value of the common suspicious point in the identification result, it is judged whether the efficiency loss value is greater than the preset loss value. If so, the common suspicious point is determined to be a production efficiency loss point; otherwise, it is determined that the common suspicious point is not a production efficiency loss point, thereby realizing the judgment of the production efficiency loss point.

[0113] Embodiment 8:

[0114] Based on Example 1, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, wherein the factor determination module includes:

[0115] a correlation acquisition unit, configured to acquire first production operation data having a first correlation with the production efficiency loss point, acquire second production operation data having a second correlation with the production efficiency loss point, and acquire first historical data related to the first production operation data from historical data, and acquire second historical data related to the second production operation data;

[0116] a matching analysis unit, configured to divide the first historical data in chronological order to obtain an operation data sequence, obtain a change trend of the operation data sequence, match the change trend with an efficiency loss feature of a production efficiency loss point, and determine whether the change trend exceeds the efficiency loss feature according to a matching result, and if so, assign a first weight to the change trend, otherwise, assign a second weight to the change trend;

[0117] a weighted processing unit, configured to perform weighted processing on the first production operation data based on a weight of a change trend to obtain first target production operation data, and based on an operation association between the first production operation data and the second production operation data, process the second production operation data according to the first target production operation data to obtain second target production data;

[0118] A factor determination unit is used to obtain a target change trend of the second historical data, and determine the influence of the second target production data on the trend direction of the target change trend; based on the data characteristics of the second target production data, determine the potential efficiency influencing factors of industrial robots and industrial equipment; based on the target change trend, determine the potential occurrence probability; based on the influence on the trend direction of the target change trend, verify and adjust the potential occurrence probability to obtain the target potential occurrence probability.

[0119] In this embodiment, the first correlation is greater than the second correlation, the first production operation data is used to determine the efficiency loss point, and the second production operation data is used to determine the potential factor.

[0120] In this embodiment, the change trend exceeding the efficiency loss characteristic indicates that the trend is getting worse, and the first weight is greater than the second weight.

[0121] In this embodiment, the first production operation data is weighted based on the weight of the change trend, and the weight of the weighted processing is the first weight or the second weight.

[0122] The beneficial effects of the above design scheme are: by analyzing historical data and effect management operation data, determining potential efficiency influencing factors and potential probability of target occurrence, realizing the early discovery of the root causes of production efficiency loss, taking corresponding measures to improve production efficiency and optimize resource allocation, realizing industrial modernization, improving production efficiency and quality control.

[0123] Embodiment 9:

[0124] Based on Example 8, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment, wherein the weighted processing unit processes the second production operation data according to the first target production operation data based on the operation association between the first production operation data and the second production operation data to obtain the second target production data, including:

[0125] Based on the operation association, a data expansion rule is determined, and according to the data expansion rule, the second production operation data is expanded based on the first target production operation data to obtain the second target production data.

[0126] In this embodiment, the data expansion rule is, for example, to expand the data type and to expand the data amount in a unified data type.

[0127] The beneficial effect of the above design scheme is: by determining data expansion rules based on operation associations, according to the data expansion rules, the second production operation data is expanded based on the first target production operation data to obtain the second target production data, thereby ensuring the richness and comprehensiveness of the obtained second target production data and providing a rich data basis for the determination of potential factors.

[0128] Embodiment 10:

[0129] Based on Example 8, the embodiment of the present invention provides a comprehensive efficiency statistics system for industrial robots and industrial equipment. In the factor determination unit, based on the influence on the trend of the target change trend, the potential occurrence probability is verified and adjusted to obtain the target potential occurrence probability, including:

[0130] Determine whether the impact situation is within the predicted trend of the target change trend;

[0131] If so, determine the potential probability of occurrence as the target potential probability of occurrence;

[0132] Otherwise, the trend difference between the predicted trend and the target change trend is determined, and based on the trend difference, an adjustment value for the potential occurrence probability is determined to obtain the target potential occurrence probability.

[0133] In this embodiment, the predicted trend is obtained based on the target change trend prediction.

[0134] In this embodiment, the greater the trend difference, the greater the corresponding adjustment value.

[0135] The beneficial effect of the above design scheme is: by determining the trend difference between the predicted trend and the target change trend, based on the trend difference, determining the adjustment value of the potential occurrence probability, obtaining the target potential occurrence probability, and ensuring the accuracy of the obtained target potential occurrence probability.

[0136] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of this application document and its equivalent technology, the present invention is also intended to include these changes and variations.

Claims

1. A comprehensive efficiency statistics system for industrial robots and industrial equipment, characterized in that: include: Data acquisition module, used to connect with sensors and controllers of industrial robots and industrial equipment to collect sensor data and production data; The indicator evaluation module is used to calculate the overall equipment efficiency of industrial robots and industrial equipment based on sensor data and production data, including three evaluation indicators: availability, performance efficiency and production quality; A loss analysis module, used to analyze and obtain production efficiency loss points based on the evaluation indicators and in combination with the production processes of industrial robots and industrial equipment; The factor determination module is used to determine the potential efficiency influencing factors of industrial robots and industrial equipment based on production efficiency loss points combined with historical data.

2. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 1, characterized in that: The data acquisition module comprises: A parameter determination unit, used to determine data acquisition parameters for sensors based on sensors of industrial robots and industrial equipment, and to determine data acquisition parameters for controllers based on sensors of industrial robots and industrial equipment; An instruction generation unit, used to generate an acquisition instruction for the sensor according to the data acquisition parameters of the sensor and the acquisition requirements, and to generate an acquisition instruction for the controller according to the data acquisition parameters of the controller and the acquisition requirements; The data acquisition unit is used to collect data from the sensor and the controller based on the collection instructions for the sensor and the collection instructions for the controller to obtain sensor data and production data.

3. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 1, characterized in that: The indicator evaluation module includes: A standardization unit is used to determine the data standard features of the sensor data and the production data based on the calculation algorithm of the three evaluation indicators of availability, performance efficiency and production quality, and to standardize the sensor data and the production data based on the data standard features to obtain standard data; The computing unit is used to obtain the availability, performance efficiency and production quality of industrial robots and industrial equipment based on standard data and combined with computing algorithms.

4. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 3 is characterized in that: The computing unit comprises: A processing unit, for processing the standard data in chronological order according to the data attributes based on the calculation algorithm to obtain a target standard value; The calculation unit is used to calculate the target standard value according to the calculation algorithm to obtain the availability, performance efficiency and production quality of the industrial robot and industrial equipment.

5. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 1, characterized in that: The loss analysis module comprises: A comparison unit is used to compare the availability, performance efficiency and production quality with the preset evaluation criteria to obtain the availability difference, performance efficiency difference and production quality difference; A process determination unit, for determining local production processes corresponding to availability, performance efficiency and production quality from the production processes of industrial robots and industrial equipment; A suspicious determination unit, configured to determine a first suspicious point based on availability difference in combination with a local production process, determine a second suspicious point based on performance efficiency difference in combination with the local production process, and determine a third suspicious point based on production quality difference in combination with the local production process; A weight determination unit, configured to obtain a common suspicious point from the first suspicious point, the second suspicious point, and the third suspicious point, and determine an availability impact weight, a performance efficiency impact weight, and a production quality impact weight based on the difference in availability, performance efficiency, and production quality; An adjustment unit is used to determine the loss type of the common suspicious point, and to perform weighted adjustment on the analysis and identification model based on the availability impact weight, the performance efficiency impact weight, and the production quality impact weight to obtain a target analysis and identification model; The selection unit is used to input the evaluation index data corresponding to the common suspicious points into the target analysis and recognition model for verification, obtain the recognition results of the common suspicious points, and select the production efficiency loss points from the common suspicious points based on the recognition results.

6. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 5, characterized in that: The adjustment unit comprises: A parameter acquisition unit, used to select an analysis and identification model corresponding to the loss type from the data model library, and obtain model parameters related to availability, performance efficiency and production quality in the analysis and identification model; The weighted adjustment unit is used to perform weighted adjustment on the model parameters based on the availability impact weight, the performance efficiency impact weight and the production quality impact weight to obtain a target analysis and identification model.

7. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 5, characterized in that: The selection unit comprises: A judging unit, configured to determine an efficiency loss value for a common suspicious point in the recognition result, and to judge whether the efficiency loss value is greater than a preset loss value; If so, determining the common suspicious point as a production efficiency loss point; Otherwise, it is determined that the common suspicious point is not a production efficiency loss point.

8. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 1, characterized in that: The factor determination module comprises: a correlation acquisition unit, configured to acquire first production operation data having a first correlation with the production efficiency loss point, acquire second production operation data having a second correlation with the production efficiency loss point, and acquire first historical data related to the first production operation data from historical data, and acquire second historical data related to the second production operation data; a matching analysis unit, configured to divide the first historical data in chronological order to obtain an operation data sequence, obtain a change trend of the operation data sequence, match the change trend with an efficiency loss feature of a production efficiency loss point, and determine whether the change trend exceeds the efficiency loss feature according to a matching result, and if so, assign a first weight to the change trend, otherwise, assign a second weight to the change trend; a weighted processing unit, configured to perform weighted processing on the first production operation data based on a weight of a change trend to obtain first target production operation data, and based on an operation association between the first production operation data and the second production operation data, process the second production operation data according to the first target production operation data to obtain second target production data; A factor determination unit is used to obtain a target change trend of the second historical data, and determine the influence of the second target production data on the trend direction of the target change trend; based on the data characteristics of the second target production data, determine the potential efficiency influencing factors of industrial robots and industrial equipment; based on the target change trend, determine the potential occurrence probability; based on the influence on the trend direction of the target change trend, verify and adjust the potential occurrence probability to obtain the target potential occurrence probability.

9. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 8, characterized in that: The weighted processing unit processes the second production operation data according to the first target production operation data based on the operation association between the first production operation data and the second production operation data to obtain the second target production data, including: Based on the operation association, a data expansion rule is determined, and according to the data expansion rule, the second production operation data is expanded based on the first target production operation data to obtain the second target production data.

10. The comprehensive efficiency statistics system for industrial robots and industrial equipment according to claim 8, characterized in that: In the factor determination unit, based on the influence on the trend of the target change trend, the potential probability of occurrence is verified and adjusted to obtain the target potential probability of occurrence, including: Determine whether the impact situation is within the predicted trend of the target change trend; If so, determine the potential probability of occurrence as the target potential probability of occurrence; Otherwise, the trend difference between the predicted trend and the target change trend is determined, and based on the trend difference, an adjustment value for the potential occurrence probability is determined to obtain the target potential occurrence probability.

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