Production data intelligent analysis system based on AI intelligence

Abstract

The invention discloses a production data intelligent analysis system based on AI intelligence, and belongs to the technical field of production data processing. The method is used for solving the technical problems that in an existing scheme, different types of production data are poor in autonomous supervision analysis effect at different levels in the aspect of interaction and poor in autonomous analysis optimization prompt effect. Supervision and data statistics are carried out on single interaction of different types of production data in the production process of an enterprise, and digital processing and analysis of a single interaction state are carried out on supervision statistical data of the different types of production data; according to all single interaction supervision processing data obtained by interaction supervision processing of different types of production data, local interaction state supervision processing and multi-dimensional data analysis between different types are carried out, and according to an analysis result, dynamic classification marking and interaction optimization analysis are carried out on interaction between every two different types. And according to an analysis result, an internal optimization prompt or an internal and external collaborative optimization prompt of the interaction resource is generated adaptively.

Description

Technical Field

[0001] The present invention relates to the technical field of production data processing, and in particular to an AI-based intelligent production data analysis system. Background Art

[0002] Digital workstation production data refers to various production-related information collected from digital workstations (i.e., workstations equipped with computers, sensors and other automation technologies) in manufacturing or similar industries.

[0003] When existing production data analysis solutions are implemented, most of them still remain at the monitoring, statistics and alarm prompts of digital workstation production data. They are unable to conduct multi-level supervision and evaluation of the interaction status of different types of production data, and adaptively perform dynamic interaction optimization analysis and prompts on the interaction of different types of production data based on the supervision and evaluation results. This results in poor autonomous supervision and analysis effects at different levels of interaction of different types of production data, as well as poor autonomous analysis optimization prompt effects. Summary of the invention

[0004] The purpose of the present invention is to provide an AI-based intelligent production data analysis system to solve the technical problems in existing solutions of poor autonomous supervision and analysis of different types of production data at different levels in terms of interaction and poor autonomous analysis optimization prompts.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An AI-based intelligent production data analysis system, including a production data single interaction supervision module, a production data local interaction supervision module, and a production data interaction optimization analysis module; The production data single interaction supervision module is used to supervise and collect data statistics on the single interactions of different types of production data in the enterprise's production process, and to digitally process and analyze the single interaction status of the supervision statistical data of different types of production data to obtain the single interaction supervision processing data corresponding to different types of production data; The local interaction supervision module of production data is used to perform local interaction status supervision processing and multi-dimensional data analysis between different types of production data according to all single interaction supervision processing data obtained from the interaction supervision processing of different types of production data, and dynamically classify and mark the interactions between different types according to the analysis results; the interaction classification marks between different types include normal type marks, first abnormal type marks, second abnormal type marks and third abnormal type marks; The production data interaction optimization analysis module is used to perform interaction optimization analysis on the corresponding type of production data according to the classification and labeling results of interactions between different types, and to adaptively generate internal optimization prompts for interactive resources or internal and external collaborative optimization prompts according to the analysis results; Wherein, the corresponding total amount of remaining local interactive resources is obtained according to all first abnormal types of classification marks, and the corresponding total amount of missing first local interactive resources and total amount of missing second local interactive resources are obtained according to all second abnormal types and all third abnormal types of classification marks respectively; The remaining total amount of local interactive resources is matched and analyzed with the total amount of missing first local interactive resources and the total amount of missing second local interactive resources, and an internal optimization prompt or an internal and external collaborative optimization prompt of interactive resources is adaptively generated according to the analysis results.

[0006] Preferably, when the supervision process of the interaction of different types of production data is performed, the corresponding type supervision cycle is obtained according to the type of different production data, and the local interaction period from the start to the end of the interaction between the production data of the target type and the production data of other types is obtained within the type supervision period, and the maximum unit interaction data volume of the interaction process between the production data of the target type and the same type within the local interaction period is obtained; The maximum unit interaction data volume obtained is calculated by the formula Calculate the single data interaction value DJ corresponding to the interaction process between the target type's production data and the same type in the local interaction period; where JL is the maximum unit interaction data volume obtained; JB is the unit standard interaction data volume corresponding to the target type's production data and the interaction type; JX is the unit standard interaction redundancy rate corresponding to the target type's production data; a is the total number of historical active interactions for active production data interaction; b is the total number of historical active interactions for passive production data interaction.

[0007] Preferably, if the single data interaction value is less than or equal to 1, a single data interaction is generated to be normal, and the total number of single interactions between the two types of production data interaction is increased by one; Otherwise, a single data interaction exception is generated, and the total number of single interaction exceptions between the two types of production data interaction is increased by one; The single data interaction values, the total number of normal single interactions and the total number of abnormal single interactions obtained through the supervision and processing of type production data and the same type production data within the local interaction period are sorted and combined to obtain the single interaction supervision and processing data corresponding to the interaction between two types.

[0008] Preferably, when supervising the local interaction status between different types of production data interaction, the total number of normal single interactions and the total number of abnormal single interactions between two types are obtained in turn, and the formula Calculate and obtain the first local interaction state value JJ1 of the interaction between two types; where NZ and NY are the total number of normal single interactions and the total number of abnormal single interactions between two types, respectively; BJ is the standard value of the local interaction state between two types; β is the error influence coefficient of the interaction between two types; And, through the formula The second local interaction state value JJ2 of the interaction between two types is calculated; where LZ is the total interaction flow of all interaction between two types; LB is the standard interaction total flow of interaction between two types.

[0009] Preferably, the step of obtaining the error influence coefficients for interaction between two types includes: Get the total number of preset standard interactions between two types, and add the total number of normal single interactions and the total number of abnormal single interactions between two types to the total number of preset standard interactions through the formula Calculate and obtain the corresponding error influence coefficient β; where NB is the total number of single interaction standards between two types of interactions; k1 and k2 are different proportional coefficients, and 0<k1<k2<1.

[0010] Preferably, when determining the local interaction state of interaction between two types according to the first local interaction state value and the second local interaction state value, the first local interaction state value and the second local interaction state value are subjected to data analysis through a local interaction recognition model, and the local interaction state evaluation value JP corresponding to the two types is output; the expression of the local interaction recognition model is: .

[0011] Preferably, a normal prompt of the local interaction state between the two types is generated according to the local interaction state evaluation value with a value of 0; According to the local interaction state evaluation value of 1, a local interaction state slight abnormality prompt between two types is generated, and at the same time, the two types are marked as the second abnormal type; According to the local interaction state evaluation value of 2, a severe abnormal prompt of the local interaction state between the two types is generated, and the two types are marked as the third abnormal type.

[0012] Preferably, when tracing back and classifying the local interaction resource surplus corresponding to the normal local interaction status between two types, all the interaction data between two types with normal local interaction status are classified by formula Calculate and obtain the corresponding normal type value ZL; where, It is the maximum value of all maximum unit interaction data volumes between two types of interactions; The minimum interactive redundancy rate per unit corresponding to the target type production data; If the normal type value is 0, a local interaction resource remaining exception between the two types is generated, and the two types are marked as the first exception type; If the normal type value is -1, the remaining local interaction resources between the two types are generated to be normal, and the two types are marked as normal types.

[0013] Preferably, the remaining amounts of local interaction resources corresponding to different first exception types are counted, and the remaining amounts of local interaction resources corresponding to all first exception types are summed to obtain the total remaining amount of local interaction resources; and, counting the first local interaction resource shortage amounts corresponding to different second abnormality types and the second local interaction resource shortage amounts corresponding to different third abnormality types; All first local interaction resource shortage amounts and all local interaction resource shortage amounts are summed up respectively to obtain the first local interaction resource shortage total amount and the second local interaction resource shortage total amount.

[0014] Preferably, if the remaining total amount of local interactive resources simultaneously meets the requirements of the first total amount of local interactive resources missing and the second total amount of local interactive resources missing, an interactive resource internal optimization prompt is generated; If the remaining total amount of local interactive resources cannot simultaneously meet the needs of the first local interactive resource shortage total amount and the second local interactive resource shortage total amount, an interactive resource internal and external collaborative optimization prompt is generated.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention supervises and counts single interactions of different types of production data in the production process of an enterprise, and digitally processes and analyzes the single interaction status of the supervision statistical data of different types of production data. This can not only realize the processing of single interaction data of different types of production data and the digital representation of single data interaction status, but also provide reliable single interaction supervision processing data support for subsequent supervision and analysis of local interaction status corresponding to different types of production data.

[0016] The present invention performs local interaction status supervision processing and multi-dimensional data analysis between different types of production data according to all single interaction supervision processing data obtained through interaction supervision processing, and dynamically classifies and labels the interactions between different pairwise types based on the analysis results, thereby realizing the expanded utilization of early single interaction supervision processing data between different pairwise types. At the same time, multi-level processing and digital representation of local interaction status between different pairwise types are performed, thereby improving the autonomous supervision and analysis effects of different types of production data at different levels in terms of interaction.

[0017] The present invention performs interaction optimization analysis on production data of corresponding types according to classification labeling results of interactions between different pairwise types, and adaptively generates internal optimization prompts or internal and external collaborative optimization prompts for interaction resources according to the analysis results, thereby evaluating the usage of interaction resources between different pairwise types from different dimensions, and performing dynamic data resource optimization management prompts internally and externally according to the evaluation results, thereby improving the autonomous analysis optimization prompt effects of different types of production data in terms of interaction. 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 module block diagram of an AI-based intelligent production data analysis system of the present invention.

[0020] Figure 2 This is a flowchart of the steps of operating an AI-based intelligent production data analysis system of the present invention.

[0021] Figure 3 It is a flowchart of the present invention for analyzing data of different pairwise types and dynamically classifying and marking them.

[0022] Figure 4 It is a flowchart of performing data analysis on the remaining total amount of local interactive resources in the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] like Figure 1 to Figure 2 As shown, the present invention is an AI-based intelligent production data intelligent analysis system, including a production data single interaction supervision module, a production data local interaction supervision module and a production data interaction optimization analysis module; The production data single interaction supervision module is used to supervise and collect data statistics on the single interactions of different types of production data in the production process of the enterprise, and to digitally process and analyze the single interaction status of the supervision statistical data of different types of production data to obtain the single interaction supervision processing data corresponding to different types of production data; including: It should be noted that different types of production data can be divided according to the department of production data, or according to the equipment of production data, or according to the actual application rules of actual application scenarios; When conducting supervision and processing on the interaction of different types of production data, the corresponding type supervision cycle is obtained according to the type of different production data. Different types of production data correspond to a different type of supervision cycle. The unit of the type supervision cycle is seconds. The specific value can be determined based on the preliminary test data of the enterprise's production data to achieve differentiated supervision and processing of different types of production data. The interaction of different types of production data refers to the exchange, sharing and integration between different types of production data, and the local interaction period from the start to the end of the interaction between the target type of production data and other types of production data is obtained within the type supervision period. The target type refers to the type that actively initiates the interaction application, and the maximum unit interaction data volume of the target type of production data and the same type of interaction process within the local interaction period is obtained; The maximum unit interaction data volume obtained is calculated by the formula Calculate the single data interaction value DJ corresponding to the interaction process between the target type's production data and the same type in the local interaction period; where JL is the maximum unit interaction data volume obtained; JB is the unit standard interaction data volume corresponding to the target type's production data and the interaction type, which can be determined based on the previous test data or operation design data before the target type and the interaction type; JX is the unit standard interaction redundancy rate corresponding to the target type's production data, which can also be determined based on the previous test data or operation design data before the target type and the interaction type; a is the total number of historical active interactions for active production data interaction; b is the total number of historical active interactions for passive production data interaction; It should be explained that the single data interaction value is used to process and calculate the interaction data between the target type and the same type in the local interaction period to digitally represent the single data interaction status between the two types; If the single data interaction value is less than or equal to 1, a single data interaction is generated to be normal, and the total number of single interactions between the two types of production data interaction is increased by one; Otherwise, a single data interaction exception is generated, and the total number of single interaction exceptions between the two types of production data interaction is increased by one; The single data interaction value, the total number of normal single interactions and the total number of abnormal single interactions obtained by the supervision and processing of the type production data and the same type production data in the local interaction period are sorted and combined to obtain the single interaction supervision and processing data corresponding to the interaction between the two types; It is understandable that when the existing digital enterprise production data is supervised and processed, different preset requirements are implemented for the interaction of different types of data generated by the enterprise production data in the early stage according to the nature, purpose, security and real-time factors involved in the production data. However, the preset requirements are basically determined according to the operation requirements of the enterprise, or the historical requirements for setting business data. Different types of data will have different problems in the actual interaction process, but the existing technical solutions do not implement targeted supervision and processing analysis; In the embodiment of the present invention, by supervising and counting single interactions of different types of production data in the production process of an enterprise, and digitally processing and analyzing the single interaction status of the supervision statistical data of different types of production data, it is possible to process the single interaction data of different types of production data and digitally represent the single data interaction status, and provide reliable single interaction supervision processing data support for the subsequent supervision and analysis of local interaction status corresponding to different types of production data.

[0025] The local interaction supervision module of production data is used to perform local interaction status supervision processing and multi-dimensional data analysis between different types of production data based on all single interaction supervision processing data obtained from different types of production data interaction supervision processing, and dynamically classify and mark the interactions between different types according to the analysis results; including: When supervising the local interaction status between different types of production data interaction, the total number of normal single interactions and the total number of abnormal single interactions between two types are obtained in turn, and the formula is used to calculate the normal and abnormal single interactions. Calculate and obtain the first local interaction state value JJ1 of the interaction between two types; where NZ and NY are the total number of normal and abnormal single interactions between two types, respectively; BJ is the standard value of the local interaction state between two types, which can be determined based on the previous test data or operation design data; β is the error influence coefficient of the interaction between two types, which plays a role in improving the accuracy and reliability of data calculation; It should be explained that the first local interaction state value is used to process and calculate all single interaction state data of interactions between two types in terms of the number of interactions, so as to digitally represent the local interaction state between them; The steps for obtaining the error influence coefficients for interaction between two types include: The total number of preset standard interactions between two types can be obtained based on the previous test data or operation design data, and the total number of normal single interactions and the total number of abnormal single interactions between two types can be combined with the total number of preset standard interactions through the formula Calculate and obtain the corresponding error influence coefficient β; where NB is the total number of single interaction standards between two types of interactions; k1 and k2 are different proportional coefficients, and 0<k1<k2<1, k1 can be taken as 0.427, and k2 can be taken as 0.573; It should be noted that by integrating and calculating the data of different times of interaction between two types, the standard value of the local interaction state of interaction between two types can be dynamically corrected, thereby improving the accuracy and reliability of the calculation of the local interaction state data between two types; And, through the formula Calculate and obtain the second local interaction state value JJ2 of the interaction between two types; where LZ is the total interaction flow of all interaction between two types; LB is the standard total interaction flow of interaction between two types, which can be determined based on the previous test data or operation design data; It should be explained that the second local interaction state value is used to process and calculate all single interaction state data of interactions between two types from the perspective of interaction traffic, so as to digitally represent the local interaction state between them; In an embodiment of the present invention, by performing diversified data calculations on different supervision processing data for interactions between two types, it is possible to digitally represent the local interaction states between two types from different aspects, which can effectively improve the diversity and reliability of the supervision processing of local interaction states between different types.

[0026] When determining the local interaction state of interaction between two types according to the first local interaction state value and the second local interaction state value, the first local interaction state value and the second local interaction state value are subjected to data analysis through the local interaction recognition model, and the local interaction state evaluation value JP corresponding to the two types is output; the expression of the local interaction recognition model is: ; The local interaction state evaluation value contains a value of 0, 1, or 2; Generate a normal prompt of the local interaction status between the two types according to the local interaction status evaluation value of 0; It is understandable that when the local interaction status between two types is normal, there are still two situations. One is that the remaining status of local interaction resources between two types is normal, that is, the actual interaction resource usage between two types is within the preset requirement range; the other is that the remaining status of local interaction resources between two types is abnormal, that is, the actual interaction resource usage between two types is lower than the preset requirement range, resulting in idle waste of preset interaction resources, so further traceability analysis is required; And, when tracing back and classifying the local interaction resource surplus corresponding to the normal local interaction status between two types, all the interaction data between two types with normal local interaction status are classified by formula Calculate and obtain the corresponding normal type value ZL; where, It is the maximum value of all maximum unit interaction data volumes between two types of interactions; The minimum unit interactive redundancy rate corresponding to the target type production data, , can be determined based on the previous test data or operational design data; like Figure 3 As shown, if the normal type value is 0, a local interaction resource remaining exception between the two types is generated, and the two types are all marked as the first exception type, which indicates that the actual interaction resource usage between the two types is lower than the preset requirement range; If the normal type value is -1, the remaining local interaction resources between the two types are generated to be normal, and the two types are marked as normal types; According to the local interaction state evaluation value of 1, a local interaction state slight abnormality prompt between two types is generated, and at the same time, the two types are marked as the second abnormal type; According to the local interaction state evaluation value of 2, a severe abnormal prompt of the local interaction state between two types is generated, and at the same time, both the two types are marked as the third abnormal type; In the embodiment of the present invention, local interaction status supervision processing and multi-dimensional data analysis are performed between different types based on all single interaction supervision processing data obtained through interactive supervision processing of different types of production data. Dynamic classification and labeling are performed on interactions between different pairwise types based on the analysis results, thereby expanding the utilization of previous single interaction supervision processing data between different pairwise types. At the same time, multi-level processing and digital representation are performed on local interaction status between different pairwise types, thereby improving autonomous supervision and analysis effects of different types of production data at different levels in terms of interaction.

[0027] The production data interaction optimization analysis module is used to perform interaction optimization analysis of the corresponding type of production data according to the classification and labeling results of interactions between different types, and to adaptively generate internal optimization prompts for interactive resources or internal and external collaborative optimization prompts according to the analysis results; including: Counting the remaining local interaction resources corresponding to different first exception types, and summing the remaining local interaction resources corresponding to all first exception types to obtain a total remaining local interaction resource; and, counting the first local interaction resource shortage amounts corresponding to different second abnormality types and the second local interaction resource shortage amounts corresponding to different third abnormality types; Sum up all first local interaction resource shortage amounts and all local interaction resource shortage amounts respectively to obtain a first local interaction resource shortage total amount and a second local interaction resource shortage total amount; like Figure 4 As shown, the remaining total amount of local interactive resources is matched and analyzed with the first total amount of local interactive resource shortage and the second total amount of local interactive resource shortage respectively; If the remaining total amount of local interactive resources simultaneously meets the requirements of the total amount of missing first local interactive resources and the total amount of missing second local interactive resources, an interactive resource internal optimization prompt is generated; If the remaining total amount of local interactive resources cannot simultaneously meet the needs of the total amount of missing first local interactive resources and the total amount of missing second local interactive resources, then an interactive resource internal and external collaborative optimization prompt is generated; Different from the existing technical solutions, which only stop at supervising and analyzing the interaction of different production data, and directly carry out the expansion and optimization of external resources based on the analysis results, it leads to poor comprehensiveness of expansion and optimization and poor resource utilization effect; In an embodiment of the present invention, an interaction optimization analysis of corresponding types of production data is performed based on the classification labeling results of interactions between different types, and internal optimization prompts for interaction resources or internal and external collaborative optimization prompts are adaptively generated based on the analysis results, thereby enabling evaluation of usage of interaction resources between different types from different dimensions, and dynamic data resource optimization management prompts are performed internally and externally based on the evaluation results, thereby improving the autonomous analysis and optimization prompt effects of different types of production data in terms of interaction.

[0028] In the several embodiments provided by the present invention, it should be understood that the disclosed system 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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. An AI-based intelligent production data analysis system, characterized in that: It includes a production data single interaction supervision module, a production data local interaction supervision module and a production data interaction optimization analysis module; The production data single interaction supervision module is used to supervise and collect data statistics on the single interactions of different types of production data in the enterprise's production process, and to digitally process and analyze the single interaction status of the supervision statistical data of different types of production data to obtain the single interaction supervision processing data corresponding to different types of production data; The local interaction supervision module of production data is used to perform local interaction status supervision processing and multi-dimensional data analysis between different types of production data according to all single interaction supervision processing data obtained from the interaction supervision processing of different types of production data, and dynamically classify and mark the interactions between different types according to the analysis results; the interaction classification marks between different types include normal type marks, first abnormal type marks, second abnormal type marks and third abnormal type marks; The production data interaction optimization analysis module is used to perform interaction optimization analysis on the corresponding type of production data according to the classification and labeling results of interactions between different types, and to adaptively generate internal optimization prompts for interactive resources or internal and external collaborative optimization prompts according to the analysis results; Wherein, the corresponding total amount of remaining local interactive resources is obtained according to all first abnormal types of classification marks, and the corresponding total amount of missing first local interactive resources and total amount of missing second local interactive resources are obtained according to all second abnormal types and all third abnormal types of classification marks respectively; The remaining total amount of local interactive resources is matched and analyzed with the total amount of missing first local interactive resources and the total amount of missing second local interactive resources, and an internal optimization prompt or an internal and external collaborative optimization prompt of interactive resources is adaptively generated according to the analysis results.

2. According to claim 1, the AI-based production data intelligent analysis system is characterized in that: When conducting supervision on the interaction of different types of production data, obtain the corresponding type supervision cycle according to the type of different production data, and obtain the local interaction period from the start to the end of the interaction between the target type of production data and other types of production data within the type supervision period, and obtain the maximum unit interaction data volume of the target type of production data and the same type of interaction process within the local interaction period; The maximum unit interaction data volume obtained is calculated by the formula Calculate the single data interaction value DJ corresponding to the interaction process between the target type's production data and the same type in the local interaction period; where JL is the maximum unit interaction data volume obtained; JB is the unit standard interaction data volume corresponding to the target type's production data and the interaction type; JX is the unit standard interaction redundancy rate corresponding to the target type's production data; a is the total number of historical active interactions for active production data interaction; b is the total number of historical active interactions for passive production data interaction.

3. According to claim 2, the AI-based production data intelligent analysis system is characterized in that: If the single data interaction value is less than or equal to 1, a single data interaction is generated to be normal, and the total number of single interactions between the two types of production data interaction is increased by one; Otherwise, a single data interaction exception is generated, and the total number of single interaction exceptions between the two types of production data interaction is increased by one; The single data interaction values, the total number of normal single interactions and the total number of abnormal single interactions obtained through the supervision and processing of type production data and the same type production data within the local interaction period are sorted and combined to obtain the single interaction supervision and processing data corresponding to the interaction between two types.

4. According to claim 3, the AI-based production data intelligent analysis system is characterized in that: When supervising the local interaction status between different types of production data interaction, the total number of normal single interactions and the total number of abnormal single interactions between two types are obtained in turn, and the formula is used to calculate the normal and abnormal single interactions. Calculate and obtain the first local interaction state value JJ1 of the interaction between two types; where NZ and NY are the total number of normal single interactions and the total number of abnormal single interactions between two types, respectively; BJ is the standard value of the local interaction state between two types; β is the error influence coefficient of the interaction between two types; And, through the formula The second local interaction state value JJ2 of the interaction between two types is calculated; where LZ is the total interaction flow of all interaction between two types; LB is the standard interaction total flow of interaction between two types.

5. According to claim 4, the AI-based production data intelligent analysis system is characterized in that: The steps for obtaining the error influence coefficients for interaction between two types include: Get the total number of preset standard interactions between two types, and add the total number of normal single interactions and the total number of abnormal single interactions between two types to the total number of preset standard interactions through the formula Calculate and obtain the corresponding error influence coefficient β; where NB is the total number of single interaction standards between two types of interactions; k1 and k2 are different proportional coefficients, and 0<k1<k2<1.

6. The AI-based production data intelligent analysis system according to claim 4 is characterized in that: When determining the local interaction state of interaction between two types according to the first local interaction state value and the second local interaction state value, the first local interaction state value and the second local interaction state value are subjected to data analysis through the local interaction recognition model, and the local interaction state evaluation value JP corresponding to the two types is output; the expression of the local interaction recognition model is: .

7. The AI-based production data intelligent analysis system according to claim 6 is characterized in that: Generate a normal prompt of the local interaction status between the two types according to the local interaction status evaluation value of 0; According to the local interaction state evaluation value of 1, a local interaction state slight abnormality prompt between two types is generated, and at the same time, the two types are marked as the second abnormal type; According to the local interaction state evaluation value of 2, a severe abnormal prompt of the local interaction state between the two types is generated, and the two types are marked as the third abnormal type.

8. The AI-based production data intelligent analysis system according to claim 7 is characterized in that: When tracing back and classifying the local interaction resource surplus corresponding to the normal local interaction status between two types, all the interaction data between two types with normal local interaction status are classified by formula Calculate and obtain the corresponding normal type value ZL; where, It is the maximum value of all maximum unit interaction data volumes between two types of interactions; The minimum interactive redundancy rate per unit corresponding to the target type production data; If the normal type value is 0, a local interaction resource remaining exception between the two types is generated, and the two types are marked as the first exception type; If the normal type value is -1, the remaining local interaction resources between the two types are generated to be normal, and the two types are marked as normal types.

9. The AI-based production data intelligent analysis system according to claim 8, characterized in that: Counting the remaining local interaction resources corresponding to different first exception types, and summing the remaining local interaction resources corresponding to all first exception types to obtain a total remaining local interaction resource; and, counting the first local interaction resource shortage amounts corresponding to different second abnormality types and the second local interaction resource shortage amounts corresponding to different third abnormality types; All first local interaction resource shortage amounts and all local interaction resource shortage amounts are summed up respectively to obtain the first local interaction resource shortage total amount and the second local interaction resource shortage total amount.

10. The AI-based production data intelligent analysis system according to claim 9, characterized in that: If the remaining total amount of local interactive resources simultaneously meets the requirements of the total amount of missing first local interactive resources and the total amount of missing second local interactive resources, an interactive resource internal optimization prompt is generated; If the remaining total amount of local interactive resources cannot simultaneously meet the needs of the first local interactive resource shortage total amount and the second local interactive resource shortage total amount, an interactive resource internal and external collaborative optimization prompt is generated.

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