Intelligent management method and system for electromechanical engineering equipment

Abstract

The invention relates to the field of intelligent management of production equipment, and provides an intelligent management method and system for electromechanical engineering equipment, and the method comprises the steps: firstly obtaining the production task information of a plurality of production tasks and the equipment information of to-be-distributed electromechanical engineering equipment; then, initializing an allocation scheme of a plurality of production tasks on electromechanical engineering equipment to be allocated; and finally, managing processing priorities of the plurality of production tasks based on production task information of the plurality of production tasks and the equipment information. Therefore, more flexible task allocation can be realized, and the adaptability and flexibility of equipment management are effectively improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent management of production equipment, and more specifically, to an intelligent management method and system for electromechanical engineering equipment. Background Art

[0002] With the development of information technology, technologies such as sensors, the Internet of Things, and big data analysis have made it possible to manage and control electromechanical equipment in an information-based manner, solving the problems of heavy manual inspections, slow fault response, and high maintenance costs in traditional management. However, the existing management and control systems use predefined scheduling methods, which makes it difficult to adapt to changes in production in real time when facing multi-variable and complex systems, affecting efficiency and management effectiveness.

[0003] In response to the above technical problems, patent CN118095580B proposes an information management and control system for electromechanical engineering equipment. It collects production tasks and equipment information, generates and optimizes task allocation plans, continuously iterates until the global optimal solution is found, and finally outputs the best task allocation plan, solving the problems of heavy manual inspections and untimely fault handling in traditional equipment management.

[0004] The above patent updates the allocation speed of each task allocation by combining the global optimal solution, the individual optimal solution, the allocation content and the allocation speed. Although this method can quickly adjust the strategy to cope with changing environments and conditions, it lacks in-depth analysis of contextual semantic associations, and its adaptability and flexibility are relatively weak when dealing with complex production and equipment management scenarios. In addition, this method mainly focuses on the current optimization value and the historical best solution, and fails to fully consider multi-dimensional factors, such as the dependencies between tasks, the working status of equipment, etc., which makes the optimization results may not be comprehensive and accurate enough, and it is difficult to cope with the needs of highly dynamic and interconnected production environments. This single-dimensional optimization may lead to unreasonable resource allocation, affecting overall production efficiency and equipment utilization.

[0005] Therefore, an optimized intelligent management system for electromechanical engineering equipment is desired. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present application provides an intelligent management method and system for electromechanical engineering equipment.

[0007] According to one aspect of the present application, there is provided an intelligent management system for electromechanical engineering equipment, comprising:

[0008] A production task information acquisition module, used to acquire production task information of multiple production tasks to obtain a set of production task information;

[0009] The equipment information acquisition module is used to acquire the equipment information of the electromechanical engineering equipment to be allocated;

[0010] An allocation speed initialization module, used to initialize the allocation scheme of the plurality of production tasks on the electromechanical engineering equipment to be allocated;

[0011] A production task allocation speed adjustment module, used for managing the processing priorities of the plurality of production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be allocated;

[0012] Among them, the production task allocation speed adjustment module includes: a production task information encoding unit, which is used to semantically encode and associate the set of production task information to obtain a set of semantic association features of production task information; an equipment information encoding unit to be assigned, which is used to semantically encode the equipment information of the electromechanical engineering equipment to be assigned to obtain equipment information semantic encoding features; a production task equipment semantic interaction response unit, which is used to perform principal component feature semantic interaction response on the set of production task information semantic association features and the equipment information semantic encoding features to obtain a set of production task information-equipment information semantic interaction response features; a processing priority management unit, which is used to generate a task processing priority allocation result based on the set of production task information-equipment information semantic interaction response features, and sort the processing priorities of the multiple production tasks.

[0013] In the above-mentioned intelligent management system for electromechanical engineering equipment, the production task information encoding unit includes: a production task information semantic encoding subunit, which is used to semantically encode the set of production task information to obtain a set of production task information semantic encoding feature vectors; a production task information semantic association feature extraction subunit, which is used to pass the set of production task information semantic encoding feature vectors through a converter-based context encoder to obtain a set of production task information semantic association feature vectors as the set of production task information semantic association features.

[0014] In the above-mentioned intelligent management system for electromechanical engineering equipment, the to-be-allocated equipment information encoding unit is used to: semantically encode the equipment information of the to-be-allocated electromechanical engineering equipment to obtain an equipment information semantic encoding feature vector as the equipment information semantic encoding feature.

[0015] In the above-mentioned intelligent management system for electromechanical engineering equipment, the production task equipment semantic interaction response unit includes: a production task information equipment information principal component analysis subunit, which is used to perform principal component analysis on the production task information semantic association feature vector and the equipment information semantic coding feature vector to obtain a set of production task information semantic principal component feature components and a set of equipment information semantic principal component feature components; a production task information-equipment information optimal matching subunit, which is used to perform production task information-equipment information optimal matching interaction on the set of production task information semantic principal component feature components and the set of equipment information semantic principal component feature components to obtain a production task information-equipment information semantic interaction response feature vector as a set of production task information-equipment information semantic interaction response features.

[0016] In the above-mentioned intelligent management system for electromechanical engineering equipment, the production task information-equipment information optimal matching subunit includes: a production task information-equipment information optimal matching secondary subunit, which is used to use each production task information semantic main component feature component in the set of the production task information semantic main component feature components as a query vector and the set of the equipment information semantic main component feature components as a query library, and match the equipment information semantic main component feature components that best match each query vector from the query library to obtain a set of {production task information semantic main component feature components; equipment information semantic main component feature components} optimal matching pairs; production task information-equipment information multi-scale interaction A secondary sub-unit is used to input each best matching pair of {production task information semantic main component feature component; equipment information semantic main component feature component} in the set of the best matching pairs of {production task information semantic main component feature component; equipment information semantic main component feature component} into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of the production task information-equipment information semantic best matching pairs; a production task information-equipment information semantic interactive response feature generation secondary sub-unit is used to cascade the set of multi-scale interactive coupling representation vectors of the production task information-equipment information semantic best matching pairs to obtain the production task information-equipment information semantic interactive response feature vector.

[0017] In the above-mentioned intelligent management system for electromechanical engineering equipment, the production task information-equipment information optimal matching secondary sub-unit is used to: extract predetermined production task information semantic principal component feature components from the set of production task information semantic principal component feature components; calculate the Mahalanobis distance between the predetermined production task information semantic principal component feature components and each equipment information semantic principal component feature component in the set of equipment information semantic principal component feature components to obtain a set of matching distances, wherein a pair of {production task information semantic principal component feature components; equipment information semantic principal component feature components} with the smallest matching distance in the set of matching distances is taken as the best matching pair.

[0018] In the above-mentioned intelligent management system for electromechanical engineering equipment, the production task information-equipment information multi-scale interactive secondary sub-unit is used to: respectively calculate the bi-norm of the production task information semantic main component feature component and the equipment information semantic main component feature component in the best matching pair of {production task information semantic main component feature component; equipment information semantic main component feature component} to obtain the production task information norm value and the equipment information norm value; extract the maximum value from the production task information norm value, the equipment information norm value and the modulation parameter to obtain the interaction maximum value; respectively calculate the positional subtraction, positional dot multiplication and positional addition between the production task information semantic main component feature component and the equipment information semantic main component feature component to obtain the production task information-equipment information semantic main component difference vector and the production task information-equipment information semantic main component dot product vector and the sum vector of the semantic principal components of production task information-equipment information; cascade the difference vector of the semantic principal components of production task information-equipment information, the dot product vector of the semantic principal components of production task information-equipment information and the sum vector of the semantic principal components of production task information-equipment information to obtain a cascade vector of the semantic principal components of production task information-equipment information; perform one-dimensional convolution encoding on the cascade vector of the semantic principal components of production task information-equipment information to obtain a convolution encoding vector of the semantic principal components of production task information-equipment information; perform maximum pooling processing based on a local window on the convolution encoding vector of the semantic principal components of production task information-equipment information, and divide the obtained semantic interaction representation vector of production task information-equipment information by the maximum interaction value point by point to obtain a multi-scale interaction coupling representation vector of the semantic best match of production task information-equipment information.

[0019] In the above-mentioned intelligent management system for electromechanical engineering equipment, the processing priority management unit includes: a production task allocation speed decoding subunit, which is used to obtain a set of production task allocation speed decoding values ​​based on the set of production task information-equipment information semantic interaction response features; a task processing priority allocation result generation subunit, which is used to arrange the individual production task allocation speed decoding values ​​in the set of production task allocation speed decoding values ​​in order from large to small to obtain a task processing priority allocation result; a production task processing sorting subunit, which is used to sort the processing priorities of the multiple production tasks based on the task processing priority allocation result.

[0020] In the above-mentioned intelligent management system for electromechanical engineering equipment, the production task allocation speed decoding subunit is used to: pass each production task information-equipment information semantic interaction response feature vector in the set of production task information-equipment information semantic interaction response feature vectors through a decoder-based task priority allocator to obtain a set of production task allocation speed decoding values.

[0021] According to another aspect of the present application, there is provided a method for intelligent management of electromechanical engineering equipment, comprising:

[0022] Acquire production task information of multiple production tasks to obtain a set of production task information;

[0023] Obtain equipment information of mechanical and electrical engineering equipment to be allocated;

[0024] Initializing the allocation scheme of the plurality of production tasks on the electromechanical engineering equipment to be allocated;

[0025] Managing the processing priorities of the plurality of production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be allocated;

[0026] Among them, the processing priorities of the multiple production tasks are managed based on the set of production task information and the equipment information of the electromechanical engineering equipment to be assigned, including: semantically encoding and associating the set of production task information to obtain a set of semantic association features of production task information; semantically encoding the equipment information of the electromechanical engineering equipment to be assigned to obtain equipment information semantic encoding features; performing principal component feature semantic interaction response on the set of production task information semantic association features and the equipment information semantic encoding features to obtain a set of production task information-equipment information semantic interaction response features; based on the set of production task information-equipment information semantic interaction response features, generating a task processing priority allocation result, and sorting the processing priorities of the multiple production tasks.

[0027] This application has significant technical effects due to the adoption of the above technical solutions:

[0028] The intelligent management method and system for electromechanical engineering equipment provided by the present application adopts information analysis and coding based on artificial intelligence to semantically encode each production task information and semantically associate the context, and to semantically encode the equipment information to be assigned, so as to intelligently obtain the production task assignment speed decoding value based on the semantic association characteristics of each production task information and the main component semantic interaction response characteristics between the semantic coding characteristics of the equipment information, and adjust the processing priority of each production task. In this way, it can better adapt to complex and changeable production and equipment management scenarios, improve the adaptability and flexibility in a dynamic environment, and can more comprehensively consider multi-dimensional factors such as the dependency between production tasks and the working status of equipment, thereby providing a more reasonable and flexible solution for task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0030] Figure 1 This is a system block diagram of an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application.

[0031] Figure 2 It is a block diagram of a production task allocation speed adjustment module in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application.

[0032] Figure 3 This is a data flow diagram of a production task allocation speed adjustment module in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application.

[0033] Figure 4 It is a block diagram of a production task information encoding unit in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application.

[0034] Figure 5 It is a block diagram of a semantic interaction response unit of a production task device in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application.

[0035] Figure 6 It is a block diagram of a processing priority management unit in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application.

[0036] Figure 7It is a flow chart of the intelligent management method for electromechanical engineering equipment according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0038] With the continuous advancement of information technology, the application of technologies such as sensors, the Internet of Things, and big data analysis has made the information management of electromechanical equipment a reality. It can effectively solve the problems of high labor intensity of manual inspections, slow fault response, and high maintenance costs in traditional management methods. However, the existing management systems mostly use preset scheduling strategies, which are difficult to adapt to the multi-variable and complex changes in the production process, thus affecting management efficiency and effectiveness.

[0039] In response to the above technical problems, patent CN118095580B proposes an information management and control system for electromechanical engineering equipment, which can collect production tasks and equipment status information, and generate and optimize task allocation plans. Through continuous iteration, the system can find the global optimal task allocation plan, effectively solving the problems of heavy manual inspection burden and untimely fault response in traditional equipment management.

[0040] Although the method described in the patent can quickly adjust strategies to adapt to environmental changes, its adaptability and flexibility are limited when dealing with complex production and equipment management scenarios due to the lack of in-depth analysis of contextual semantics. In addition, the method mainly focuses on the current optimization results and historical best solutions, and fails to fully consider multi-dimensional factors such as the interdependence between tasks and the actual working status of the equipment. This may result in optimization results that are not comprehensive and accurate enough to meet the needs of a highly dynamic and interconnected production environment. Single-dimensional optimization may lead to unreasonable resource allocation, which in turn affects overall production efficiency and equipment utilization.

[0041] Based on this, the present application proposes an intelligent management system for electromechanical engineering equipment. Figure 1 1 is a system block diagram of an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application. Figure 1As shown, in the intelligent management system 100 for electromechanical engineering equipment, it includes: a production task information acquisition module 110, which is used to acquire the production task information of multiple production tasks to obtain a set of production task information; an equipment information acquisition module 120, which is used to acquire the equipment information of the electromechanical engineering equipment to be assigned; an allocation speed initialization module 130, which is used to initialize the allocation scheme of the multiple production tasks on the electromechanical engineering equipment to be assigned; a production task allocation speed adjustment module 140, which is used to manage the processing priorities of the multiple production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be assigned.

[0042] In the embodiment of the present application, the production task information acquisition module 110 and the equipment information acquisition module 120 are respectively used to obtain the production task information of multiple production tasks to obtain a collection of production task information and to obtain the equipment information of the electromechanical engineering equipment to be assigned. It should be understood that the production task information of each production task includes the task number, task type, processing priority and the like, and the collection of production task information can be obtained by sorting the production task information of multiple production tasks. The equipment information of the electromechanical engineering equipment to be assigned specifically includes the equipment number, equipment type, processing capacity, current status and the like. Here, by obtaining the production task information of multiple production tasks, it can be analyzed in subsequent steps, which can help the system understand the urgency of different production tasks, and can also identify the dependencies between different production tasks, thereby helping to optimize task scheduling. By obtaining the equipment information of the electromechanical engineering equipment to be assigned and analyzing it, it can help the system better understand the equipment processing capacity and status, thereby optimizing task allocation and avoiding waste of resources and overload of equipment. In general, combined with real-time production tasks and equipment information, the system can flexibly respond to changes in the production environment, adjust task priorities and equipment allocation plans in a timely manner, thereby improving overall production efficiency.

[0043] In an embodiment of the present application, the allocation speed initialization module 130 is used to initialize the allocation scheme of the multiple production tasks on the electromechanical engineering equipment to be allocated. It should be noted that a concept of allocation speed is proposed in the present application, which refers to the degree of adjustment of the processing priority of the production task on the electromechanical engineering equipment. It should be understood that initializing the allocation scheme of the multiple production tasks in the present application specifically refers to initially setting the allocation speed of the task allocation scheme of each production task in the multiple production tasks to 0. By resetting the allocation speeds of different production tasks to zero, a unified reference state can be provided for subsequent task scheduling and priority adjustment, thereby helping to avoid scheduling confusion caused by inconsistent initial states of different tasks.

[0044] In the embodiment of the present application, the production task allocation speed adjustment module 140 is used to manage the processing priorities of the multiple production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be allocated.

[0045] Correspondingly, in the production task allocation speed adjustment module, the technical concept of the present application is to use information analysis and coding methods based on artificial intelligence to semantically encode each production task information and semantically associate it with the context, and to semantically encode the equipment information to be allocated, so as to intelligently obtain the production task allocation speed decoding value based on the semantic association features of each production task information and the main component semantic interaction response features between the semantic coding features of the equipment information, and adjust the processing priority of each production task. In this way, it can better adapt to complex and changeable production and equipment management scenarios, improve the adaptability and flexibility in a dynamic environment, and can more comprehensively consider multi-dimensional factors such as the dependency between production tasks and the working status of equipment, thereby providing a more reasonable and flexible solution for task allocation.

[0046] Specifically, Figure 2 It is a block diagram of a production task allocation speed adjustment module in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application. Figure 3 Schematic diagram of data flow of the production task allocation speed adjustment module in the intelligent management system for electromechanical engineering equipment according to the embodiment of the present application. Figure 2 and Figure 3 As shown, the production task allocation speed adjustment module 140 includes: a production task information encoding unit 141, which is used to semantically encode and associate the set of production task information to obtain a set of semantic association features of production task information; an equipment information encoding unit 142 to be assigned, which is used to semantically encode the equipment information of the electromechanical engineering equipment to be assigned to obtain equipment information semantic encoding features; a production task equipment semantic interaction response unit 143, which is used to perform principal component feature semantic interaction response on the set of production task information semantic association features and the equipment information semantic encoding features to obtain a set of production task information-equipment information semantic interaction response features; a processing priority management unit 144, which is used to generate a task processing priority allocation result based on the set of production task information-equipment information semantic interaction response features, and to sort the processing priorities of the multiple production tasks.

[0047] In the embodiment of the present application, the production task information encoding unit 141 is used to semantically encode and associate the set of production task information to obtain a set of production task information semantic association features. Specifically, Figure 4 FIG. 1 is a block diagram of a production task information encoding unit in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application. Figure 4 As shown, the production task information encoding unit 141 includes: a production task information semantic encoding subunit 1411, used to semantically encode the set of production task information to obtain a set of production task information semantic encoding feature vectors; a production task information semantic association feature extraction subunit 1412, used to pass the set of production task information semantic encoding feature vectors through a converter-based context encoder to obtain a set of production task information semantic association feature vectors as the set of production task information semantic association features.

[0048] In an embodiment of the present application, the production task information semantic encoding subunit 1411 is used to semantically encode the set of production task information to obtain a set of semantically encoded feature vectors of the production task information. It should be understood that the set of production task information contains a large amount of semantic information, such as task descriptions, processing priorities, resource requirements, etc. In order to better capture and extract the deep semantic meanings and contextual relationships in each piece of production task information, in the technical solution of the present application, the set of production task information is semantically encoded to obtain a set of semantically encoded feature vectors of the production task information. In particular, in a specific implementation method of the embodiment of the present application, a BERT model can be used to semantically encode the set of production task information to obtain a set of semantically encoded feature vectors of the production task information.

[0049] In an embodiment of the present application, the production task information semantic association feature extraction subunit 1412 is used to pass the set of production task information semantic encoding feature vectors through a converter-based context encoder to obtain a set of production task information semantic association feature vectors as the set of production task information semantic association features. Accordingly, considering that different features in the set of production task information semantic encoding feature vectors have different semantic associations, and the converter model can effectively capture the long-distance dependencies between features at different positions in the input feature sequence through the self-attention mechanism, this means that the model can capture the complex relationships and contextual information between the production task information semantic encoding features. Based on this, in the technical solution of the present application, the set of production task information semantic encoding feature vectors is passed through a converter-based context encoder to capture and mine the production task information semantic association relationships between different semantic spans, thereby obtaining a set of production task information semantic association feature vectors.

[0050] In the embodiment of the present application, the device information encoding unit 142 to be allocated is used to semantically encode the device information of the electromechanical engineering equipment to be allocated to obtain the semantic encoding features of the device information. Specifically, in the embodiment of the present application, the device information encoding unit to be allocated is used to: semantically encode the device information of the electromechanical engineering equipment to be allocated to obtain the device information semantic encoding feature vector as the device information semantic encoding feature. It should be understood that the device information to be allocated expresses semantic information about the device, such as the capability range, device number, and processing status of the device. Therefore, in the technical solution of the present application, the device information of the electromechanical engineering equipment to be allocated is semantically encoded to capture the deep semantics in the device information, and obtain the device information semantic encoding feature vector. In particular, in a specific implementation method of the embodiment of the present application, the BERT model is used to semantically encode the device information of the electromechanical engineering equipment to be allocated, so as to obtain the device information semantic encoding feature vector.

[0051] In the embodiment of the present application, the production task equipment semantic interaction response unit 143 is used to perform principal component feature semantic interaction response on the set of production task information semantic association features and the equipment information semantic coding features to obtain a set of production task information-equipment information semantic interaction response features. Specifically, Figure 5 FIG. 1 is a block diagram of a semantic interaction response unit for a production task device in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application. Figure 5 As shown, the production task equipment semantic interaction response unit 143 includes: a production task information equipment information principal component analysis subunit 1431, which is used to perform principal component analysis on the production task information semantic association feature vector and the equipment information semantic coding feature vector to obtain a set of production task information semantic principal component feature components and a set of equipment information semantic principal component feature components; a production task information-equipment information optimal matching subunit 1432, which is used to perform a production task information-equipment information optimal matching interaction on the set of the production task information semantic principal component feature components and the set of the equipment information semantic principal component feature components to obtain a production task information-equipment information semantic interaction response feature vector as a set of production task information-equipment information semantic interaction response features.

[0052] It should be understood that each of the production task information semantic association feature vectors in the set of production task information semantic association feature vectors and the equipment information semantic coding feature vector contain key semantic information, and there is a semantic correlation between the production task information and the equipment information. Therefore, in order to achieve a more refined and efficient matching between production tasks and equipment, to identify which task features are most relevant to the equipment features, and to optimize the matching accordingly, thereby improving the allocation of processing priorities, in the technical solution of the present application, each of the production task information semantic association feature vectors in the set of production task information semantic association feature vectors and the equipment information semantic coding feature vector are subjected to principal component feature semantic interaction response to obtain a set of production task information-equipment information semantic interaction response feature vectors as a set of production task information-equipment information semantic interaction response features.

[0053] In detail, first, principal component analysis is performed on the semantic association feature vector of the production task information and the semantic encoding feature vector of the equipment information to obtain a set of semantic principal component feature components of the production task information and a set of semantic principal component feature components of the equipment information. Specifically, principal component analysis (PCA) is a commonly used dimensionality reduction technique that projects the original feature vector into a new coordinate system to reveal the intrinsic structure hidden in high-dimensional data, retaining most of the variance information of the original data while reducing the dimension of the features, that is, the obtained principal component sets represent the main part of the original feature space that best reflects the data changes.

[0054] Then, the set of the semantic principal component feature components of the production task information and the set of the semantic principal component feature components of the equipment information are subjected to the optimal matching interaction of the production task information and the equipment information to obtain the production task information-equipment information semantic interaction response feature vector. Specifically, taking the semantic principal component feature components of each production task information as the query vector and the set of the semantic principal component feature components of the equipment information as the query library, the optimal matching is performed on each semantic principal component feature component of the production task information to obtain the set of the best matching pairs of {semantic principal component feature components of production task information; semantic principal component feature components of equipment information}. Such pairing is not just a simple element association, but an attempt to establish a one-to-one mapping relationship between the two sets of features, so as to clarify the key semantic relationship between the production task information and the equipment information, and select the most representative feature combination. In particular, in the present technical solution, the Mahalanobis distance between the two principal component components is calculated as the matching value between the two, and the pair with the smallest distance is taken as the best matching pair. Afterwards, in order to further identify and explore the deep meaning connection and multi-dimensional and multi-scale interactions between each group of best matching pairs, each group of best matching pairs is interacted and captured at multiple levels by using a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of the best matching pairs of production task information-equipment information semantics. Finally, the set of vectors of the best matching pairs is cascaded to comprehensively and fully represent the semantic matching between production tasks and equipment, forming a compact and information-rich production task information-equipment information semantic interactive response feature vector.

[0055] Specifically, in an embodiment of the present application, the production task information-equipment information optimal matching subunit includes: a production task information-equipment information optimal matching secondary subunit, which is used to use each production task information semantic main component feature component in the set of the production task information semantic main component feature components as a query vector and the set of the equipment information semantic main component feature components as a query library, and match the equipment information semantic main component feature components that optimally match each query vector from the query library to obtain a set of {production task information semantic main component feature components; equipment information semantic main component feature components} optimal matching pairs; a production task information-equipment information multi-scale interaction secondary subunit. A unit is used to input each best matching pair of {production task information semantic principal component feature component; equipment information semantic principal component feature component} in the set of the best matching pairs of {production task information semantic principal component feature component; equipment information semantic principal component feature component} into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of the production task information-equipment information semantic best matching pairs; a production task information-equipment information semantic interactive response feature generation secondary sub-unit is used to cascade the set of multi-scale interactive coupling representation vectors of the production task information-equipment information semantic best matching pairs to obtain the production task information-equipment information semantic interactive response feature vector.

[0056] More specifically, in an embodiment of the present application, the production task information-equipment information optimal matching secondary sub-unit is used to: extract predetermined production task information semantic main component feature components from the set of production task information semantic main component feature components; calculate the Mahalanobis distance between the predetermined production task information semantic main component feature components and each equipment information semantic main component feature component in the set of equipment information semantic main component feature components to obtain a set of matching distances, wherein a pair of {production task information semantic main component feature components; equipment information semantic main component feature components} with the smallest matching distance in the set of matching distances is taken as the best matching pair.

[0057] More specifically, in an embodiment of the present application, the production task information-equipment information multi-scale interaction secondary subunit is used to: respectively calculate the bi-norm of the production task information semantic main component feature component and the equipment information semantic main component feature component in the best matching pair of {production task information semantic main component feature component; equipment information semantic main component feature component} to obtain the production task information norm value and the equipment information norm value; extract the maximum value from the production task information norm value and the equipment information norm value and the modulation parameter to obtain the interaction maximum value; respectively calculate the positional subtraction, positional dot product and positional addition between the production task information semantic main component feature component and the equipment information semantic main component feature component to obtain the production task information-equipment information semantic main component difference vector, the production task information-equipment information semantic main component dot product vector and the production task information-equipment information semantic main component difference vector. The task information-equipment information semantic principal component sum vector; cascade the production task information-equipment information semantic principal component difference vector, the production task information-equipment information semantic principal component dot product vector and the production task information-equipment information semantic principal component sum vector to obtain the production task information-equipment information semantic principal component cascade vector; perform one-dimensional convolution encoding on the production task information-equipment information semantic principal component cascade vector to obtain the production task information-equipment information semantic principal component convolution encoding vector; perform local window-based maximum pooling processing on the production task information-equipment information semantic principal component convolution encoding vector, and divide the obtained production task information-equipment information semantic interaction representation vector and the interaction maximum value point by point to obtain the multi-scale interaction coupling representation vector of the production task information-equipment information semantic best match.

[0058] In the embodiment of the present application, specifically, the production task equipment semantic interaction response unit is used to: perform principal component feature semantic interaction response processing on the production task information semantic association feature vector and the equipment information semantic coding feature vector according to the following formula, thereby obtaining the production task information-equipment information semantic interaction response feature vector; wherein the formula is:

[0059] C1=U1Λ1U1 T

[0060] C2=U2Λ2U2 T

[0061] U1=[v 11 ,v 12 ,…,v 1m ]

[0062]

[0063] U2=[v 21 ,v 22 ,…,v2m ]

[0064]

[0065] v f =[v p1 ;v p2 ;...;v pi ...;v pm ]

[0066] Among them, C1 and C2 are the sample covariance matrix of production task information and the sample covariance matrix of equipment information, respectively; U1 and U2 are the principal component orthogonal matrix of production task information and the principal component orthogonal matrix of equipment information, respectively; 11 ,v 12 ,…,v 1m is each production task information semantic principal component feature component in the set of production task information semantic principal component feature components, v 21 ,v 22 ,…,v 2m is each device information semantic principal component feature component in the set of device information semantic principal component feature components, Λ1 and Λ2 are the production task information diagonal matrix and the device information diagonal matrix respectively, λ 11 , 1m are the weight values ​​of the first and mth device information semantic principal component feature components in the set of device information semantic principal component feature components, respectively, 21 , 2m are the weight values ​​of the first and mth device information semantic principal component feature components in the set of device information semantic principal component feature components, respectively, U1 T and U2 T are the transposed matrices of U1 and U2, respectively, 1i is the i-th production task information semantic principal component feature component in the set of production task information semantic principal component feature components, S ij v 1i and v 2j The covariance matrix between j min(·) is the j value corresponding to the minimum value returned, k is the minimum approximate matching value, ⊙ and are position difference, position dot multiplication and position addition respectively, [·; ·; ·] is cascade processing, conv1D(·) is a one-dimensional convolutional coding operation, MaxPool(·) is a maximum pooling operation, max(·,·,·) is to extract the maximum value, ‖·‖2 is the vector bi-norm, ε is the modulation parameter, and v p1 、v p2 、v pi and v pmare the first, second, i-th and m-th multi-scale interactive coupling representation vectors of the best semantic matching pair of production task information and equipment information in the set of multi-scale interactive coupling representation vectors of the best semantic matching pair of production task information and equipment information, respectively, v f It is the production task information-equipment information semantic interaction response feature vector.

[0067] In the embodiment of the present application, the processing priority management unit 144 is used to generate a task processing priority allocation result based on the set of production task information-equipment information semantic interaction response features, and sort the processing priorities of the multiple production tasks. Specifically, Figure 6 FIG. 1 is a block diagram of a processing priority management unit in an intelligent management system for electromechanical engineering equipment according to an embodiment of the present application. Figure 6 As shown, the processing priority management unit 144 includes: a production task allocation speed decoding subunit 1441, which is used to obtain a set of production task allocation speed decoding values ​​based on the set of production task information-equipment information semantic interaction response features; a task processing priority allocation result generating subunit 1442, which is used to arrange each production task allocation speed decoding value in the set of production task allocation speed decoding values ​​in order from large to small to obtain a task processing priority allocation result; a production task processing sorting subunit 1443, which is used to sort the processing priorities of the multiple production tasks based on the task processing priority allocation result.

[0068] In an embodiment of the present application, the production task allocation speed decoding subunit 1441 is used to obtain a set of production task allocation speed decoding values ​​based on the set of production task information-equipment information semantic interaction response features. Specifically, in an embodiment of the present application, the production task allocation speed decoding subunit is used to: pass each production task information-equipment information semantic interaction response feature vector in the set of production task information-equipment information semantic interaction response feature vectors through a decoder-based task priority allocator to obtain the set of production task allocation speed decoding values. That is, the set of production task information-equipment information semantic interaction response features obtained by semantic interaction response using each production task information semantic association feature vector in the set of production task information semantic association feature vectors and the equipment information semantic encoding feature vector is decoded to obtain a set of production task allocation speed decoding values. Specifically, tasks with higher speed decoding values ​​may have higher priorities, indicating that they are more preferentially adapted to the currently allocated equipment.

[0069] In the embodiment of the present application, the task processing priority allocation result generating subunit 1442 and the production task processing sorting subunit 1443 are respectively used to arrange the individual production task allocation speed decoding values ​​in the set of the production task allocation speed decoding values ​​in order from large to small to obtain the task processing priority allocation result and to sort the processing priority of the multiple production tasks based on the task processing priority allocation result. A larger decoding value usually indicates that the production task has a higher priority, which means that it needs to be processed faster. By arranging the production task allocation speed decoding values ​​in order from large to small, it can be clear which production tasks are more urgent in the current production environment. Based on the task processing priority allocation result obtained by the arrangement, a clear execution order can be provided for the production equipment, and by giving priority to high-priority tasks, it can ensure that limited resources are used most effectively and reduce production losses caused by task delays. In this way, it can better adapt to complex and changeable production and equipment management scenarios, improve adaptability and flexibility in a dynamic environment, and can more comprehensively consider multi-dimensional factors such as dependencies between production tasks and equipment workload prediction, thereby providing a more reasonable and flexible solution for task allocation.

[0070] In particular, when the production task information semantic association feature vector and the equipment information semantic coding feature vector respectively represent the semantic coding features of the production task information and the equipment information to be assigned, when performing the principal component feature semantic interactive response, the uneven interactive matching sparsity of the principal component granularity distribution of the semantic coding features under different task source semantics will lead to the lack of local response coupling, that is, it will lead to the simple repetition of the set coupling semantics in the local semantic domain, affecting the logical dependency of the set semantics and reducing the distinguishability accuracy of each production task assignment speed decoding value in the set of production task assignment speed decoding values.

[0071] Preferably, each production task information-equipment information semantic interaction response feature vector in the set of the production task information-equipment information semantic interaction response feature vector is respectively passed through a decoder-based task priority allocator to obtain a set of production task allocation speed decoding values, including: cascading each production task information-equipment information semantic interaction response feature vector into a production task information-equipment information semantic interaction response cascade feature vector; determining a production task information-equipment information semantic interaction response cascade association response matrix and a production task information-equipment information semantic interaction response cascade distance response matrix based on the square root of the association value of the eigenvalue of the production task information-equipment information semantic interaction response cascade feature vector and the L2 distance value: wherein the process can be expressed as follows:

[0072]

[0073] vi ,v j ∈V∈R 1×L

[0074] Where V is the cascade feature vector of the semantic interaction response of production task information and equipment information, L represents the number of feature values ​​in V, and v i is the feature value of the ith position in the cascade feature vector of the production task information-equipment information semantic interaction response, v j is the eigenvalue of the jth position in the production task information-equipment information semantic interaction response cascade feature vector, M1(i,j) is the eigenvalue of the (i,j)th position in the production task information-equipment information semantic interaction response cascade association response matrix, and M2(i,j) is the eigenvalue of the (i,j)th position in the production task information-equipment information semantic interaction response cascade distance response matrix;

[0075] The production task information-equipment information semantic interaction response cascade feature vector is matrix-multiplied with the production task information-equipment information semantic interaction response cascade association response matrix to obtain the production task information-equipment information semantic interaction response cascade association response vector; the production task information-equipment information semantic interaction response cascade distance response matrix is ​​matrix-multiplied with the transposed vector of the production task information-equipment information semantic interaction response cascade feature vector to obtain the production task information-equipment information semantic interaction response cascade distance response vector; the production task information-equipment information semantic interaction response cascade association response matrix is ​​dot-multiplied with the production task information-equipment information semantic interaction response cascade distance response matrix, and the transposed vector of the production task information-equipment information semantic interaction response cascade feature vector is multiplied. Perform matrix multiplication to obtain a production task information-equipment information semantic interaction response cascade bias vector; perform point multiplication on the production task information-equipment information semantic interaction response cascade association response vector, the production task information-equipment information semantic interaction response cascade distance response vector and the production task information-equipment information semantic interaction response cascade bias vector to obtain an optimized production task information-equipment information semantic interaction response cascade feature vector; split the optimized production task information-equipment information semantic interaction response cascade feature vector into individual optimized production task information-equipment information semantic interaction response feature vectors; pass each of the optimized production task information-equipment information semantic interaction response feature vectors through the decoder-based task priority allocator to obtain a set of production task allocation speed decoding values.

[0076] Here, the optimized production task information-equipment information semantic interaction response cascade feature vector is expressed as:

[0077]

[0078] V1∈R 1×L

[0079] V2∈R L×1

[0080]

[0081] Wherein, V is the cascade feature vector of the semantic interaction response of production task information and equipment information, L represents the number of feature values ​​in V, represents matrix multiplication, M1 is the production task information-equipment information semantic interaction response cascade association response matrix, represents adding by location point, M2 is the cascade distance response matrix of the production task information-equipment information semantic interaction response, V T is the transposed vector of V, V1 is the point-added vector between the production task information-equipment information semantic interaction response cascade association response vector and the production task information-equipment information semantic interaction response cascade distance response vector, ⊙ represents the point multiplication by position, V2 is the production task information-equipment information semantic interaction response cascade bias vector, and V' is the optimized production task information-equipment information semantic interaction response cascade feature vector.

[0082] That is, by using the production task information-equipment information semantic interaction response cascade feature vector of each production task information-equipment information semantic interaction response feature vector set as its statistical reference-free distribution response framework based on the self-association matrix and the self-distance matrix, the simple repetition of feature distribution is avoided based on the retrieval-enhanced reverse response construction of the production task information-equipment information semantic interaction response cascade feature vector, and the surface combination mapping is avoided by ensuring the intrinsic mapping logic of the production task information-equipment information semantic interaction response cascade feature vector based on the retrieval-response context, so as to realize the logical dependency mapping of the production task information-equipment information semantic interaction response cascade feature vector to the decoding target domain while maintaining the intuitive response correlation, thereby improving the distinguishable accuracy of the decoding values ​​of the speed of each production task allocation. In this way, it can better adapt to complex and changeable production and equipment management scenarios, improve the adaptability and flexibility in a dynamic environment, and can more comprehensively consider multi-dimensional factors such as the dependency relationship between production tasks and the workload prediction of equipment, thereby providing a more reasonable and flexible solution for task allocation.

[0083] In summary, the production task allocation speed adjustment module 140 is clearly explained. It uses information analysis and coding methods based on artificial intelligence to semantically encode each production task information and semantically associate the context, and to semantically encode the equipment information to be allocated. Based on the semantic interaction response characteristics of the main components between the semantic association characteristics of each production task information and the semantic coding characteristics of the equipment information, it intelligently obtains the production task allocation speed decoding value, and adjusts the processing priority of each production task. In this way, it can better adapt to complex and changeable production and equipment management scenarios, improve adaptability and flexibility in dynamic environments, and more comprehensively consider multi-dimensional factors such as the dependency between production tasks and the working status of equipment, thereby providing a more reasonable and flexible solution for task allocation.

[0084] In summary, the intelligent management system 100 for electromechanical engineering equipment based on the embodiment of the present application is explained, which first obtains the production task information of multiple production tasks and the equipment information of the electromechanical engineering equipment to be assigned, then initializes the allocation scheme of multiple production tasks on the electromechanical engineering equipment to be assigned, and finally manages the processing priorities of the multiple production tasks based on the production task information of the multiple production tasks and the equipment information. In this way, more flexible task allocation can be achieved, effectively improving the adaptability and flexibility of equipment management.

[0085] As described above, the intelligent management system 100 for electromechanical engineering equipment according to the embodiment of the present application can be implemented in various terminal devices, such as a server for intelligent management of electromechanical engineering equipment. In one example, the intelligent management system 100 for electromechanical engineering equipment according to the embodiment of the present application can be integrated into the terminal device as a software module and / or a hardware module. For example, the intelligent management system 100 for electromechanical engineering equipment can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the intelligent management system 100 for electromechanical engineering equipment can also be one of the many hardware modules of the terminal device.

[0086] Alternatively, in another example, the intelligent management system 100 for electromechanical engineering equipment and the terminal device may be separate devices, and the intelligent management system 100 for electromechanical engineering equipment may be connected to the terminal device via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0087] Figure 7 Flow chart of the intelligent management method for electromechanical engineering equipment according to an embodiment of the present application. Figure 7As shown, in the intelligent management method for electromechanical engineering equipment, it includes: S110, obtaining production task information of multiple production tasks to obtain a set of production task information; S120, obtaining equipment information of the electromechanical engineering equipment to be assigned; S130, initializing the allocation plan of the multiple production tasks on the electromechanical engineering equipment to be assigned; S140, managing the processing priorities of the multiple production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be assigned; wherein step S130 includes: semantically encoding and associating the set of production task information to obtain a set of semantic association features of production task information; semantically encoding the equipment information of the electromechanical engineering equipment to be assigned to obtain the semantic encoding features of equipment information; performing principal component feature semantic interaction response on the set of semantic association features of production task information and the semantic encoding features of equipment information to obtain a set of production task information-equipment information semantic interaction response features; based on the set of production task information-equipment information semantic interaction response features, generating a task processing priority allocation result, and sorting the processing priorities of the multiple production tasks.

[0088] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned intelligent management method for electromechanical engineering equipment have been referred to above. Figures 1 to 6 It has been introduced in detail in the description of the intelligent management system for electromechanical engineering equipment, and therefore, its repeated description will be omitted.

[0089] In summary, the intelligent management method for electromechanical engineering equipment based on the embodiment of the present application is explained, which first obtains the production task information of multiple production tasks and the equipment information of the electromechanical engineering equipment to be assigned, then initializes the allocation scheme of multiple production tasks on the electromechanical engineering equipment to be assigned, and finally manages the processing priorities of the multiple production tasks based on the production task information of the multiple production tasks and the equipment information. In this way, more flexible task allocation can be achieved, effectively improving the adaptability and flexibility of equipment management.

Claims

1. An intelligent management system for electromechanical engineering equipment, characterized in that: include: A production task information acquisition module, used to acquire production task information of multiple production tasks to obtain a set of production task information; The equipment information acquisition module is used to acquire the equipment information of the electromechanical engineering equipment to be allocated; An allocation speed initialization module, used to initialize the allocation scheme of the plurality of production tasks on the electromechanical engineering equipment to be allocated; A production task allocation speed adjustment module, used for managing the processing priorities of the plurality of production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be allocated; Among them, the production task allocation speed adjustment module includes: a production task information encoding unit, which is used to semantically encode and associate the set of production task information to obtain a set of semantic association features of production task information; an equipment information encoding unit to be assigned, which is used to semantically encode the equipment information of the electromechanical engineering equipment to be assigned to obtain equipment information semantic encoding features; a production task equipment semantic interaction response unit, which is used to perform principal component feature semantic interaction response on the set of production task information semantic association features and the equipment information semantic encoding features to obtain a set of production task information-equipment information semantic interaction response features; a processing priority management unit, which is used to generate a task processing priority allocation result based on the set of production task information-equipment information semantic interaction response features, and sort the processing priorities of the multiple production tasks.

2. The intelligent management system for electromechanical engineering equipment according to claim 1 is characterized in that: The production task information encoding unit comprises: A production task information semantic coding subunit, used for semantically coding the set of production task information to obtain a set of production task information semantic coding feature vectors; The production task information semantic association feature extraction subunit is used to pass the set of production task information semantic encoding feature vectors through a converter-based context encoder to obtain a set of production task information semantic association feature vectors as the set of production task information semantic association features.

3. The intelligent management system for electromechanical engineering equipment according to claim 2 is characterized in that: The device information encoding unit to be allocated is used to: perform semantic encoding on the device information of the electromechanical engineering equipment to be allocated to obtain a device information semantic encoding feature vector as the device information semantic encoding feature.

4. The intelligent management system for electromechanical engineering equipment according to claim 3 is characterized in that: The production task device semantic interaction response unit includes: A production task information and equipment information principal component analysis subunit, used for performing principal component analysis on the production task information semantic association feature vector and the equipment information semantic coding feature vector to obtain a set of production task information semantic principal component feature components and a set of equipment information semantic principal component feature components; The production task information-equipment information optimal matching subunit is used to perform production task information-equipment information optimal matching interaction on the set of the production task information semantic principal component feature components and the set of the equipment information semantic principal component feature components to obtain a production task information-equipment information semantic interaction response feature vector as the set of production task information-equipment information semantic interaction response features.

5. The intelligent management system for electromechanical engineering equipment according to claim 4 is characterized in that: The production task information-equipment information optimal matching subunit includes: The production task information-equipment information best matching secondary subunit is used to use each production task information semantic main component feature component in the set of the production task information semantic main component feature components as a query vector and the set of the equipment information semantic main component feature components as a query library, and match the equipment information semantic main component feature components that best match each query vector from the query library to obtain a set of best matching pairs of {production task information semantic main component feature components; equipment information semantic main component feature components}; The production task information-equipment information multi-scale interaction secondary sub-unit is used to input each best matching pair of {production task information semantic principal component feature component; equipment information semantic principal component feature component} in the set of the best matching pairs of {production task information semantic principal component feature component; equipment information semantic principal component feature component} into the multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of the best matching pairs of production task information-equipment information semantics; The production task information-equipment information semantic interaction response feature generation secondary subunit is used to cascade the set of multi-scale interactive coupling representation vectors of the production task information-equipment information semantic best match to obtain the production task information-equipment information semantic interaction response feature vector.

6. The intelligent management system for electromechanical engineering equipment according to claim 5, characterized in that: The production task information-equipment information optimal matching secondary subunit is used to: Extracting predetermined production task information semantic principal component feature components from the set of production task information semantic principal component feature components; The Mahalanobis distances between the predetermined production task information semantic principal component feature components and the equipment information semantic principal component feature components in the set are calculated to obtain a set of matching distances, wherein a pair of {production task information semantic principal component feature components; equipment information semantic principal component feature components} with the smallest matching distance in the set of matching distances is taken as the best matching pair.

7. The intelligent management system for electromechanical engineering equipment according to claim 6, characterized in that: The production task information-equipment information multi-scale interaction secondary sub-unit is used to: Respectively calculating the bi-norms of the semantic principal component feature component of the production task information and the semantic principal component feature component of the equipment information in the best matching pair of {the semantic principal component feature component of the production task information; the semantic principal component feature component of the equipment information} to obtain the norm value of the production task information and the norm value of the equipment information; Extracting a maximum value from the production task information norm value, the equipment information norm value and the modulation parameter to obtain an interaction maximum value; Respectively calculate the positional subtraction, positional dot product and positional addition between the semantic principal component feature components of the production task information and the semantic principal component feature components of the equipment information to obtain a production task information-equipment information semantic principal component difference vector, a production task information-equipment information semantic principal component dot product vector and a production task information-equipment information semantic principal component sum vector; Cascade processing is performed on the production task information-equipment information semantic principal component difference vector, the production task information-equipment information semantic principal component dot product vector and the production task information-equipment information semantic principal component sum vector to obtain a production task information-equipment information semantic principal component cascade vector; Performing one-dimensional convolution coding on the production task information-equipment information semantic principal component cascade vector to obtain a production task information-equipment information semantic principal component convolution coding vector; The production task information-equipment information semantic principal component convolution coding vector is subjected to local window-based maximum pooling processing, and the obtained production task information-equipment information semantic interaction representation vector is divided point by point by the interaction maximum value to obtain the multi-scale interaction coupling representation vector of the production task information-equipment information semantic best match.

8. The intelligent management system for electromechanical engineering equipment according to claim 7, characterized in that: The processing priority management unit includes: A production task allocation speed decoding subunit, used to obtain a set of production task allocation speed decoding values ​​based on the set of production task information-equipment information semantic interaction response features; A task processing priority allocation result generating subunit, used for arranging the individual production task allocation speed decoding values ​​in the set of production task allocation speed decoding values ​​in descending order to obtain a task processing priority allocation result; The production task processing sorting subunit is used to sort the processing priorities of the multiple production tasks based on the task processing priority allocation result.

9. The intelligent management system for electromechanical engineering equipment according to claim 8, characterized in that: The production task allocation speed decoding subunit is used to: respectively pass each production task information-equipment information semantic interaction response feature vector in the set of production task information-equipment information semantic interaction response feature vectors through a decoder-based task priority allocator to obtain a set of production task allocation speed decoding values.

10. An intelligent management method for electromechanical engineering equipment, characterized in that: include: Acquire production task information of multiple production tasks to obtain a set of production task information; Obtain equipment information of mechanical and electrical engineering equipment to be allocated; Initializing the allocation scheme of the plurality of production tasks on the electromechanical engineering equipment to be allocated; Managing the processing priorities of the plurality of production tasks based on the set of production task information and the equipment information of the electromechanical engineering equipment to be allocated; Among them, the processing priorities of the multiple production tasks are managed based on the set of production task information and the equipment information of the electromechanical engineering equipment to be assigned, including: semantically encoding and associating the set of production task information to obtain a set of semantic association features of production task information; semantically encoding the equipment information of the electromechanical engineering equipment to be assigned to obtain equipment information semantic encoding features; performing principal component feature semantic interaction response on the set of production task information semantic association features and the equipment information semantic encoding features to obtain a set of production task information-equipment information semantic interaction response features; based on the set of production task information-equipment information semantic interaction response features, generating a task processing priority allocation result, and sorting the processing priorities of the multiple production tasks.

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