A language processing method for intelligent manufacturing systems based on collaborative perception

Abstract

This invention provides a language processing method for intelligent manufacturing systems based on collaborative perception. Under static conditions, it employs a data management convergence factor based on a language utility function. Under dynamic conditions, it combines this data management convergence factor with particle swarm optimization feedback parameters to perform language processing, generate task processing, and reallocate resources, thus achieving language processing for intelligent manufacturing systems based on collaborative perception. This invention addresses key scientific issues in intelligent manufacturing systems, including intelligent modeling, precise semantic analysis, Chinese language perception, and intelligent language generation. It provides theoretical support and application guidance for improving human-computer interaction in intelligent manufacturing systems.

Description

Technical Field

[0001] The present invention belongs to the field of language processing, and in particular relates to a language processing method for an intelligent manufacturing system based on collaborative perception. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The core of language processing technology for intelligent manufacturing systems is a multi-hop wireless self-organizing network system composed of a large number of language-aware nodes deployed in the work area. Each node collaboratively perceives, collects and processes the language information of the perceived objects in the coverage area of ​​the intelligent manufacturing system.

[0004] Currently, the primary challenge is how to coordinately perceive, collect, and process information about various language application objects within a network's coverage area, perform accurate semantic analysis, and distribute this information to the intelligent manufacturing system's functional modules that require it. The theories, frameworks, and technologies related to language processing and generation are becoming a research hotspot. Leveraging the theory of language-adaptive collaborative perception to explore key technologies and applications for human-computer interaction in intelligent manufacturing systems will provide essential support for improving the intelligence and interactivity of intelligent manufacturing systems.

[0005] However, due to the complexity of intelligent manufacturing systems themselves, there is currently no mature technology for the application of their language processing and generation technologies. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a language processing method for intelligent manufacturing systems based on collaborative perception. The present invention solves the key scientific problems of intelligent manufacturing systems in intelligent modeling, precise semantic analysis, Chinese language perception and intelligent language generation, and provides theoretical support and application guidance for improving the human-computer interactivity of intelligent manufacturing systems.

[0007] According to some embodiments, the present invention adopts the following technical solutions:

[0008] A language processing method for an intelligent manufacturing system based on collaborative perception includes the following steps:

[0009] Initialize language task data, including total task completion time, energy consumption, and network load status;

[0010] Based on the initialized data, a cost function is constructed and optimized using the particle swarm optimization algorithm. Based on the mapping relationship between particles and the nonlinear characteristics of the position and velocity of each particle, the data set of the system language model is obtained.

[0011] Establish collaborative groups based on the nonlinear characteristics of language data sets, and use the cohesiveness and semantic relevance of language to set the numerical characteristics of collaborative groups to ensure that nodes can independently perceive language processing requirements.

[0012] Based on the principle that vectors are orthogonal and do not have direct mapping, the idea of ​​jointly optimizing the language's own properties and the collaborative group's feature vectors is used to jointly judge and estimate the data of related functional nodes within the group.

[0013] Establish a system node collaborative coverage model and set constraints for the node collaborative coverage model;

[0014] Based on the collaborative coverage model, a dynamic balancing scheme is used to improve the connection strategy between system language generation tasks;

[0015] Configuring the self-adjustment and self-configuration characteristics of system routing in the case of multi-source language fusion, and performing intra-cluster coverage control based on the characteristics;

[0016] From the perspective of multi-source similar information fusion, the language generation requirements with high correlation and strong spatiotemporal correlation are integrated;

[0017] Under static conditions, a data management convergence factor based on language utility function is adopted; under dynamic conditions, a combination of a data management convergence factor based on language utility function and particle swarm optimization feedback parameters is adopted to carry out language processing, generate task processing and resource redistribution, and realize language processing of intelligent manufacturing system based on collaborative perception.

[0018] As an optional implementation, the process of constructing the cost function includes:

[0019] The cost function is constructed with the minimum system energy consumption as the objective function and the maintenance of network semantic node connectivity as the constraint condition.

[0020] As an optional implementation, the process of establishing a system node collaborative coverage model and setting constraints for the node collaborative coverage model includes:

[0021] Based on the requirements of system network logic mapping coverage and energy consumption minimization, on plane A containing n network semantic nodes n The spatial constraint mechanism is used, and the logical mapping radius of each network semantic node is d; the network nodes are on plane A. n The uniform distribution is followed, and the total semantic interference of the network is reduced by increasing the plane.

[0022] As an optional implementation, based on the collaborative coverage model, the process of improving the connection strategy between system language generation tasks using a dynamic balancing solution includes:

[0023] Let l i,l j ∈R 2 Represents the network semantic node v i ,v j The position of v i ≠v j ; Define network semantic node v i and v j The direct connectivity is d; when there is a non-empty set in the network node group P, set the network semantic node v i and v j There are multi-hop connections between i and v j The information between nodes can be effectively addressed and transmitted through the node group P; given the number of nodes is n, let l scr ,l1,l2,...,l n-1 ∈R 2 Represent the source node and nodes v1, v2, ..., v respectively -1 , and let V be the set of all network semantic nodes; set N(l i ) is l i The maximum number of nodes in the logical mapping range with d as the center and d as the mapping radius, and N(l i )={v z :v z ∈Vand‖l i -l z ‖≤d}; all network semantic nodes have the same logical mapping coverage radius and mapping correlation. The adjustability of system nodes is used to improve the coverage quality of sensitive area monitoring and realize inter-node collaboration and adjustment under energy-constrained conditions.

[0024] As an optional implementation, the process of configuring the self-adjustment and self-configuration features of system routing in the case of multi-source language fusion and performing intra-cluster logical mapping coverage control includes:

[0025] When merging multiple source languages, without considering the semantic boundary effect, the third-party network node v k Able to communicate with network nodes v j Achieve direct interconnection but cannot connect to network nodes v i The upper bound for achieving direct interconnection is A(v j )-[A(v i )∩A(v j )], thereby realizing the self-adjustment and self-configuration characteristics of the system routing. Set the existing positions to be l i ,l j ∈R 2 Network nodes v i and v j , and ||li -l j ||=d; ignoring the additional space of the last network node, the lower bound of the spatial position of the network node is Let T src is the total number of transmissions required when using the multi-hop method under error-free conditions, assuming that n network nodes are randomly located in area A according to uniform distribution n Within, the logical mapping coverage radius is d. When a network node transmits T = k times to realize a single bit broadcast to other (n-1) network nodes, its probability upper bound is G(k,n)(d 2 / A n ) n-1 ,in and This enables logical mapping coverage control within the cluster.

[0026] As an optional implementation method, from the perspective of multi-source similar information fusion, the process of integrating language generation requirements with high correlation and strong spatiotemporal correlation includes:

[0027] Based on the relationship between semantic nodes and the semantic stability of the system, the probability of the RTS initiated by the semantic node of the intelligent manufacturing system network for flow f is defined as P nf , define the probability of successful data transmission for flow f as P sf , initialize the energy consumption and load status of semantic nodes;

[0028] According to the initialized parameters, a cost function is constructed in the system perception strategy process based on precise semantic analysis, and the particle swarm optimization algorithm is used for optimization to obtain the intermediate state analysis model of the semantic node;

[0029] Based on the obtained semantic node intermediate state analysis model, a collaborative group of source semantic node sets and target semantic node sets is established to perform data collaborative perception evolution modeling and obtain a system perception strategy based on precise semantic analysis.

[0030] Based on the obtained precise semantic analysis system perception strategy and the language generation mechanism, the channel status of the source semantic node before initiating the RTS is dynamically controlled, and the semantic relevance and spatiotemporal correlation of the destination semantic node are dynamically adjusted to ensure the fusion performance required by multi-source language generation.

[0031] From the perspective of multi-source similar information fusion, the language generation requirements with correlation greater than the set value and spatiotemporal correlation intensity exceeding the preset value are integrated.

[0032] As a further step, the particle swarm optimization algorithm is used to optimize and obtain the intermediate state analysis model of the semantic node, including the following steps:

[0033] Define the probability that the destination semantic node becomes a hidden node due to being out of the listening range of the source semantic node as Y t , in the steady state Y t With P nf and P sf The relationship is P sf With P nf The ratio of represents the source semantic node's listening efficiency for flow f and the probability of data correctness. The basic framework of the semantic node intermediate state analysis model is characterized by the source semantic node listening effectiveness.

[0034] Based on the particle swarm optimization algorithm, the maximum number of RTS initiated by the network before the data packet is discarded is set to u r , construct the state transition process of the semantic node intermediate state analysis model.

[0035] Furthermore, based on the obtained semantic node intermediate state analysis model, a collaborative group of a source semantic node set and a destination semantic node set is established, and a process of data collaborative perception evolution modeling is performed, including:

[0036] Set Y t Indicates the probability of a hidden node appearing each time a semantic node initiates RTS, 1-Y t It represents the probability of a data packet achieving normal addressing each time an RTS is initiated. Based on the semantic node intermediate state analysis model, the mapping relationship between the source semantic node set and the destination semantic node set is abstracted, and the nonlinear mapping between the sets is used to construct a collaborative group.

[0037] By utilizing the vector characteristics and spatiotemporal correlation of the collaborative group, the number of RTS failures initiated by the semantic network is controlled within a reasonable range. Based on the performance requirements of multi-information fusion perception, a system perception strategy based on precise semantic analysis is obtained.

[0038] As an optional implementation method, under static conditions, a data management convergence factor based on a language utility function is adopted; under dynamic conditions, a method combining a data management convergence factor based on a language utility function with a particle swarm optimization feedback parameter is adopted. The process of language processing, task generation, and resource reallocation includes:

[0039] Taking advantage of the fact that mathematical spaces are locally compact, we optimize the perception strategy models of the precise semantic analysis system based on static and dynamic conditions, respectively. Based on the characteristics of locally compact spaces, we design a multi-objective optimization genetic algorithm solution.

[0040] The adjustability of semantic nodes is used to improve the coverage quality of language-sensitive area monitoring, and the characteristics of language utility functions are used to achieve collaboration and adjustment between semantic nodes under energy-constrained conditions;

[0041] The Poisson Boolean model is used to set the probability lower bound of the network connectivity, and to set the maximum value of semantic elements transmitted simultaneously by the system and the language processing limit;

[0042] The system's comprehensive packet loss rate is set. Based on the maximum number of semantic elements transmitted simultaneously by the system and the language processing limit, language processing and generation task processing and resource redistribution are performed based on a combination of the data management convergence factor of the language utility function and the particle swarm optimization feedback parameter to meet the language collaborative perception needs of all nodes in the system.

[0043] Based on the initiation characteristics of multi-source languages ​​and the sensitivity of data collaborative perception mechanisms, the accuracy of language collaborative perception decisions is described;

[0044] Based on the accurate description of language collaborative perception decision-making, language processing, task generation processing and resource reallocation processing are realized to complete the language processing of intelligent manufacturing system based on collaborative perception.

[0045] Furthermore, the process of setting the probability lower bound of the network connectivity through the Poisson Boolean model and setting the maximum value of semantic elements transmitted simultaneously by the system and the language processing limit includes:

[0046] Assume that the probability of connecting semantic nodes in the interval 0≤x≤D is P oD (x) = P o (x)+P o (Dx)-P o (x)P o (Dx), where The average connection probability of a semantic node occupying the language utility function is P oD The lower bound of the node position depends on the worst state, that is, P oD+ =inf 0≤x≤D {P oD (x)}=2P o (D / 2)-P o 2 (D / 2); because P o The lower bound of (x) is So when 2r≤D≤4r, P o (D / 2)=1-e -λr -λDe -λr / 2 and P oD The lower bound of P oD + =1-(λD / 2+1) 2 e -2λrBased on the above characteristics, the data management convergence factor of the language utility function is used to improve the convergence stability of language processing;

[0047] Set the average number of semantic nodes sent in a stable state n y =b l n t , where b l is the network congestion coefficient. To ensure the normal initiation of RTS, the minimum space requirement is Among them, M c is the carrier sense capacity, which indicates the maximum number of RTSs that can be initiated without packet collision. Combined with the particle swarm optimization feedback parameters, we have nodes can successfully transmit data, RTS initiated simultaneously, where M b It is the data transmission capacity. Based on the above characteristics, the maximum value of semantic elements that the system can transmit simultaneously and the language processing limit of the system are obtained.

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

[0049] This invention deeply combines the theory of adaptive collaborative perception with language processing technology for intelligent manufacturing systems. The high-precision, low-complexity multi-source language processing technology proposed in this invention, based on the theory of adaptive collaborative perception, can effectively promote the intelligence and interactivity of intelligent manufacturing systems.

[0050] The present invention proposes an algorithm for calculating the fault tolerance and scalability of language aggregation functions in the collaborative perception subsystem of the multi-source language adaptive collaborative perception solution of the intelligent manufacturing system, determines the node energy consumption parameters and load indicators, and solves the problem of applying language processing technology to language perception data query processing.

[0051] This paper designs a scheduling algorithm that supports high-reliability strategies, elastic computing strategies, dynamic resource allocation strategies, and disaster recovery and backup strategies. It achieves the goal of collaboratively controlling language information to detect the language generation capabilities of nodes in a certain direction using a hop-by-hop routing approach, and proposes a strategy for updating the intelligent manufacturing system model and optimizing language generation.

[0052] This paper uses a language feedback mechanism and particle swarm optimization to establish a system-oriented multi-source Chinese language fusion model. It also constructs a new Chinese language feedback prediction algorithm that predicts grammar and semantics for numerical prediction, further enhancing the interactivity of intelligent manufacturing systems.

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0055] Figure 1 This is an overall design diagram of language processing for an intelligent manufacturing system according to an embodiment;

[0056] Figure 2 This is a schematic diagram of a language processing process of an intelligent manufacturing system based on collaborative perception according to an embodiment;

[0057] Figure 3 A relationship diagram between a current node and neighboring nodes in an adaptive collaborative perception semantic analysis model of an embodiment;

[0058] Figure 4 The present invention is a schematic diagram of a state transition process of a system perception strategy based on precise semantic analysis in an embodiment. DETAILED DESCRIPTION

[0059] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0060] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0061] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0062] In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0063] Example 1

[0064] As described in the background technology, in order to address the key technologies and applications of human-computer interaction in intelligent manufacturing systems, and to solve the problems of low system adaptive cognitive ability, unbalanced load, inaccurate semantic analysis, weak predictability of multi-source Chinese language, and insufficient dynamics of language generation balancing algorithms, this embodiment provides a language processing method for intelligent manufacturing systems based on collaborative perception to achieve the intelligence and interactivity of intelligent manufacturing systems.

[0065] A language processing method for an intelligent manufacturing system based on collaborative perception includes the following steps:

[0066] Step S1: Initialize the total system task completion time, energy consumption and network load status;

[0067] Step S2: Based on the initialized parameter data, a cost function is constructed and optimized using the particle swarm optimization algorithm. Based on the mapping relationship between particles and the nonlinear characteristics of the position and velocity of each particle, a data set of the system language model is obtained;

[0068] based on Figure 1 The data collaborative perception subsystem takes minimizing system energy consumption as the objective function and maintaining the connectivity of network semantic nodes as the constraint condition, and constructs the same cost function in its various modules.

[0069] Step S3: Establish a collaborative group based on the nonlinear characteristics of the language data set inherited from the particle swarm. Leveraging the cohesiveness of the language and the relevance of semantics, the numerical characteristics of the collaborative group are set to ensure that nodes can independently perceive language processing requirements. Based on the principle that vectors are orthogonal and do not have direct mappings, the idea of ​​jointly optimizing the language's own properties and the collaborative group's feature vectors is used to jointly determine and estimate the data of relevant functional nodes within the group.

[0070] The above nonlinear characteristics refer to the nonlinearity of the mapping relationship between nodes reflected by the particle swarm algorithm.

[0071] The numerical properties of collaborative groups are set using the cohesiveness of language and the relevance of semantics.

[0072] Step S4: Establish a system semantic node collaborative coverage model and set constraints for the node collaborative coverage model;

[0073] Specifically, based on the requirements of system network logic mapping coverage and energy consumption minimization, on plane A containing n network semantic nodes n The spatial constraint mechanism is used, and the logical mapping radius of each network semantic node is d; the network nodes are on plane A. n The uniform distribution is followed, and the total semantic interference of the network is reduced by increasing the plane.

[0074] Step S5: Based on the collaborative coverage model, a dynamic balancing solution is used to improve the connection strategy between system language generation tasks.

[0075] In this embodiment, let l i ,l j ∈R 2 Represents the network semantic node v i ,v j The position of v i ≠vj ; Define network semantic node v i and v j The direct connectivity is d; when there is a non-empty set in the network node group P, set the network semantic node v i and v j There are multi-hop connections between i and v j The information between nodes can be effectively addressed and transmitted through the node group P; given the number of nodes is n, let l scr ,l1,l2,...,l n-1 ∈R 2 Represent the source node and nodes v1, v2, ..., v respectively -1 , and let V be the set of all network semantic nodes; set N(l i ) is l i The maximum number of nodes in the logical mapping range with d as the center and d as the mapping radius, and N(l i )={v z :v z ∈Vand||l i -l z ||≤d}; all network semantic nodes have the same logical mapping coverage radius and mapping correlation. The adjustability of system nodes is used to improve the coverage quality of sensitive area monitoring and achieve inter-node collaboration and adjustment under energy-constrained conditions.

[0076] Step S6: configuring the system routing self-adjustment and self-configuration characteristics in the case of multi-source language fusion, and performing intra-cluster coverage control based on the characteristics;

[0077] Based on Figure 2 The framework shown in the figure shows that when merging multiple source languages, without considering the semantic boundary effect, the third-party network node v k Able to communicate with network nodes v j Achieve direct interconnection but cannot connect to network nodes v i The upper bound for achieving direct interconnection is A(v j )-[A(v i )∩A(v j )], thereby realizing the self-adjustment and self-configuration characteristics of the system routing. Set the existing positions to be l i ,l j ∈R 2 Network nodes v i and v j , and ||l i -l j ||=d; ignoring the additional space of the last network node, the lower bound of the spatial position of the network node is Let Tsrc is the total number of transmissions required when using the multi-hop method under error-free conditions, assuming that n network nodes are randomly located in area A according to uniform distribution n Within, the logical mapping coverage radius is d. When a network node transmits T = k times to realize a single bit broadcast to other (n-1) network nodes, its probability upper bound is G(k,n)(d 2 / A n ) n-1 ,in and This enables logical mapping coverage control within the cluster.

[0078] Step S7: From the perspective of multi-source similar information fusion, integrate language generation requirements with high correlation and strong spatiotemporal correlation;

[0079] Specifically, this step includes:

[0080] Step S71: Based on Figure 3 The relationship between the semantic nodes shown in the figure is based on the semantic stability of the system. The probability of the semantic node of the intelligent manufacturing system network initiating RTS for the flow f is defined as P nf , define the probability of successful data transmission for flow f as P sf , initialize the energy consumption and load status of semantic nodes;

[0081] Step S72: constructing a cost function in the system perception strategy process based on precise semantic analysis according to the initialized parameters, and optimizing it using a particle swarm optimization algorithm to obtain an intermediate state analysis model of the semantic node;

[0082] Step S72 further comprises:

[0083] Step S7201: Define the probability that the destination semantic node becomes a hidden node due to being out of the listening range of the source semantic node as Y t , in the steady state Y t With P nf and P sf The relationship is P sf With P nf The ratio of represents the source semantic node's listening efficiency for stream f and the probability of data correctness. The basic framework of the semantic node intermediate state analysis model is characterized by the source semantic node listening effectiveness.

[0084] Step S7202: Based on the particle swarm optimization algorithm, the maximum number of RTSs initiated by the network before the data packet is discarded is set to u r ,based on Figure 4 Construct the state transition process of the semantic node intermediate state analysis model.

[0085] Step S73: Based on the obtained semantic node intermediate state analysis model, a collaborative group of the source semantic node set and the target semantic node set is established to perform data collaborative perception evolution modeling to obtain a system perception strategy based on precise semantic analysis;

[0086] Step S73 further comprises:

[0087] Step S7301: Set Y t Indicates the probability of a hidden node appearing each time a semantic node initiates RTS, 1-Y t It represents the probability of a data packet achieving normal addressing each time an RTS is initiated. Based on the semantic node intermediate state analysis model, the mapping relationship between the source semantic node set and the destination semantic node set is abstracted, and the nonlinear mapping between the sets is used to construct a collaborative group.

[0088] Step S7302: Utilize the vector characteristics and spatiotemporal correlation of the collaborative group to control the number of RTS failures initiated by each functional module of the semantic network within a reasonable range. Based on the performance requirements of multi-information fusion perception, a system perception strategy based on precise semantic analysis is obtained.

[0089] In this embodiment, performance evaluation is mainly based on perception accuracy and node connection capability, and the resulting perception strategy based on precise semantic analysis is the optimized algorithm architecture.

[0090] Step S74: Based on the obtained precise semantic analysis system perception strategy and the language generation mechanism, the channel state of the source semantic node before initiating the RTS is dynamically controlled, and the semantic relevance and spatiotemporal correlation of the destination semantic node are dynamically adjusted to ensure the fusion performance of multi-source language generation requirements;

[0091] This embodiment mainly implements adjustment and control based on the cohesiveness of natural language information, the relevance of words, and the dynamic mapping of grammar.

[0092] Step S75: Complete the fusion of language generation requirements with high correlation and strong spatiotemporal correlation from the perspective of multi-source similar information fusion.

[0093] Step S8: Under static conditions, a data management convergence factor based on the language utility function is used; under dynamic conditions, a combination of the data management convergence factor based on the language utility function and the particle swarm optimization feedback parameter is used to perform language processing, generate task processing, and reallocate resources. This achieves language processing for intelligent manufacturing systems based on collaborative perception.

[0094] Specifically, this step includes:

[0095] Step S81: Based on the characteristic that the mathematical space R(N) covered by the present invention is a locally compact space, the perception strategy models of the precise semantic analysis system based on static conditions and dynamic conditions are optimized respectively, and a solution of a multi-objective optimization genetic algorithm is designed according to the characteristic of the locally compact space;

[0096] Step S82: Based on Figure 4 , let ∪A=N. For any n∈N, we can define Fn={1,2,…,n} and Un={{A}∪(A / Fn):A∈A}∪{{x}:x∈Fn}, then Un is an open cover of R(N). Based on this feature, the adjustability of semantic nodes is used to improve the coverage quality of language-sensitive area monitoring, and the characteristics of language utility functions are used to achieve collaboration and adjustment between semantic nodes under energy-constrained conditions;

[0097] Step S82 further comprises:

[0098] Step S8201: For any x∈R(N), when x∈Fn, st(x,Un)={x}, and x∈A, st(x,Un)={x}∪(x / Fn), so {Un} is an expansion of R(N). Let K be a compact subspace of R(N). Since A is a closed discrete subspace of R(N), K∩A is a finite set. Therefore, K is a countable set in R(N) and K is measurable, so K has a countable neighborhood basis in R(N). Based on this property, the adjustability of semantic nodes is achieved, and the coverage quality of language-sensitive area monitoring is improved.

[0099] Step S8202: Assume that N is a countably dense subset of R(N). R(N) is a separable space. If R(N) has a point-countable basis, then R(N) must have a countable basis. Therefore, A, a subspace of R(N), has a countable basis. This contradicts the assumption that A is an uncountably closed discrete subspace of R(N). Therefore, R(N) does not have a point-countable basis. Based on this property, collaboration and coordination between semantic nodes are achieved under energy-constrained conditions.

[0100] Step S83: The network topology of the intelligent manufacturing system designed in this embodiment is set to belong to the Gillman-Jerison space. The probability lower bound of the network connectivity can be set using the Poisson Boolean model. Based on this characteristic, the maximum value of the semantic elements transmitted simultaneously and the language processing limit of the system are designed;

[0101] Step S83 further comprises:

[0102] Step S8301: Set the probability of semantic nodes being connected within the interval 0≤x≤D to P oD (x) = P o (x)+P o (Dx)-Po (x)P o (Dx), where The average connection probability of a semantic node occupying the language utility function is P oD The lower bound of the node position depends on the worst state, that is, P oD + =inf 0≤x≤D {P oD (x)}=2P o (D / 2)-P o 2 (D / 2). Because P o The lower bound of (x) is So when 2r≤D≤4r, P o (D / 2)=1-e -λr -λDe -λr / 2 and P oD The lower bound of P oD + =1-(λD / 2+1) 2 e -2λr Based on this characteristic, the data management convergence factor of the language utility function is used to improve the language processing convergence stability.

[0103] Step S8302: Set the average value n of the number of semantic nodes sent in a stable state y =b l n t , where b l is the network congestion coefficient. To ensure the normal initiation of RTS, the minimum space requirement is Among them, M c is the carrier sense capacity, which indicates the maximum number of RTSs that can be initiated without packet collision. Combined with the particle swarm optimization feedback parameters, we have nodes can successfully transmit data, RTS initiated simultaneously, where M b is the data transmission capacity. Based on this characteristic, the maximum value of the semantic elements that the system can transmit simultaneously and the language processing limit of the system are obtained.

[0104] Step S84: Set the system comprehensive packet loss rate to According to the maximum value of semantic elements transmitted simultaneously by the system and the language processing limit value, the data management convergence factor based on the language utility function and the particle swarm optimization feedback parameter are combined to realize language processing and generation task processing and resource redistribution to meet the language collaborative perception needs of all nodes in the system.

[0105] Based on the initiation characteristics of multi-source languages ​​and the sensitivity of data collaborative perception mechanisms, the accurate description of language collaborative perception decisions is achieved;

[0106] In this embodiment, description is achieved based on the cohesion of multi-source natural language information, the relevance of words, and the dynamic mapping of grammar.

[0107] Step S85: Based on the accuracy description of language collaborative perception decision, language processing, task generation processing and resource reallocation processing are realized to complete the language processing of the intelligent manufacturing system based on collaborative perception.

[0108] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made by those skilled in the art that fall within the spirit and principles of the present invention and do not require creative effort are intended to be within the scope of protection of the present invention.

Claims

1. A language processing method for an intelligent manufacturing system based on collaborative perception, characterized by: The following steps are involved: Initialize language task data, including total task completion time, energy consumption, and network load status; Based on the initialized data, a cost function is constructed and optimized using the particle swarm optimization algorithm. Based on the mapping relationship between particles and the nonlinear characteristics of the position and velocity of each particle, the data set of the system language model is obtained. Establish collaborative groups based on the nonlinear characteristics of language data sets, and use the cohesiveness and semantic relevance of language to set the numerical characteristics of collaborative groups to ensure that nodes can independently perceive language processing requirements. Based on the principle that vectors are orthogonal and do not have direct mapping, the idea of ​​jointly optimizing the language's own properties and the collaborative group's feature vectors is used to jointly judge and estimate the data of related functional nodes within the group. Establish a system node collaborative coverage model and set constraints for the node collaborative coverage model; Based on the collaborative coverage model, a dynamic balancing scheme is used to improve the connection strategy between system language generation tasks; Configuring the self-adjustment and self-configuration characteristics of system routing in the case of multi-source language fusion, and performing intra-cluster coverage control based on the characteristics; From the perspective of multi-source similar information fusion, the language generation requirements with high correlation and strong spatiotemporal correlation are integrated; Under static conditions, a data management convergence factor based on language utility function is adopted; under dynamic conditions, a combination of a data management convergence factor based on language utility function and particle swarm optimization feedback parameters is adopted to carry out language processing, generate task processing and resource redistribution, and realize language processing of intelligent manufacturing system based on collaborative perception.

2. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 1, characterized in that: The process of constructing the cost function includes: The cost function is constructed with the minimum system energy consumption as the objective function and the maintenance of network semantic node connectivity as the constraint condition.

3. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 1, characterized in that: The process of establishing a system node collaborative coverage model and setting constraints for the node collaborative coverage model includes: Based on the requirements of system network logic mapping coverage and energy consumption minimization, on plane A containing n network semantic nodes n The spatial constraint mechanism is used, and the logical mapping radius of each network semantic node is d; the network nodes are on plane A. n The uniform distribution is followed, and the total semantic interference of the network is reduced by increasing the plane.

4. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 1, characterized in that: Based on the collaborative coverage model, the process of improving the connection strategy between system language generation tasks using a dynamic balancing solution includes: Let l i ,l j ∈R 2 Represents the network semantic node v i ,v j The position of v i ≠v j ; Define network semantic node v i and v j The direct connectivity is d; when there is a non-empty set in the network node group P, set the network semantic node v i and v j There are multi-hop connections between i and v j The information between nodes can be effectively addressed and transmitted through the node group P; given the number of nodes is n, let l scr ,l1,l2,...,l n-1 ∈R 2 Represent the source node and nodes v1, v2, ..., v respectively -1 , and let V be the set of all network semantic nodes; set N(l i ) is l i The maximum number of nodes in the logical mapping range with d as the center and d as the mapping radius, and N(l i )={v z :v z ∈Vand||l i -l z ||≤d}; all network semantic nodes have the same logical mapping coverage radius and mapping correlation. The adjustability of system nodes is used to improve the coverage quality of sensitive area monitoring and achieve inter-node collaboration and adjustment under energy-constrained conditions.

5. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 1, characterized in that: The process of configuring the self-adjustment and self-configuration features of system routing in the case of multi-source language fusion and performing logical mapping coverage control within the cluster includes: When merging multiple source languages, without considering the semantic boundary effect, the third-party network node v k Able to communicate with network nodes v j Achieve direct interconnection but cannot connect to network nodes v i The upper bound for achieving direct interconnection is A(v j )-[A(v i )∩A(v j )], thereby realizing the characteristics of self-adjustment and self-configuration of system routing; Set the existing positions to be l i ,l j ∈R 2 Network nodes v i and v j , and ||l i -l j ||=d; ignoring the additional space of the last network node, the lower bound of the spatial position of the network node is Let T src is the total number of transmissions required when using the multi-hop method under error-free conditions, assuming that n network nodes are randomly located in area A according to uniform distribution n Within, the logical mapping coverage radius is d. When a network node transmits T = k times to realize a single bit broadcast to other (n-1) network nodes, its probability upper bound is G(k,n)(d 2 / A n ) n-1 ,in and This enables logical mapping coverage control within the cluster.

6. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 1, characterized in that: From the perspective of multi-source similar information fusion, the process of integrating language generation requirements with high correlation and strong spatiotemporal correlation includes: Based on the relationship between semantic nodes and the semantic stability of the system, the probability of the RTS initiated by the semantic node of the intelligent manufacturing system network for flow f is defined as P nf , define the probability of successful data transmission for flow f as P sf , initialize the energy consumption and load status of semantic nodes; According to the initialized parameters, a cost function is constructed in the system perception strategy process based on precise semantic analysis, and the particle swarm optimization algorithm is used for optimization to obtain the intermediate state analysis model of the semantic node; Based on the obtained semantic node intermediate state analysis model, a collaborative group of source semantic node sets and target semantic node sets is established to perform data collaborative perception evolution modeling and obtain a system perception strategy based on precise semantic analysis. Based on the obtained precise semantic analysis system perception strategy and the language generation mechanism, the channel status of the source semantic node before initiating the RTS is dynamically controlled, and the semantic relevance and spatiotemporal correlation of the destination semantic node are dynamically adjusted to ensure the fusion performance required by multi-source language generation. From the perspective of multi-source similar information fusion, the language generation requirements with correlation greater than the set value and spatiotemporal correlation intensity exceeding the preset value are integrated.

7. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 6, characterized in that: The process of optimizing the intermediate state analysis model of the semantic node using the particle swarm optimization algorithm includes: Define the probability that the destination semantic node becomes a hidden node due to being out of the listening range of the source semantic node as Y t , in the steady state Y t With P nf and P sf The relationship is P sf With P nf The ratio of represents the source semantic node's listening efficiency for flow f and the probability of data correctness. The basic framework of the semantic node intermediate state analysis model is characterized by the source semantic node listening effectiveness. Based on the particle swarm optimization algorithm, the maximum number of RTS initiated by the network before the data packet is discarded is set to u r , construct the state transition process of the semantic node intermediate state analysis model.

8. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 6, characterized in that: Based on the obtained semantic node intermediate state analysis model, a collaborative group of source semantic node sets and target semantic node sets is established. The process of data collaborative perception evolution modeling includes: Set Y t Indicates the probability of a hidden node appearing each time a semantic node initiates RTS, 1-Y t It represents the probability of a data packet achieving normal addressing each time an RTS is initiated. Based on the semantic node intermediate state analysis model, the mapping relationship between the source semantic node set and the destination semantic node set is abstracted, and the nonlinear mapping between the sets is used to construct a collaborative group. By utilizing the vector characteristics and spatiotemporal correlation of the collaborative group, the number of RTS failures initiated by the semantic network is controlled within a reasonable range. Based on the performance requirements of multi-information fusion perception, a system perception strategy based on precise semantic analysis is obtained.

9. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 1, characterized in that: Under static conditions, a data management convergence factor based on the language utility function is adopted; under dynamic conditions, a combination of the data management convergence factor based on the language utility function and the particle swarm optimization feedback parameter is adopted. The process of language processing, generation task processing and resource reallocation includes: Taking advantage of the fact that mathematical spaces are locally compact, we optimize the perception strategy models of the precise semantic analysis system based on static and dynamic conditions, respectively. Based on the characteristics of locally compact spaces, we design a multi-objective optimization genetic algorithm solution. The adjustability of semantic nodes is used to improve the coverage quality of language-sensitive area monitoring, and the characteristics of language utility functions are used to achieve collaboration and adjustment between semantic nodes under energy-constrained conditions; The Poisson Boolean model is used to set the probability lower bound of the network connectivity, and to set the maximum value of semantic elements transmitted simultaneously by the system and the language processing limit; The system's comprehensive packet loss rate is set. Based on the maximum number of semantic elements transmitted simultaneously by the system and the language processing limit, language processing and generation task processing and resource redistribution are performed based on a combination of the data management convergence factor of the language utility function and the particle swarm optimization feedback parameter to meet the language collaborative perception needs of all nodes in the system. Based on the initiation characteristics of multi-source languages ​​and the sensitivity of data collaborative perception mechanisms, the accuracy of language collaborative perception decisions is described; Based on the accurate description of language collaborative perception decision-making, language processing, task generation processing and resource reallocation processing are realized to complete the language processing of intelligent manufacturing system based on collaborative perception.

10. The language processing method for an intelligent manufacturing system based on collaborative perception according to claim 9, characterized in that: The process of setting the probability lower bound of network connectivity through the Poisson Boolean model and setting the maximum value of semantic elements transmitted simultaneously by the system and the language processing limit includes: Assume that the probability of connecting semantic nodes in the interval 0≤x≤D is P oD (x) = P o (x)+P o (Dx)-P o (x)P o (Dx), where The average connection probability of a semantic node occupying the language utility function is P oD The lower bound of the node position depends on the worst state, that is, P oD + =inf 0≤x≤D {P oD (x)}=2P o (D / 2)-P o 2 (D / 2); because P o The lower bound of (x) is So when 2r≤D≤4r, P o (D / 2)=1-e -λr -λDe -λr / 2 and P oD The lower bound of P oD + =1-(λD / 2+1) 2 e -2λr Based on the above characteristics, the data management convergence factor of the language utility function is used to improve the convergence stability of language processing; Set the average number of semantic nodes sent in a stable state n y =b l n t , where b l is the network congestion coefficient. To ensure the normal initiation of RTS, the minimum space requirement is Among them, M c is the carrier sense capacity, which indicates the maximum number of RTSs that can be initiated without packet collision. Combined with the particle swarm optimization feedback parameters, we have nodes can successfully transmit data, RTS initiated simultaneously, where M b It is the data transmission capacity. Based on the above characteristics, the maximum value of semantic elements that the system can transmit simultaneously and the language processing limit of the system are obtained.

Images