Multi-node data resource allocation method and system based on industrial Internet of Things

Abstract

The present invention discloses a multi-node data resource allocation method and system based on industrial Internet of Things, which relates to the field of industrial Internet of Things data processing technology, including obtaining multiple load nodes for running data services, obtaining multiple maximum load characteristics in the load nodes for supporting the operation of data services, and building a node allocation model based on the maximum load characteristics; obtaining data resources, obtaining the initial service requirements corresponding to the data resources according to the data resources, configuring the initial load nodes, and allocating the data resources to the initial load nodes; after the data resources flow for a preset time period, obtaining multiple real-time load characteristics of the initial load nodes, and judging whether there are real-time load characteristics exceeding the corresponding preset threshold range; if so, configuring the target load node, and allocating the data resources to the target load node. The present invention has the advantages of scientific allocation, dynamic adjustment and precise matching.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things data processing, and in particular to a multi-node data resource allocation method and system based on industrial Internet of Things. Background Art

[0002] With the rapid development of the Industrial Internet of Things (IIoT), more and more industrial equipment and systems are connected to the network, realizing data collection, transmission, analysis, processing and storage. This progress has greatly improved production efficiency, reduced operating costs, and provided strong technical support for the transformation of industrial intelligence.

[0003] However, with the explosive growth of data volume, there are many problems in the allocation of data resources in the existing IIoT environment. First, the lack of real-time status monitoring of load nodes can easily lead to overload of some nodes, while other load nodes are idle or inefficient, resulting in resource waste; second, with the dynamic changes in production demand, the initial data resource allocation plan may quickly become inapplicable, affecting the stability and performance of the entire system; third, there are many types of data services in IIoT, and each service has different requirements for computing resources, storage resources, network bandwidth, etc. How to accurately match the most suitable load node according to the characteristics of data resources and their corresponding service requirements is also one of the challenges facing current technology. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a multi-node data resource allocation method and system based on industrial Internet of Things.

[0005] A multi-node data resource allocation method based on the industrial Internet of Things includes: obtaining multiple load nodes for running data services, obtaining multiple maximum load characteristics in the load nodes for supporting the operation of data services, and building a node allocation model based on the maximum load characteristics; obtaining data resources, obtaining initial service requirements corresponding to the data resources according to the data resources, configuring initial load nodes based on the node allocation model and the initial service requirements, and allocating the data resources to the initial load nodes; after the data resources flow for a preset time period, obtaining multiple real-time load characteristics of the initial load nodes, and judging whether there are real-time load characteristics exceeding the corresponding preset threshold range based on the node allocation model; if so, configuring a target load node based on the node allocation model and the multiple real-time load characteristics, and allocating the data resources to the target load node.

[0006] Optionally, the method for constructing a node allocation model includes: obtaining the first load node, the second load node, ..., the i-th load node; obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the j-th maximum load characteristic of the first load node, obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the j-th maximum load characteristic of the second load node, ..., obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the j-th maximum load characteristic of the i-th load node; constructing a node allocation model based on the obtained i load nodes and the j maximum load characteristics of each load node.

[0007] Optionally, configuring the initial load node based on the node allocation model and the initial service requirements includes: obtaining multiple demand indicators for different load characteristics according to the initial service requirements; calculating comprehensive performance indicators of multiple load nodes in turn according to the multiple demand indicators; and obtaining the load node corresponding to the largest comprehensive performance indicator and using it as the initial load node.

[0008] Optionally, the comprehensive performance indicators of multiple load nodes are calculated in sequence according to multiple demand indicators, including: , ;in, is the comprehensive performance index of the nth load node, is the weight of the mth performance indicator, To transform the demand index into a function of performance index, is the mth maximum load characteristic of the nth load node, is the demand indicator of the initial service demand for the mth load characteristic.

[0009] Optional, The expressions include: .

[0010] Optionally, determining whether there is a real-time load feature that exceeds a corresponding preset threshold range based on the node allocation model includes: obtaining multiple preset threshold ranges corresponding to multiple load features of the initial load node according to the node allocation model, the preset threshold range including a maximum threshold point and a minimum threshold point; determining in turn whether all of the multiple real-time load features are less than or equal to the corresponding maximum threshold point or greater than or equal to the corresponding minimum threshold point; if so, there is no real-time load feature that exceeds the corresponding preset threshold range; otherwise, there is a real-time load feature that exceeds the corresponding preset threshold range.

[0011] Optionally, configuring the target load node based on the node allocation model and multiple real-time load characteristics includes: obtaining multiple load nodes and multiple maximum load characteristics corresponding to the load nodes based on the node allocation model; screening out multiple load nodes that meet the real-time load characteristics from the multiple load nodes, and in this way, screening out multiple load nodes that meet the multiple real-time load characteristics and forming a pre-target load node set; selecting the load node closest to the initial load node in the pre-target load node set as the target load node.

[0012] A multi-node data resource allocation system based on the industrial Internet of Things is also provided. The system is used to implement a multi-node data resource allocation method based on the industrial Internet of Things. The system includes a management platform, a sensor network platform and an object platform that are communicatively connected in sequence. The sensor network platform includes: a model construction module, which is used to obtain multiple load nodes for running data services, and is also used to obtain multiple maximum load characteristics in the load nodes for supporting the operation of data services, and is also used to build a node allocation model based on the maximum load characteristics; a pre-allocation module, which is used to obtain data resources, and is also used to obtain initial service requirements corresponding to data resources according to the data resources, and is also used to configure initial load nodes based on the node allocation model and the initial service requirements, and is also used to allocate data resources to the initial load nodes; a real-time judgment module, which is used to obtain multiple real-time load characteristics of the initial load node after a preset time period of data resource flow, and is also used to judge whether there is a real-time load characteristic that exceeds the corresponding preset threshold range based on the node allocation model; an actual allocation module, which is used to configure a target load node based on the node allocation model and the multiple real-time load characteristics after there is a real-time load characteristic that exceeds the corresponding preset threshold range, and is also used to allocate data resources to the target load node.

[0013] Optionally, the model construction module is also used to: obtain the first load node, the second load node, ..., the i-th load node; obtain the first maximum load characteristic, the second maximum load characteristic, ..., the j-th maximum load characteristic of the first load node, obtain the first maximum load characteristic, the second maximum load characteristic, ..., the j-th maximum load characteristic of the second load node, ..., obtain the first maximum load characteristic, the second maximum load characteristic, ..., the j-th maximum load characteristic of the i-th load node; and construct a node allocation model based on the obtained i load nodes and the j maximum load characteristics of each load node.

[0014] Optionally, the real-time judgment module is also used to: obtain multiple preset threshold ranges corresponding to multiple load characteristics of the initial load node according to the node allocation model, the preset threshold range including a maximum threshold point and a minimum threshold point; determine in turn whether the multiple real-time load characteristics are all less than or equal to the corresponding maximum threshold point or greater than or equal to the corresponding minimum threshold point; if so, there is no real-time load characteristic that exceeds the corresponding preset threshold range, otherwise there is a real-time load characteristic that exceeds the corresponding preset threshold range.

[0015] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned multi-node data resource allocation method based on the industrial Internet of Things.

[0016] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the multi-node data resource allocation method based on the industrial Internet of Things is implemented.

[0017] The beneficial effects of the present invention are embodied in:

[0018] In the entire multi-node data resource allocation method based on the industrial Internet of Things, by comprehensively acquiring the maximum load characteristics of the load nodes and constructing a node allocation model, a scientific and reasonable basis is provided for data resource allocation, which effectively avoids the problem of overload of some nodes and waste of resources caused by the lack of accurate perception of the real-time status of the load nodes; further, the real-time monitoring and dynamic adjustment mechanism in the method can timely discover and solve problems arising in the data resource allocation process, such as node overload or inefficient operation, thereby ensuring the efficient operation and stability of the system; further, the method also comprehensively considers the characteristics of data resources, service requirements, and the performance and capabilities of load nodes, realizes the precise matching of data resources and load nodes, and improves resource utilization and data processing efficiency; further, the implementation of the entire method not only optimizes the performance of the industrial Internet of Things and reduces operating costs, but also provides strong technical support for the future industrial intelligent transformation and promotes the sustainable development of industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0020] Figure 1 A schematic diagram of the steps of the multi-node data resource allocation method based on the industrial Internet of Things of the present invention;

[0021] Figure 2 It is a schematic diagram of step S1 in the multi-node data resource allocation method based on the industrial Internet of Things of the present invention;

[0022] Figure 3 It is a schematic diagram of step S2 in the multi-node data resource allocation method based on the industrial Internet of Things of the present invention;

[0023] Figure 4 It is a schematic diagram of step S3 in the multi-node data resource allocation method based on industrial Internet of Things of the present invention;

[0024] Figure 5 It is a schematic diagram of step S4 in the multi-node data resource allocation method based on industrial Internet of Things of the present invention;

[0025] Figure 6 It is a schematic diagram of the composition of the multi-node data resource allocation system based on the industrial Internet of Things of the present invention;

[0026] Figure 7 It is a schematic diagram of the composition of the optimized industrial Internet of Things involved in the present invention;

[0027] Figure 8 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0028] Reference numerals:

[0029] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - input / output (I / O) interface, 705 - communication component. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0033] like Figure 1 As shown, a multi-node data resource allocation method based on industrial Internet of Things is provided, including:

[0034] S1. Acquire multiple load nodes for running data services, acquire multiple maximum load characteristics in the load nodes for supporting the operation of the data services, and build a node allocation model based on the maximum load characteristics;

[0035] S2. Acquire data resources, acquire initial service requirements corresponding to the data resources according to the data resources, configure initial load nodes based on the node allocation model and the initial service requirements, and allocate data resources to the initial load nodes;

[0036] S3, after the data resource flows for a preset period of time, multiple real-time load characteristics of the initial load node are obtained, and whether there is a real-time load characteristic that exceeds the corresponding preset threshold range is determined based on the node allocation model;

[0037] S4. If so, configure the target load node based on the node allocation model and multiple real-time load characteristics, and allocate data resources to the target load node.

[0038] In this embodiment, it should be noted that in S1, first, it is necessary to obtain multiple load nodes for running data services in the industrial Internet of Things. These load nodes are the basis for data resource allocation. They may be distributed in different physical locations and have different hardware configurations and performance parameters; then, for each load node, it is necessary to obtain multiple maximum load characteristics for supporting the operation of data services. These maximum load characteristics may include maximum CPU usage, maximum memory usage, maximum disk IO, maximum network bandwidth, etc., which together determine the limits of the data processing capacity and carrying capacity of the load node. Based on these maximum load characteristics, a node allocation model can be constructed, which has the characteristics and parameters of a large number of load nodes, providing a scientific basis for subsequent data resource allocation. In summary, by obtaining the maximum load characteristics of the load node, the performance status of each node can be fully understood to avoid blindness in the data resource allocation process; secondly, the node allocation model constructed based on the maximum load characteristics can achieve accurate matching of data resources and load nodes and improve resource utilization; finally, the work at this stage lays a solid foundation for subsequent data resource allocation and ensures the scientificity and rationality of the entire data resource allocation process.

[0039] In S2, the data resources to be allocated are first obtained. These data resources may come from various devices and systems in the industrial Internet of Things, including production data, monitoring data, operational data, etc. Subsequently, the initial service requirements corresponding to these data resources need to be clarified according to their characteristics. These service requirements may involve data calculation, data processing, data storage, data analysis or data transmission, and each requirement has different requirements for computing resources, storage resources, network bandwidth, etc. For example, some data resources need to perform a large amount of data calculation, so the requirements for computing resources are relatively high, mainly relying on CPU characteristics; for example, some data resources need to store and record a large amount of temporary data, so the requirements for storage resources are relatively high, mainly relying on disk read and write speed characteristics and disk capacity characteristics; for example, some data resources need to perform a large amount of network transmission, so the requirements for network bandwidth are relatively high, mainly relying on network bandwidth characteristics; After clarifying the initial service requirements of the data resources, the initial load nodes can be configured for these data resources based on the node allocation model constructed in the S1 stage. This configuration process needs to comprehensively consider the characteristics of the data resources, service requirements, and the performance and capabilities of the load nodes to ensure that the data resources can be allocated to the most suitable load nodes, thereby achieving optimal configuration and efficient utilization of resources. In summary, by configuring the initial load nodes for data resources based on the node allocation model and initial service requirements, it can be ensured that data resources are scientifically and rationally planned during the allocation process, avoiding waste of resources and excessive competition; at the same time, since the characteristics and service requirements of data resources are taken into consideration, data resources can be allocated to the most suitable load nodes, thereby improving the efficiency of data processing and analysis and reducing response time; in addition, the implementation of this stage will also help to improve the stability and reliability of the entire industrial Internet of Things, providing a strong guarantee for the smooth progress of industrial production.

[0040] In S3, after a preset period of time of data resource flow, the real-time load characteristics of the initial load node are obtained; this step is to monitor the actual operating status of the load node after data resource allocation, including real-time changes in key indicators such as CPU usage, memory usage, disk IO, and network bandwidth; after obtaining these real-time load characteristics, based on the node allocation model constructed in the S1 stage, it is determined whether there are real-time load characteristics that exceed the corresponding preset threshold range; the preset threshold is set in advance according to the load characteristics of the load node and the system stability requirements. Different load characteristics may have different preset thresholds, which are used to determine whether the load node is in an overloaded (greater than the maximum value of the preset threshold range) or inefficient (less than the minimum value of the preset threshold range) state; if real-time load characteristics are found to exceed the preset threshold range, it means that there is a problem with the current data resource allocation plan and it needs to be adjusted. In summary, by real-time monitoring of the real-time load characteristics of load nodes and making judgments based on the node allocation model, problems arising in the data resource allocation process, such as overload of some nodes and waste of resources, can be discovered and resolved in a timely manner; the implementation of this stage helps to ensure that data resources are always allocated to the most appropriate load nodes, thereby maintaining efficient operation and stability of the system; at the same time, by dynamically adjusting the data resource allocation plan, it can also better adapt to the dynamic changes in production needs and improve the flexibility and scalability of the entire industrial Internet of Things.

[0041] In S4, when the monitoring and judgment in the S3 stage find that there are real-time load characteristics that exceed the preset threshold range, it means that the current data resource allocation is no longer applicable and needs to be adjusted; at this time, the target load node is reconfigured based on the node allocation model constructed in the S1 stage and the multiple real-time load characteristics obtained in the S3 stage; this configuration process will comprehensively consider the real-time load characteristics of the current load node (that is, the actual operating status), and does not need to be allocated based on the demand for data resources, effectively ensuring that data resources can be allocated to new and more suitable load nodes. After completing the configuration of the target load node, the data resources will be migrated from the initial load node to the target load node to achieve efficient utilization of data resources and optimization of system performance. In summary, through comprehensive consideration based on real-time load characteristics and data resource requirements, efficient and reasonable allocation of data resources can be achieved. It no longer relies solely on the demand for data resources as the basis for allocation, but comprehensively considers the real-time load characteristics of the current load node, that is, its actual operating status, which can more accurately reflect the true load capacity of the node, thereby ensuring that data resources can be accurately allocated to new and more matching load nodes, avoiding performance bottlenecks and resource waste caused by node overload or idle resources; secondly, the adjustment and optimization process at this stage enhances the flexibility and adaptability of the system, enabling it to respond quickly to dynamic changes in production demand and ensure that the system always maintains the best operating state; finally, by smoothly migrating data resources from the initial load node to the target load node, efficient resource utilization and performance optimization are achieved, providing a solid guarantee for the stable operation and sustainable development of the Industrial Internet of Things.

[0042] In summary, in the entire multi-node data resource allocation method based on the industrial Internet of Things, by comprehensively acquiring the maximum load characteristics of the load nodes and constructing a node allocation model, a scientific and reasonable basis is provided for data resource allocation, which effectively avoids the problem of overload of some nodes and waste of resources caused by the lack of accurate perception of the real-time status of the load nodes; further, the real-time monitoring and dynamic adjustment mechanism in the method can timely discover and solve problems arising in the data resource allocation process, such as node overload or inefficient operation, thereby ensuring the efficient operation and stability of the system; further, the method also comprehensively considers the characteristics of data resources, service requirements, and the performance and capabilities of load nodes, realizes the precise matching of data resources and load nodes, and improves resource utilization and data processing efficiency; further, the implementation of the entire method not only optimizes the performance of the industrial Internet of Things and reduces operating costs, but also provides strong technical support for the future industrial intelligent transformation and promotes the sustainable development of industrial production.

[0043] like Figure 2 As shown, in one embodiment, the method of constructing a node allocation model in S1 includes:

[0044] S11, obtaining the first load node, the second load node, ..., the i-th load node;

[0045] S12, obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the first load node, obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the second load node, ..., obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the i-th load node;

[0046] S13. Construct a node allocation model according to the obtained i load nodes and the j maximum load characteristics of each load node.

[0047] In this embodiment, it should be noted that is the maximum load characteristic vector of the nth load node, ,in is the mth maximum load feature (CPU usage, memory usage, disk IO or network bandwidth, etc.) of the nth load node; then the entire node allocation model can be represented by a matrix: . In summary, the entire node allocation model provides an intuitive way to organize and display the characteristic data of load nodes in the form of a matrix. Through the matrix, we can clearly see the various characteristic indicators of each load node and the comparative relationship between these indicators. This visualization helps us better understand and analyze the performance and carrying capacity of load nodes; at the same time, matrix operation is an efficient data processing method, especially suitable for large-scale data processing and analysis. In the node allocation model, matrix operations can be used to quickly calculate how data resources should be allocated to each load node, thereby achieving efficient resource allocation and optimization; further, the matrix form has good scalability and updateability. When adding new load nodes or data resources, you only need to add corresponding rows or columns to the matrix and then recalculate the allocation vector. This flexibility enables the matrix-based node allocation model to adapt to changes and development.

[0048] like Figure 3 As shown, in one embodiment, configuring the initial load node based on the node allocation model and the initial service requirements in S2 includes:

[0049] S21. Obtain multiple demand indicators for different load characteristics according to the initial service demand;

[0050] S22, calculating the comprehensive performance indicators of multiple load nodes in sequence according to the multiple demand indicators;

[0051] S23. Obtain the load node corresponding to the maximum comprehensive performance index and use it as the initial load node.

[0052] In this embodiment, it should be noted that in S21, it is necessary to clarify the specific requirements of the initial service demand for the load node. These requirements may include processing power, storage capacity, network bandwidth, response time and other aspects. For each aspect, one or more specific demand indicators are set. These indicators will serve as the basis for the subsequent evaluation of the performance of the load node. In S22, we calculate for each load node according to the demand indicators determined in the previous step to obtain the comprehensive performance indicator of the node. This comprehensive performance indicator can be a weighted sum, in which the weight of each demand indicator reflects its importance in the overall performance; for example, if processing power is the most important indicator, we can assign it a higher weight. In S23, compare the comprehensive performance indicators of all load nodes, and select the largest one as the initial load node. This node is theoretically the most able to meet the initial service demand.

[0053] The above process can ensure that the selected initial load nodes can achieve a relatively good level in multiple key indicators, thereby providing stable support for subsequent services. At the same time, through the calculation of clear demand indicators and comprehensive performance indicators, the performance of load nodes can be evaluated more objectively and accurately, avoiding subjective assumptions and misjudgments. It should be noted that this implementation method is a static process. It selects the initial load nodes based on the initial service requirements and the initial node allocation model. In actual applications, the performance of the load nodes needs to be re-evaluated regularly, and the node allocation model needs to be adjusted as needed.

[0054] In one embodiment, calculating the comprehensive performance indicators of multiple load nodes in sequence according to the multiple demand indicators in S22 includes: , ;in,

[0055] is the comprehensive performance index of the nth load node, is the weight of the mth performance indicator, To transform the demand index into a function of performance index, is the mth maximum load characteristic of the nth load node, is the demand indicator of the initial service demand for the mth load characteristic.

[0056] In this embodiment, it should be noted that the entire comprehensive performance index takes into account multiple demand indicators, and these indicators are integrated into a comprehensive performance index by weighted sum. This can not only comprehensively evaluate the performance of the load node, but also ensure the accuracy of the evaluation, because each indicator is assigned a corresponding weight according to its importance; further, the function in the expression It is flexible and can be defined and adjusted according to actual needs, which means that when the system requirements or the characteristics of the load nodes change, this function can be easily modified to quickly adapt to the new situation. The process of converting demand indicators into performance indicators needs to satisfy monotonically increasing requirements, ensuring that the larger the maximum load characteristic, the smaller the demand indicator and the higher the performance indicator. Furthermore, the comprehensive performance indicators provide strong support for the decision-making of the system. For example, when it is necessary to expand the system or upgrade the load node, the most suitable node can be selected for operation based on the comprehensive performance indicators, thereby ensuring the continuous and efficient operation of the allocation and further processing of data resources.

[0057] In one embodiment, The expressions include: .

[0058] In this embodiment, it should be noted that the above expression has a smooth growth characteristic, which means that when This characteristic helps to suppress excessive load characteristics to a certain extent when evaluating the performance of load nodes, so as to avoid excessive impact of a single indicator on the comprehensive performance indicators. Furthermore, through the transformation of the above expression, load characteristics of different dimensions can be converted to the same order of magnitude, which helps to eliminate the impact of dimensional differences on the calculation of comprehensive performance indicators. In this way, even if the original values ​​of different load characteristics differ greatly, their impact on the comprehensive performance indicators will be more balanced after the transformation of the above expression. Similarly, when the value of a load characteristic is very large, if a linear relationship is used directly for calculation, the comprehensive performance indicators may be dominated by this extreme value. The use of the above expression can weaken the impact of extreme values ​​to a certain extent, making the comprehensive performance indicators more robust and reliable.

[0059] like Figure 4 As shown, in one embodiment, judging whether there is a real-time load feature exceeding the corresponding preset threshold range based on the node allocation model in S3 includes:

[0060] S31, acquiring multiple preset threshold ranges corresponding to multiple load characteristics of the initial load node according to the node allocation model, where the preset threshold ranges include a maximum threshold point and a minimum threshold point;

[0061] S32, determining in sequence whether the plurality of real-time load characteristics are all less than or equal to the corresponding maximum threshold point or greater than or equal to the corresponding minimum threshold point;

[0062] S33. If yes, then there is no real-time load feature that exceeds the corresponding preset threshold range; otherwise, there is a real-time load feature that exceeds the corresponding preset threshold range.

[0063] In this embodiment, it should be noted that in S31, according to the node allocation model, multiple preset threshold ranges corresponding to multiple load characteristics of the initial load node are obtained. The preset threshold range usually includes a maximum threshold point and a minimum threshold point, which together define an acceptable load characteristic value interval. This step provides a benchmark for subsequent load characteristic judgments and ensures the accuracy and consistency of the evaluation. In S32, multiple real-time load characteristics are checked in turn to determine whether they are all less than or equal to the corresponding maximum threshold point, or greater than or equal to the corresponding minimum threshold point; the real-time load characteristics reflect the actual load situation of the load node at the current moment. Through this step, we can understand the real-time state of the load node and whether they are in the preset safe or efficient operation range. In S33, it is determined whether there is any real-time load characteristic that exceeds its corresponding preset threshold range; if not, all real-time load characteristics are within the preset threshold range, indicating that the load node is currently in a normal state; if so, there are real-time load characteristics that exceed the preset threshold range, and further measures may be needed to adjust the load or optimize resource allocation.

[0064] like Figure 5 As shown, in one embodiment, configuring the target load node based on the node allocation model and multiple real-time load characteristics in S4 includes:

[0065] S41, acquiring multiple load nodes and multiple maximum load characteristics corresponding to the load nodes based on the node allocation model;

[0066] S42, screening out multiple load nodes that meet the real-time load characteristics from the multiple load nodes, and repeating this process to screen out multiple load nodes that meet the multiple real-time load characteristics and form a pre-target load node set;

[0067] S43. Select a load node closest to the initial load node from the pre-target load node set as the target load node.

[0068] In this embodiment, it should be noted that, in S41, based on the node allocation model, multiple load nodes in the system and multiple maximum load characteristics corresponding to each load node are obtained. This step provides basic data for subsequent load node screening, ensuring the accuracy and effectiveness of the screening. In S42, among multiple load nodes, screening is performed according to real-time load characteristics; specifically, it is to find those load nodes whose maximum load characteristics can meet or exceed the current real-time load characteristics; this process needs to be repeated until load nodes that meet all real-time load characteristics are screened out; this step ensures that the selected load node can meet the current actual needs in terms of processing capacity, and provides a candidate set for the subsequent final selection. In S43, in the pre-target load node set, the closest load node is selected as the target load node based on the distance to the initial load node (which may be physical distance, network delay, transmission cost, etc.); this step ensures that the selected target load node not only meets the requirements in terms of processing capacity, but also is close to the initial load node in terms of geographical location or network topology, thereby optimizing system performance and reducing transmission costs.

[0069] like Figure 6 As shown, a multi-node data resource allocation system based on the industrial Internet of Things is also provided, the system is used to implement the multi-node data resource allocation method based on the industrial Internet of Things in any one of the above embodiments, the system includes a management platform, a sensor network platform and an object platform that are sequentially connected in communication, and the sensor network platform includes:

[0070] A model building module, used to obtain a plurality of load nodes for running data services, and also used to obtain a plurality of maximum load characteristics in the load nodes for supporting the operation of the data services, and also used to build a node allocation model based on the maximum load characteristics;

[0071] A pre-allocation module, used to obtain data resources, to obtain initial service requirements corresponding to the data resources according to the data resources, to configure initial load nodes based on the node allocation model and the initial service requirements, and to allocate data resources to the initial load nodes;

[0072] A real-time judgment module, used to obtain multiple real-time load characteristics of the initial load node after a preset time period of data resource flow, and also used to judge whether there is a real-time load characteristic exceeding a corresponding preset threshold range based on the node allocation model;

[0073] The actual allocation module is used to configure the target load node based on the node allocation model and multiple real-time load characteristics after there is a real-time load characteristic that exceeds the corresponding preset threshold range, and is also used to allocate data resources to the target load node.

[0074] In one embodiment, the model building module is further configured to:

[0075] Obtain the first load node, the second load node, ..., the i-th load node;

[0076] Obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the first load node, obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the second load node, ..., obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the i-th load node;

[0077] A node allocation model is constructed based on the obtained i load nodes and the j maximum load characteristics of each load node.

[0078] In one embodiment, the real-time determination module is further used to:

[0079] Acquire multiple preset threshold ranges corresponding to multiple load characteristics of the initial load node according to the node allocation model, wherein the preset threshold ranges include a maximum threshold point and a minimum threshold point;

[0080] Determine in sequence whether the multiple real-time load characteristics are all less than or equal to the corresponding maximum threshold point or greater than or equal to the corresponding minimum threshold point;

[0081] If yes, then there is no real-time load feature that exceeds the corresponding preset threshold range; otherwise, there is a real-time load feature that exceeds the corresponding preset threshold range.

[0082] In this embodiment, it should be noted that, regarding the above-mentioned multi-node data resource allocation system based on industrial Internet of Things, the specific method of performing operations has been described in detail in the implementation of the multi-node data resource allocation method based on industrial Internet of Things, and will not be elaborated here.

[0083] It should also be noted that the entire multi-node data resource allocation system based on industrial Internet of Things can be applied to the optimized industrial Internet of Things, such as Figure 7 As shown, the optimized industrial Internet of Things includes a user platform, a service platform, a management platform, a sensor network platform and an object platform that establish communication in sequence;

[0084] The user platform is configured to provide front-end service functions to users; users obtain the required perceived service information through the user platform, process the perceived service information, and convert it into user perceived information; users analyze the user perceived information and make corresponding decisions based on their own wishes, and convert the user perceived information into user control information through the corresponding information system and send it to the service platform, thereby expressing the user's corresponding service demand intention.

[0085] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., which realize user-side services through integration with user information system software.

[0086] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entity of the service platform includes various servers.

[0087] The management platform is configured to perform at least one of equipment operation status monitoring management, data monitoring management, equipment parameter management, and life cycle management; the management platform is an operational coordination platform for the Internet of Things, which may include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0088] The sensor network platform is configured to perform at least one of network management, command management, device status management, data protocol management, data analysis, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides data communication, analysis, identification, classification, and other functions, avoiding the direct aggregation of data from various object platforms on the management platform, resulting in redundant data on the management platform and low data processing efficiency; the physical entities of the object platform include various gateways, edge computing devices, etc.

[0089] The object platform is configured to carry out specific production work such as production control, detection, and measurement; the physical entities of the production objects include various production equipment, sensors, etc.

[0090] Figure 8 is a block diagram of an electronic device showing a multi-node data resource allocation method based on the industrial Internet of Things according to an exemplary embodiment. Figure 8 As shown, the electronic device 700 may include: a processor 701 , a memory 702 . The electronic device 700 may also include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0091] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned multi-node data resource allocation method based on the industrial Internet of Things. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the above-mentioned other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0092] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned multi-node data resource allocation method based on industrial Internet of Things.

[0093] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the multi-node data resource allocation method based on the industrial Internet of Things are implemented. For example, the computer-readable storage medium can be the memory 702 including the program instructions, and the program instructions can be executed by the processor 701 of the electronic device 700 to complete the multi-node data resource allocation method based on the industrial Internet of Things.

[0094] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned multi-node data resource allocation method based on the industrial Internet of Things when executed by the programmable device.

[0095] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0096] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0097] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A multi-node data resource allocation method based on industrial Internet of Things, characterized in that: include: Acquire multiple load nodes for running data services, acquire multiple maximum load characteristics in the load nodes for supporting the operation of the data services, and build a node allocation model based on the maximum load characteristics; Acquire data resources, acquire initial service requirements corresponding to the data resources according to the data resources, configure initial load nodes based on the node allocation model and the initial service requirements, and allocate the data resources to the initial load nodes; After the data resource flows for a preset time period, multiple real-time load characteristics of the initial load node are obtained, and based on the node allocation model, it is determined whether there is a real-time load characteristic that exceeds the corresponding preset threshold range; If so, configuring the target load node based on the node allocation model and multiple real-time load characteristics, and allocating data resources to the target load node; The configuring of the initial load node based on the node allocation model and the initial service demand includes: obtaining a plurality of demand indicators for different load characteristics according to the initial service demand; calculating the comprehensive performance indicators of a plurality of load nodes in turn according to the plurality of demand indicators; obtaining the load node corresponding to the maximum comprehensive performance indicator and using it as the initial load node; The step of calculating the comprehensive performance indicators of multiple load nodes in sequence according to multiple demand indicators includes: , ;in, is the comprehensive performance index of the nth load node, is the weight of the mth performance indicator, To transform the demand index into a function of performance index, is the mth maximum load characteristic of the nth load node, is the demand index of the initial service demand for the mth load characteristic; The expressions include: .

2. The multi-node data resource allocation method based on industrial Internet of Things according to claim 1 is characterized in that: The method for constructing a node allocation model comprises: Obtain the first load node, the second load node, ..., the i-th load node; Obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the first load node, obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the second load node, ..., obtaining the first maximum load characteristic, the second maximum load characteristic, ..., the jth maximum load characteristic of the i-th load node; A node allocation model is constructed based on the obtained i load nodes and the j maximum load characteristics of each load node.

3. The multi-node data resource allocation method based on industrial Internet of Things according to claim 1 is characterized in that: The determining whether the real-time load characteristic is within a preset threshold range based on the node allocation model includes: Acquire multiple preset threshold ranges corresponding to multiple load characteristics of the initial load node according to the node allocation model, wherein the preset threshold ranges include a maximum threshold point and a minimum threshold point; Determine in sequence whether the multiple real-time load characteristics are all less than or equal to the corresponding maximum threshold point or greater than or equal to the corresponding minimum threshold point; If yes, then there is no real-time load feature that exceeds the corresponding preset threshold range; otherwise, there is a real-time load feature that exceeds the corresponding preset threshold range.

4. The multi-node data resource allocation method based on industrial Internet of Things according to claim 3 is characterized in that: The configuring of the target load node based on the node allocation model and multiple real-time load characteristics includes: Acquire multiple load nodes and multiple maximum load characteristics corresponding to the load nodes based on the node allocation model; Screening out multiple load nodes that meet the real-time load characteristics from multiple load nodes, and looping in this way to screen out multiple load nodes that meet the multiple real-time load characteristics and form a pre-target load node set; A load node closest to the initial load node is selected from the pre-target load node set as the target load node.

5. A multi-node data resource allocation system based on industrial Internet of Things, characterized in that: The system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the sensor network platform includes: A model building module, used to obtain multiple load nodes for running data services, obtain multiple maximum load characteristics in the load nodes for supporting the operation of the data services, and build a node allocation model based on the maximum load characteristics; A pre-allocation module is used to obtain data resources, obtain initial service requirements corresponding to the data resources according to the data resources, configure initial load nodes based on the node allocation model and the initial service requirements, and allocate the data resources to the initial load nodes, wherein the configuration of the initial load nodes based on the node allocation model and the initial service requirements includes: obtaining multiple demand indicators for different load characteristics according to the initial service requirements; calculating the comprehensive performance indicators of multiple load nodes in sequence according to the multiple demand indicators; obtaining the load node corresponding to the maximum comprehensive performance indicator and using it as the initial load node; the calculation of the comprehensive performance indicators of multiple load nodes in sequence according to the multiple demand indicators includes: , ;in, is the comprehensive performance index of the nth load node, is the weight of the mth performance indicator, To transform the demand index into a function of performance index, is the mth maximum load characteristic of the nth load node, is the demand index of the initial service demand for the mth load characteristic; The expressions include: ; A real-time judgment module is used to obtain multiple real-time load characteristics of the initial load node after a preset time period of data resource flow, and judge whether there is a real-time load characteristic exceeding a corresponding preset threshold range based on the node allocation model; The actual allocation module is used to configure the target load node based on the node allocation model and multiple real-time load characteristics and allocate data resources to the target load node when there is a real-time load characteristic that exceeds the corresponding preset threshold range.

6. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the multi-node data resource allocation method based on the industrial Internet of Things as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the multi-node data resource allocation method based on the industrial Internet of Things described in any one of claims 1 to 4 is implemented.

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