A device access method and system based on local heterogeneous networking

Abstract

The application discloses a device access method and system based on local heterogeneous networking, and relates to the field of electric vehicle access. The method comprises the following steps: obtaining operation data of local heterogeneous networking, constructing a device matching optimization problem, and introducing a virtual queue to transform the device matching optimization problem; dividing the optimized device matching optimization problem into a first stage and a second stage; the first stage is a lightweight common sense algorithm terminal scheduling subproblem, and the second stage is a data compression subproblem and a time slicing subproblem; the first stage is solved by using a lightweight common sense algorithm terminal scheduling algorithm to obtain a lightweight common sense algorithm terminal scheduling decision; the second stage is solved by using a data compression optimization algorithm and a time slicing optimization algorithm to obtain an optimal compression scheme and an optimal time slicing ratio; and a to-be-accessed device is obtained, and the to-be-accessed device is accessed to the local heterogeneous networking based on the lightweight common sense algorithm terminal scheduling decision, the optimal compression scheme and the optimal time slicing ratio.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of device access of heterogeneous networking, and mainly relates to a device access method and system based on local heterogeneous networking. BACKGROUND

[0002] With the promotion of new power systems, a large number of distributed photovoltaic, electric vehicles and energy storage devices need to access the power grid with low latency, and through source-grid-load-storage collaborative regulation to ensure new energy consumption and balance energy supply and demand. However, the distribution network is wide and complex in topology, and is affected by electromagnetic interference, signal attenuation, etc. There are network coverage blind areas in some areas. Heterogeneous networking uses power line carrier, 5G and micro-power wireless technology to fill in the blind spots. Power line carrier transmits data through existing power lines, which has the advantages of wide coverage and low cost. The number of devices connected by each lightweight sensing and computing terminal is quite different, resulting in uneven distribution of network load, and the queue of some heavily loaded terminals is too large, affecting the satisfaction of low latency requirements.

[0003] However, the traditional networking method does not fully consider the need to fill in the communication coverage blind area, and it is difficult to jointly optimize the scheduling of lightweight sensing and computing terminals, data compression, and time slicing, and cannot guarantee the normal connection of devices and reliable low-latency transmission of data; and it is difficult to dynamically adjust the networking strategy according to connectivity, latency deficit and cluster matching utility, resulting in that lightweight sensing and computing terminals with poor connectivity, low deficit or low cluster matching utility will occupy the channel of lightweight sensing and computing terminals with good connectivity, high deficit and high cluster matching utility, affecting the overall performance and efficiency of the network; in addition, the traditional method is difficult to realize multi-agent collaborative heterogeneous network resource allocation in an uncertain information scenario, and has slow convergence speed and low optimization precision, making it difficult to make optimal data compression scheme decisions.

[0004] A Chinese invention patent with publication number "CN116156649A" discloses a "scheduling method and device, and storage medium", which specifically discloses "sending scheduling indication information to a terminal device; the scheduling indication information includes a first indicator and a second indicator; the first indicator indicates a target scheduling time window; the second indicator indicates the position of a target transmission unit in the scheduling time window, the target transmission unit corresponding to the terminal device is determined based on at least the first indicator and the second indicator, and the target transmission unit is used for the terminal device to receive downlink data sent by a second access network device", but this method does not consider network dynamic changes (such as device load, channel quality, data queue backlog, etc.), lacks the ability to dynamically adjust according to real-time operation data, and is difficult to cope with communication interference, time delay fluctuation and other problems in complex scenarios; in addition, this method cannot solve the problems of uneven load, channel resource occupation and low data transmission efficiency in heterogeneous networks, especially in the environment of power distribution network with many aspects, coverage blind area and electromagnetic interference, it is difficult to guarantee the stability and low time delay demand of device access. SUMMARY

[0005] In order to solve the above-mentioned problems existing in the prior art, the application provides a device access method and system based on local heterogeneous networking.

[0006] The technical scheme of the application is as follows:

[0007] On the one hand, the application provides a device access method based on local heterogeneous networking, which comprises:

[0008] Obtaining operation data of local heterogeneous networking, the local heterogeneous networking comprising a perception layer, a relay layer, an edge layer and a service layer; wherein the relay layer comprises a lightweight sensing algorithm terminal;

[0009] Constructing a device matching optimization problem, and introducing a virtual queue to transform the device matching optimization problem; the optimized device matching optimization problem is divided into a first stage and a second stage; wherein the first stage is a lightweight sensing algorithm terminal scheduling subproblem, and the second stage is a data compression subproblem and a time slicing subproblem;

[0010] The device matching optimization problem comprises a target function constructed by minimizing scheduling variables, time slicing variables and data compression schemes, and constraint conditions constructed by channel selection constraints, lightweight sensing algorithm terminal scheduling constraints, time slicing constraints, range constraints of data compression scheme selection indicator variables, relay data transmission queuing delay constraints and local collected data transmission queuing delay constraints;

[0011] The virtual queue is introduced to transform the device matching optimization problem, to obtain a transformed device matching optimization problem and a virtual queue; and the optimized device matching optimization problem is decomposed into a first stage and a second stage.

[0012] The first stage includes a first sub-objective function constructed with a scheduling variable as an objective function, and a first sub-constraint condition constructed with a channel selection constraint and a lightweight sensing algorithm terminal scheduling constraint as constraints;

[0013] The second stage includes a second sub-objective function constructed with a time slicing variable and a data compression scheme as an objective function, and a second sub-constraint condition constructed with a time slicing constraint and a range constraint of a data compression scheme selection indicator variable as constraints;

[0014] The first stage is solved by using a lightweight sensing algorithm terminal scheduling algorithm to obtain a lightweight sensing algorithm terminal scheduling decision; and the second stage is solved by using a data compression optimization algorithm and a time slicing optimization algorithm to obtain an optimal compression scheme and an optimal time slicing ratio;

[0015] The time slicing optimization algorithm specifically optimizes time slicing based on a guidance degree and a guidance migration probability, and specifically includes:

[0016] Based on the data queue backlog and the corresponding virtual queue, the guidance degree of the cluster represented by each lightweight sensing algorithm terminal is calculated;

[0017] Based on the data queue backlog in a preset historical time window length, the guidance migration probability of the cluster represented by each lightweight sensing algorithm terminal is calculated;

[0018] The time slicing variable under the current time slot is solved based on the guidance degree and the guidance migration probability to obtain the optimal time slicing ratio;

[0019] The method further includes obtaining a to-be-accessed device, and based on the lightweight sensing algorithm terminal scheduling decision, the optimal compression scheme and the optimal time slicing ratio, the to-be-accessed device is accessed to the local heterogeneous networking.

[0020] Preferably, the method further includes data cleaning of the operation data, and the data cleaning includes processing missing values, abnormal values and data format unification.

[0021] Preferably, the lightweight sensing algorithm terminal scheduling algorithm includes an initialization stage, a cluster matching utility evaluation stage, a preference list establishment stage, a matching application stage and a conflict coordination stage, and specifically includes:

[0022] The initialization stage specifically initializes the data queue backlog of all lightweight sensing algorithm terminals, the number of matching requests received by the channel and the matching cost;

[0023] The cluster matching utility evaluation stage specifically comprises calculating the data transmission queuing delay and the corresponding coverage value coefficient of the cluster represented by each lightweight sensing terminal through weighted calculation of all lightweight sensing terminals, so as to obtain the matching utility of the cluster represented by each lightweight sensing terminal.

[0024] The preference list establishment stage specifically comprises calculating the preference value of the cluster represented by each lightweight sensing terminal for different channels based on the data transmission queuing delay and the data queue backlog; and arranging all the generated preference values in descending order to obtain the matching preference list of the lightweight sensing terminal.

[0025] The matching application stage specifically comprises that, based on the matching preference list, the un-matched lightweight sensing terminal initiates a matching request to the channel with the largest preference value; if there is no resource competition conflict in the currently selected channel, the matching is completed, and the cluster matching utility evaluation stage is entered; otherwise, it indicates that there is a resource competition conflict, and the conflict coordination stage is entered.

[0026] The conflict coordination stage specifically comprises updating the matching cost of the current channel based on the matching cost increment step, the connectivity of the cluster represented by the lightweight sensing terminal, and the matching utility of the cluster represented by each lightweight sensing terminal.

[0027] The iterative matching process is performed until all lightweight sensing terminals are matched, and the lightweight sensing terminal scheduling decision is obtained.

[0028] Preferably, the data compression optimization algorithm specifically comprises a performance deviation perception adaptive particle swarm algorithm, wherein:

[0029] The target function value obtained in the search of the current round of each particle is calculated by using the improved performance deviation perception inertia weight;

[0030] The iterative search process specifically comprises comparing the current fitness value of the current particle with the individual optimal extreme value of the current particle; if the current fitness value is less than the individual optimal extreme value, the individual optimal extreme value is updated to the current fitness value.

[0031] Until the maximum search round number or the target function converges, the iteration is stopped, the individual optimal extreme values of all particles are traversed, the minimum individual optimal extreme value is taken as the global optimal extreme value, that is, the optimal compression scheme.

[0032] Preferably, the method further comprises data monitoring on the local heterogeneous networking, specifically real-time monitoring of the indicators of the lightweight sensing terminal scheduling decision, the data compression optimization algorithm and the time slicing optimization algorithm, wherein the indicator of the lightweight sensing terminal scheduling decision is the scheduling delay, the indicator of the data compression optimization algorithm is the compression rate, and the indicator of the time slicing optimization algorithm is the slicing deviation.

[0033] If any index is monitored to exceed the preset threshold, a multi-modal communication intelligent fusion container level gateway built in the edge layer will trigger to re-solve the device matching optimization problem, generate a new lightweight sensing algorithm terminal scheduling decision, data compression optimization algorithm and time slicing optimization algorithm.

[0034] In another aspect, the application also provides a device access system based on local heterogeneous networking, comprising a data acquisition module, an optimization problem construction module, a solving module and a result output module, wherein:

[0035] The data acquisition module is configured to acquire running data of the local heterogeneous network, wherein the local heterogeneous network comprises a perception layer, a relay layer, an edge layer and a service layer; the relay layer comprises lightweight sensing algorithm terminals; and the running data is transmitted to the optimization problem construction module.

[0036] The optimization problem construction module is configured to construct a device matching optimization problem, and introduce a virtual queue to transform the device matching optimization problem; and the optimized device matching optimization problem is decomposed into a first stage and a second stage; wherein the first stage is a lightweight sensing algorithm terminal scheduling sub-problem, and the second stage is a data compression sub-problem and a time slicing sub-problem.

[0037] The device matching optimization problem comprises a target function constructed by taking a scheduling variable, a time slicing variable and a data compression scheme as a minimum target, and constraint conditions constructed by taking a channel selection constraint, a lightweight sensing algorithm terminal scheduling constraint, a time slicing constraint, a range constraint of a data compression scheme selection indicator variable, a relay data transmission queuing delay constraint and a local acquisition data transmission queuing delay constraint as constraints.

[0038] The device matching optimization problem is transformed by introducing a virtual queue to obtain a transformed device matching optimization problem and a virtual queue; and the optimized device matching optimization problem is decomposed into a first stage and a second stage.

[0039] The first stage comprises a first sub-target function constructed by taking a scheduling variable as a minimum target function, and a first sub-constraint condition constructed by taking a channel selection constraint and a lightweight sensing algorithm terminal scheduling constraint as constraints.

[0040] The second stage comprises a second sub-target function constructed by taking a time slicing variable and a data compression scheme as a minimum target function, and a second sub-constraint condition constructed by taking a time slicing constraint and a range constraint of a data compression scheme selection indicator variable as constraints.

[0041] The solving module is configured to solve the first stage by using a lightweight sensing algorithm terminal scheduling algorithm to obtain a lightweight sensing algorithm terminal scheduling decision; and solve the second stage by using a data compression optimization algorithm and a time slicing optimization algorithm to obtain an optimal compression scheme and an optimal time slicing ratio.

[0042] The time slicing optimization algorithm is specifically for optimizing time slicing based on orientation degree and orientation migration probability, and specifically comprises the following steps:

[0043] Based on the data queue backlog and the corresponding virtual queue, the orientation degree of the cluster represented by each lightweight sensing algorithm terminal is calculated.

[0044] Based on the data queue backlog in the preset historical time window length, the orientation migration probability of the cluster represented by each lightweight sensing algorithm terminal is calculated.

[0045] Based on the orientation degree and the orientation migration probability, the time slicing variable under the current time slot is solved to obtain the optimal time slicing ratio.

[0046] The result output module acquires the to-be-accessed device, and based on the lightweight sensing algorithm terminal scheduling decision, the optimal compression scheme and the optimal time slicing ratio, the to-be-accessed device is accessed to the local heterogeneous networking.

[0047] In another aspect, the application also provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the device access method based on local heterogeneous networking as described in the application when executing the program.

[0048] In another aspect, the application also provides a computer readable storage medium having a computer program stored thereon, and the program is executed by a processor to implement the device access method based on local heterogeneous networking as described in the application.

[0049] Compared with the prior art, the application has the following beneficial effects:

[0050] 1) The application provides a device access method and system based on local heterogeneous networking, which improves the compatibility of different protocols and different types of devices through the collaborative design of local heterogeneous networking, enhances the flexibility and scene adaptability of network deployment, and improves the robustness in complex environments.

[0051] 2) The application provides a device access method and system based on local heterogeneous networking, which decomposes the problem into terminal scheduling, data compression and time slicing sub-problems based on the device matching optimization framework of the virtual queue, improves the spatiotemporal allocation accuracy of computing resources and communication bandwidth, and enhances the load balancing capability of the system in high-concurrency device access scenarios.

[0052] 3) The application provides a device access method and system based on local heterogeneous networking, and the multi-stage optimization mechanism improves the collaborative utilization efficiency of heterogeneous resources by jointly scheduling terminal computing power, communication bandwidth and data compression rate. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a method flowchart of an embodiment of the present application;

[0054] Figure 2 is a comparison experiment result chart of an embodiment of the present application. DETAILED DESCRIPTION

[0055] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0056] The present application provides the following technical solutions: a device access method and system based on local heterogeneous networking.

[0057] Embodiment 1:

[0058] Specifically referring to Figure 1 The embodiment provides a device access method based on local heterogeneous networking, and the specific steps include:

[0059] S1, obtaining running data of local heterogeneous networking, the local heterogeneous networking including a perception layer, a relay layer, an edge layer and a service layer; the method further includes data cleaning on the running data, the data cleaning including processing missing values, abnormal values and data format unification;

[0060] S11, the perception layer contains sensors deployed on distributed photovoltaic, electric vehicle charging piles, distributed energy storage and other power equipment, which real-time collects voltage, active power, reactive power and other data; the collected perception layer data is transmitted to the relay layer by high-speed radio frequency communication or long-distance radio communication;

[0061] S12, the relay layer is built-in a lightweight sensing algorithm terminal, which is used for receiving the perception layer data and transmitting the perception layer data to the edge layer by low-voltage power line high-speed carrier communication or 5G; wherein the lightweight sensing algorithm terminal includes a multi-mode communication module, a compression scheme decision module and a time slicing decision module;

[0062] The multi-mode communication module includes high-speed radio frequency communication, long-distance radio communication, low-voltage power line high-speed carrier communication and 5G; the high-speed radio frequency communication or long-distance radio communication is responsible for receiving the perception layer data, and the low-voltage power line high-speed carrier communication or 5G is responsible for relaying the perception layer data to the edge layer for processing;

[0063] The compression scheme decision module obtains a compression scheme decision through a data compression optimization algorithm based on a performance bias perception inertia weight;

[0064] The time slicing decision module obtains an optimal time slicing ratio through a time slicing optimization algorithm based on a guidance degree and a guidance migration probability;

[0065] S13, the edge layer built-in multi-modal communication intelligent fusion container level gateway is used for processing data uploaded by the lightweight sensing algorithm terminal, supporting power business operation such as device detection, load control, distributed power regulation of the business layer; the multi-modal communication intelligent fusion container level gateway comprises a power supply module, an HPLC / 5G communication module, a container module, an optimization problem modeling module and a lightweight sensing algorithm terminal scheduling decision module;

[0066] The power supply module is responsible for power supply for the multi-modal communication intelligent fusion container level gateway device;

[0067] The HPLC / 5G communication module is responsible for receiving information such as relay data queue backlog and queuing delay uploaded by the lightweight sensing algorithm terminal, and its own data queue backlog and queuing delay; and issuing lightweight sensing algorithm terminal scheduling, data compression strategy and time slicing ratio;

[0068] The container module provides virtualized computing resources for different power businesses;

[0069] The optimization problem modeling module is used for constructing a device matching optimization problem, and converting a long-term queuing delay constraint into a short-term queue stability constraint;

[0070] The lightweight sensing algorithm terminal scheduling decision module issues terminal scheduling decisions to the relay layer lightweight sensing algorithm terminal through the HPLC / 5G communication module;

[0071] S14, the business layer is responsible for device detection, load control and distributed power regulation business;

[0072] S2, a device matching optimization problem is constructed, specifically:

[0073] The objective function of the device matching optimization problem, the device matching optimization problem includes an objective function constructed with the minimum scheduling variable, time slicing variable and data compression scheme as the target, which is expressed by the formula as:

[0074] ;

[0075] In the formula, Indicates the device matching optimization problem; Indicates the time slot The cluster represented by the i th lightweight sensing algorithm terminal and the Scheduling variables among channels; Indicates the first Time slot number The time-slicing variable of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number The cluster represented by the lightweight synergistic computing terminal is selected as the first... Indicator variables for various data compression schemes; Indicates the first The index value of the time slot; Represents a set of time slots; Indicates the first Time slot number The relay data transmission queuing latency of a cluster represented by a lightweight synaptic terminal; Indicates the first Time slot number Local data transmission queuing latency of a cluster represented by a lightweight synesthetic terminal; Indicates the first The index value of a lightweight synesthetic computing terminal; Indicates the number of lightweight synergistic computing terminals; Indicates the first The index value of each channel; Describes the minimum value function;

[0076] The constraints, constructed using channel selection constraints, lightweight inductive computing terminal scheduling constraints, time fragmentation constraints, range constraints of data compression scheme selection indicator variables, relay data transmission queuing delay constraints, and local acquisition data transmission queuing delay constraints, are expressed by the following formula:

[0077] ;

[0078] In the formula, Represents the constraints in the equipment matching optimization problem; Indicates channel selection constraints; This represents the scheduling constraints of a lightweight synesthetic computing terminal; Indicates time-sharing constraints; This indicates the range constraints of the indicator variable for selecting a data compression scheme. This indicates the queuing delay constraint for relay data transmission; This indicates the queuing delay constraint for local data acquisition and transmission; Indicates the first The cluster represented by a lightweight synesthetic computing terminal; This represents a lightweight synesthetic computing terminal cluster. Indicates the first One channel; Represents the channel set; Indicates the number of channels; Indicates the first Index values ​​for various data compression schemes; Indicates the total number of data compression schemes; This indicates the maximum tolerable relay data transmission queuing delay; This indicates the maximum tolerable queuing delay for local data acquisition.

[0079] Among them, the Time slot number The relay data transmission queuing latency and local data acquisition data transmission queuing latency of the cluster represented by a lightweight inductive computing terminal are expressed by the following formula:

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] In the formula, Indicates the first Time slot number The relay data queue of the cluster represented by a lightweight synaptic terminal is backlogged. Indicates the first Time slot number The local data collection queue of the cluster represented by a lightweight synesthetic computing terminal is backlogged; Indicates the first Time slot number The relay data queue of the cluster represented by a lightweight synaptic terminal is backlogged. Indicates the first Time slot number The local data collection queue of the cluster represented by a lightweight synesthetic computing terminal is backlogged; This represents the average arrival rate of the data queue backlog. This indicates the average arrival rate of the local data collection queue backlog; Indicates the first Time slot number The amount of relay data received by the cluster represented by a lightweight synaptic terminal; Indicates the first Time slot number The amount of local data received by the cluster represented by a lightweight synaptic terminal; Indicates the length of a single time slot; Indicates the first Compression ratio values ​​for various data compression schemes; Indicates the first Time slot number The cluster represented by the lightweight synergistic computing terminal and the first Transmission rate between channels; Indicates the first Compression rate of various data compression schemes; Represents the maximum value function;

[0085] S3. Introduce a virtual queue to transform the device matching optimization problem, resulting in the transformed device matching optimization problem and the virtual queue, expressed by the following formula:

[0086] ;

[0087] ;

[0088] ;

[0089] In the formula, This indicates the equipment matching optimization problem after conversion; Indicates the first Time slot number A virtual queue representing the relay data queuing latency of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number A virtual queue representing the local data acquisition queuing latency of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number A virtual queue representing the relay data queuing latency of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number A virtual queue representing the local data acquisition queuing latency of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first The optimization objective function after introducing virtual queues into time slots;

[0090] S4. Decompose the optimized device matching optimization problem into a first stage and a second stage; the first stage is a lightweight sensory terminal scheduling sub-problem, and the second stage is a data compression sub-problem and a time sharding problem;

[0091] The first stage includes a first sub-objective function constructed with minimizing the scheduling variable as the objective function, and a first sub-constraint condition constructed with channel selection constraints and lightweight inductive computing terminal scheduling constraints as constraints, expressed by the formula:

[0092] ;

[0093] ;

[0094] wherein, denotes the first stage sub-problem;

[0095] The second stage includes a second sub-objective function constructed with time-slicing variables and data compression scheme minimization as the objective function, a second sub-constraint condition constructed with time-slicing constraints and range constraints of data compression scheme selection indicator variables, and is expressed as:

[0096] ;

[0097] ;

[0098] wherein, denotes the second stage sub-problem;

[0099] S5, solving the first stage by using a lightweight common sense algorithm terminal scheduling algorithm, the lightweight common sense algorithm terminal scheduling algorithm includes an initialization stage, a cluster matching utility evaluation stage, a preference list establishment stage, a matching application stage and a conflict coordination stage;

[0100] S51, the initialization stage specifically initializes the data queue backlog of all lightweight common sense algorithm terminals in the relay layer, including relay data queue backlog and local acquisition data queue backlog, the number of matching requests received by the channel is an empty set and the matching cost is 0;

[0101] S52, the cluster matching utility evaluation stage specifically calculates the data transmission queuing delay and the corresponding coverage value coefficient of all lightweight common sense algorithm terminals represented clusters through weighted calculation, to obtain the matching utility of each lightweight common sense algorithm terminal represented cluster, and is expressed as:

[0102] ;

[0103] ;

[0104] wherein, denotes the matching utility of the cluster represented by the i-th lightweight common sense algorithm terminal in the j-th time slot; denotes the preset average local acquisition data transmission queuing delay of the cluster represented by the i-th lightweight common sense algorithm terminal in the j-th time slot; denotes the preset relay data transmission queuing delay of the cluster represented by the i-th lightweight common sense algorithm terminal in the j-th time slot; ​​​​​​This represents the data transmission weighting coefficient; This represents the relay coverage weighting coefficient; Indicates the first Time slot number The number of sensors in the cluster represented by a lightweight synaptic terminal; Indicates the first Time slot number The coverage value coefficient of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number The density of electrical equipment within the cluster coverage area represented by a lightweight synaptic terminal; Indicates the first Time slot number The regional value weight of the cluster represented by each lightweight synesthetic computing terminal; Indicates the first Time slot number The first lightweight synergistic computing terminal represents the cluster coverage area of ​​the first... The business importance of individual sensor data; Indicates the first Sensor index values; Indicates the first The number of sensors in the cluster represented by a lightweight synaptic terminal;

[0105] S53. The preference list establishment phase specifically involves calculating the preference value for different channels for each lightweight synergistic computing terminal's cluster based on data transmission queuing delay and data queue backlog, expressed by the formula:

[0106] ;

[0107] In the formula, Indicates the first Time slot number The cluster represented by the lightweight synergistic computing terminal is the first... The preference values ​​for each channel; Indicates the first Time slot number The cluster represented by the lightweight synergistic computing terminal is the first... Matching cost of each channel; Indicates the first The index value of each time slot;

[0108] All generated preference values ​​are sorted in descending order to obtain the matching preference list of the lightweight synesthetic computing terminal;

[0109] S54. The matching request stage specifically involves, based on the matching preference list, the unmatched lightweight sensory computing terminal initiating a matching request to the channel with the highest preference value; if... ,in Indicates the first The number of matching requests received by each channel indicates that there is no resource contention conflict on the currently selected channel. The lightweight sensory computing terminal that initiated the matching request is then matched with the current channel. Once the matching is complete, the process returns to the cluster matching utility evaluation phase. This indicates that a resource competition conflict has occurred, and the process has entered the conflict coordination stage.

[0110] S55. The conflict coordination phase specifically involves updating the matching cost of the current channel based on the matching cost increase step size, the connectivity of the cluster represented by the lightweight synergistic computing terminal, and the matching utility of the cluster represented by each lightweight synergistic computing terminal, as expressed by the formula:

[0111] ;

[0112] In the formula, Indicates the first The cluster represented by the lightweight synergistic computing terminal is the first... Increase the matching cost step size for each channel; Indicates the first Time slot number The connectivity of a cluster represented by a lightweight synesthetic computing terminal; Indicates the updated number Time slot number The cluster represented by the lightweight synergistic computing terminal is the first... Matching cost of each channel;

[0113] Among them, the first Time slot number The connectivity of a cluster represented by a lightweight synergistic computing terminal can be expressed by the following formula:

[0114] ;

[0115] ;

[0116] ;

[0117] In the formula, This represents a function that finds the second smallest eigenvalue of a matrix. Indicates the first Time slot number The degree matrix of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number The adjacency matrix of a cluster represented by a lightweight synesthetic computing terminal; Indicates the first Time slot number The cluster represented by the lightweight synergistic computing terminal is the first The sensor and the first The connectivity variables between sensors, where if Then it means the first The sensor and the first If the sensors are connected, Then it means the first The sensor and the first The sensors are not interconnected; Indicates the first The index value of each sensor; Indicates the first The index value of each sensor; Indicates the first The number of sensors in the cluster represented by a lightweight synaptic terminal;

[0118] S56. Iterative matching process until all lightweight synesthetic computing terminals in the relay layer have completed matching, and obtain lightweight synesthetic computing terminal scheduling decision;

[0119] S6. Solve the second stage using data compression optimization algorithm and time partitioning optimization algorithm to obtain the optimal compression scheme and the optimal time partitioning ratio;

[0120] S61. The data compression optimization algorithm is specifically based on the performance deviation-aware adaptive particle swarm optimization algorithm. It utilizes an improved performance deviation-aware inertia weight to calculate the objective function value obtained for each particle in the current round of search, expressed by the formula:

[0121] ;

[0122] In the formula, Indicates the first The objective function value of the round search; This indicates the preset data transmission weighting coefficient; Indicates the first The index value for round-robin search;

[0123] The improved performance deviation sensing inertia weight is expressed by the following formula:

[0124] ;

[0125] In the formula, Indicates the first Performance deviation perception inertia weight in round-robin search; This represents the preset initial inertia weight; Indicates the preset termination inertia weight; Indicates the maximum number of search rounds; Indicates the activation function; Indicates the first The objective function value of the round search;

[0126] The iterative search process specifically involves comparing the current fitness value of the current particle with the individual optimal value of the current particle. If the current fitness value is less than the individual optimal value, then the individual optimal value is updated to the current fitness value.

[0127] The iteration stops when the maximum number of search rounds is reached or the objective function converges. The individual optimal extreme value of all particles is traversed, and the smallest individual optimal extreme value is taken as the global optimal extreme value, i.e., the optimal compression scheme.

[0128] S62, The time slice optimization algorithm specifically optimizes time slices based on guidance degree and guidance migration probability;

[0129] Based on the data queue backlog and the corresponding virtual queue, the orientation degree of the cluster represented by each lightweight synesthetic computing terminal is calculated, expressed by the formula:

[0130] ;

[0131] In the formula, Indicates the first Time slot number The orientation of a cluster represented by a lightweight synesthetic computing terminal;

[0132] Based on the data queue backlog within a preset historical time window, the directed migration probability of the cluster represented by each lightweight synesthetic computing terminal is calculated, expressed by the formula:

[0133] ;

[0134] ;

[0135] ;

[0136] In the formula, Indicates the first Time slot number The probability of guided migration of a cluster represented by a lightweight synesthetic computing terminal; This represents the preset time-slicing weight factor; Indicates the preset length of the historical time window; Represents an exponential function;

[0137] The optimal time-slicing ratio is obtained by solving the time-slicing variables in the current time slot based on the guidance degree and guidance migration probability, expressed by the formula:

[0138] ;

[0139] In the formula, Represents a random number; This indicates the preset adjustment standard value;

[0140] S7. Obtain the device to be connected, and connect the device to be connected to the local heterogeneous network based on the lightweight inductive computing terminal scheduling decision, the optimal compression scheme and the optimal time segmentation ratio.

[0141] S8. The method further includes data monitoring of the local heterogeneous network, specifically monitoring in real time the indicators of lightweight synesthetic computing terminal scheduling decision, data compression optimization algorithm and time slicing optimization algorithm, wherein the indicator of lightweight synesthetic computing terminal scheduling decision is scheduling delay, the indicator of data compression optimization algorithm is compression ratio, and the indicator of time slicing optimization algorithm is slicing deviation.

[0142] If any indicator is detected to exceed the preset threshold, the multimodal communication intelligent fusion container-level gateway built into the edge layer will be triggered to re-solve the device matching optimization problem and generate a new lightweight synergistic computing terminal scheduling decision, data compression optimization algorithm and time slicing optimization algorithm.

[0143] S9, please see Figure 2 The optimization method proposed in this invention was compared with two other algorithms in terms of end-to-end latency changes. Algorithm 1 was a confidence interval upper bound algorithm, and Algorithm 2 was a genetic algorithm. Specifically, the end-to-end queuing latency of the optimization method proposed in this invention decreased by 6.19% and 12.67%, respectively.

[0144] Example 2:

[0145] This embodiment provides a device access system based on a local heterogeneous network. The system includes a data acquisition module, an optimization problem construction module, a solution module, and a result output module, wherein:

[0146] The data acquisition module is used to acquire the operational data of the local heterogeneous network, which includes a perception layer, a relay layer, an edge layer, and a service layer; wherein the relay layer includes a lightweight sensory computing terminal; and transmits the operational data to the optimization problem construction module;

[0147] The optimization problem construction module is used to construct the device matching optimization problem and introduce a virtual queue to transform the device matching optimization problem; the optimized device matching optimization problem is decomposed into a first stage and a second stage; wherein the first stage is a lightweight synesthetic terminal scheduling sub-problem, and the second stage is a data compression sub-problem and a time sharding sub-problem;

[0148] The solution module is used to solve the first stage using a lightweight synesthetic computing terminal scheduling algorithm to obtain the lightweight synesthetic computing terminal scheduling decision; and to solve the second stage using a data compression optimization algorithm and a time segmentation optimization algorithm to obtain the optimal compression scheme and the optimal time segmentation ratio.

[0149] The result output module acquires the device to be connected and, based on the lightweight inductive computing terminal scheduling decision, the optimal compression scheme, and the optimal time fragmentation ratio, connects the device to be connected to the local heterogeneous network.

[0150] Example 3:

[0151] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a device access method based on a local heterogeneous network as described in any embodiment of the present invention.

[0152] Example 4:

[0153] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a device access method based on a local heterogeneous network as described in any embodiment of the present invention.

[0154] It is worth noting that the system, electronic device, and computer-readable storage medium described in this invention are all based on the same principle as the method described in Embodiment 1, and will not be repeated here.

[0155] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A device access method based on local heterogeneous networking, characterized in that, The method includes: Obtain operational data of a local heterogeneous network, which includes a perception layer, a relay layer, an edge layer, and a service layer; wherein the relay layer includes a lightweight sensory computing terminal; A device matching optimization problem is constructed, and a virtual queue is introduced to transform the device matching optimization problem; the optimized device matching optimization problem is decomposed into a first stage and a second stage; wherein the first stage is a lightweight synesthetic terminal scheduling sub-problem, and the second stage is a data compression sub-problem and a time sharding sub-problem; The device matching optimization problem includes an objective function constructed with the goal of minimizing scheduling variables, time slicing variables, and data compression schemes, and constraint conditions constructed with channel selection constraints, lightweight inductive computing terminal scheduling constraints, time slicing constraints, range constraints of data compression scheme selection indicator variables, relay data transmission queuing delay constraints, and local acquisition data transmission queuing delay constraints. A virtual queue is introduced to transform the device matching optimization problem, resulting in a transformed device matching optimization problem and a virtual queue; the optimized device matching optimization problem is then decomposed into a first stage and a second stage. The first stage includes a first sub-objective function constructed with minimizing the scheduling variable as the objective function, and a first sub-constraint condition constructed with channel selection constraints and lightweight inductive computing terminal scheduling constraints as constraints; The second stage includes a second sub-objective function constructed with minimizing the time-slicing variable and the data compression scheme as the objective function, and a second sub-constraint condition constructed with the range constraints of the time-slicing constraint and the data compression scheme selection indicator variable as constraints; The first stage is solved using a lightweight synesthetic computing terminal scheduling algorithm to obtain the lightweight synesthetic computing terminal scheduling decision; the second stage is solved using a data compression optimization algorithm and a time segmentation optimization algorithm to obtain the optimal compression scheme and the optimal time segmentation ratio. The time-slicing optimization algorithm specifically optimizes time-slicing based on guidance degree and guidance-transition probability, as follows: Based on the data queue backlog and the corresponding virtual queue, the orientation degree of the cluster represented by each lightweight synesthetic computing terminal is calculated. Based on the data queue backlog within a preset historical time window, the directional migration probability of the cluster represented by each lightweight synesthetic computing terminal is calculated. The optimal time segmentation ratio is obtained by solving the time segmentation variables under the current time slot based on the guidance degree and guidance migration probability. The system acquires the devices to be connected and, based on lightweight inductive computing terminal scheduling decisions, optimal compression schemes, and optimal time fragmentation ratios, connects the devices to the local heterogeneous network.

2. The device access method based on local heterogeneous networking according to claim 1, characterized in that, The method also includes data cleaning of the running data, which includes handling missing values, outliers, and data format standardization.

3. The device access method based on local heterogeneous networking according to claim 1, characterized in that, The lightweight synesthetic terminal scheduling algorithm includes an initialization phase, a cluster matching utility evaluation phase, a preference list establishment phase, a matching request phase, and a conflict coordination phase, specifically: The initialization phase specifically involves initializing the data queue backlog of all lightweight synaptic terminals, the number of matching requests received by the channel, and the matching cost. The cluster matching utility evaluation stage specifically involves calculating the data transmission queuing delay and corresponding coverage value coefficient of the clusters represented by all lightweight synesthetic computing terminals using a weighted average, to obtain the matching utility of the cluster represented by each lightweight synesthetic computing terminal. The preference list establishment phase specifically involves calculating the preference value of each lightweight synesthetic computing terminal for different channels based on data transmission queuing delay and data queue backlog; and sorting all the generated preference values ​​in descending order to obtain the matching preference list of the lightweight synesthetic computing terminal. The matching application stage is specifically based on the matching preference list. Unmatched lightweight synergistic computing terminals will initiate a matching request to the channel with the highest preference value. If there is no resource contention conflict in the currently selected channel, the matching is completed and the process moves to the cluster matching utility evaluation stage. Otherwise, it indicates that there is a resource contention conflict and the process moves to the conflict coordination stage. The conflict coordination phase specifically involves updating the matching cost of the current channel based on the matching cost increase step size, the connectivity of the cluster represented by the lightweight synesthetic computing terminal, and the matching utility of the cluster represented by each lightweight synesthetic computing terminal. The iterative matching process continues until all lightweight synesthetic computing terminals have completed matching, thus obtaining the lightweight synesthetic computing terminal scheduling decision.

4. The device access method based on local heterogeneous networking according to claim 1, characterized in that, The data compression optimization algorithm is specifically based on the performance deviation-aware adaptive particle swarm optimization algorithm, and specifically: The objective function value obtained in the current round of search for each particle is calculated by using the improved performance deviation sensing inertial weight. The iterative search process specifically involves comparing the current fitness value of the current particle with the individual optimal value of the current particle. If the current fitness value is less than the individual optimal value, then the individual optimal value is updated to the current fitness value. The iteration stops when the maximum number of search rounds is reached or the objective function converges. The individual optimal extreme value of all particles is traversed, and the smallest individual optimal extreme value is taken as the global optimal extreme value, i.e., the optimal compression scheme.

5. A device access method based on local heterogeneous networking according to claim 1, characterized in that, The method also includes data monitoring of the local heterogeneous network, specifically real-time monitoring of the indicators of lightweight synesthetic computing terminal scheduling decision, data compression optimization algorithm and time slicing optimization algorithm, wherein the indicator of lightweight synesthetic computing terminal scheduling decision is scheduling delay, the indicator of data compression optimization algorithm is compression ratio, and the indicator of time slicing optimization algorithm is slicing deviation. If any indicator is detected to exceed the preset threshold, the multimodal communication intelligent fusion container-level gateway built into the edge layer will be triggered to re-solve the device matching optimization problem and generate a new lightweight sensory terminal scheduling decision, data compression optimization algorithm, and time slicing optimization algorithm.

6. A device access system based on a local heterogeneous network, characterized in that, The system includes a data acquisition module, an optimization problem construction module, a solution module, and a result output module, wherein: The data acquisition module is used to acquire the operational data of the local heterogeneous network, which includes a perception layer, a relay layer, an edge layer, and a service layer; wherein the relay layer includes a lightweight sensory computing terminal; and transmits the operational data to the optimization problem construction module; The optimization problem construction module is used to construct the device matching optimization problem and introduce a virtual queue to transform the device matching optimization problem; the optimized device matching optimization problem is decomposed into a first stage and a second stage; wherein the first stage is a lightweight synesthetic terminal scheduling sub-problem, and the second stage is a data compression sub-problem and a time sharding sub-problem; The device matching optimization problem includes an objective function constructed with the goal of minimizing scheduling variables, time slicing variables, and data compression schemes, and constraint conditions constructed with channel selection constraints, lightweight inductive computing terminal scheduling constraints, time slicing constraints, range constraints of data compression scheme selection indicator variables, relay data transmission queuing delay constraints, and local acquisition data transmission queuing delay constraints. A virtual queue is introduced to transform the device matching optimization problem, resulting in a transformed device matching optimization problem and a virtual queue; the optimized device matching optimization problem is then decomposed into a first stage and a second stage. The first stage includes a first sub-objective function constructed with minimizing the scheduling variable as the objective function, and a first sub-constraint condition constructed with channel selection constraints and lightweight inductive computing terminal scheduling constraints as constraints; The second stage includes a second sub-objective function constructed with minimizing the time-slicing variable and the data compression scheme as the objective function, and a second sub-constraint condition constructed with the range constraints of the time-slicing constraint and the data compression scheme selection indicator variable as constraints; The solution module is used to solve the first stage using a lightweight synesthetic computing terminal scheduling algorithm to obtain the lightweight synesthetic computing terminal scheduling decision; and to solve the second stage using a data compression optimization algorithm and a time segmentation optimization algorithm to obtain the optimal compression scheme and the optimal time segmentation ratio. The time-slicing optimization algorithm specifically optimizes time-slicing based on guidance degree and guidance-transition probability, as follows: Based on the data queue backlog and the corresponding virtual queue, the orientation degree of the cluster represented by each lightweight synesthetic computing terminal is calculated. Based on the data queue backlog within a preset historical time window, the directional migration probability of the cluster represented by each lightweight synesthetic computing terminal is calculated. The optimal time segmentation ratio is obtained by solving the time segmentation variables under the current time slot based on the guidance degree and guidance migration probability. The result output module acquires the device to be connected and, based on the lightweight inductive computing terminal scheduling decision, the optimal compression scheme, and the optimal time fragmentation ratio, connects the device to be connected to the local heterogeneous network.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a device access method based on local heterogeneous networking as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a device access method based on a local heterogeneous network as described in any one of claims 1 to 5.

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