Data acquisition and computing system based on edge computing

Abstract

The application relates to the technical field of data collection and computation, in particular to a data collection and computation system based on edge computation, which comprises a dynamic sensing and adjusting module, an edge redundancy packet grouping module, a priority transmission scheduling module, a distributed computation resource management module and a computation task integration module.In the application, through dynamic sensing of network load and signal strength, work parameters are dynamically adjusted in combination with transmission coverage range, node cooperative work configuration optimization is realized, through data packet grouping processing, in combination with redundancy coding statistics and integrity checking, data transmission stability and safety are realized, through path and timing priority sorting and dynamic adjustment, transmission time delay is reduced, data transmission efficiency and path utilization rate are improved, based on cooperative relationship optimization distribution, computation resource balanced utilization is realized, system bottleneck problems are reduced, task execution conditions are recorded and analyzed in real time, task processing strategies are optimized, and edge device task processing efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and computing technology, and in particular to a data acquisition and computing system based on edge computing. Background Technology

[0002] The field of data acquisition and computing technology focuses on capturing data through various sensors and devices and performing real-time or near-real-time analysis to support decision-making and improve operational efficiency. It involves data acquisition, transmission, storage, and analysis, and is frequently applied in areas such as the Internet of Things (IoT), industrial automation, environmental monitoring, and smart cities. It typically requires integration with efficient computing architectures to process large volumes of data and extract useful information. The key to these systems lies in their ability to process and analyze data quickly and accurately, as well as ensuring the security and integrity of data during acquisition and transmission.

[0003] Edge computing-based data acquisition and processing systems refer to systems that perform preliminary data processing and analysis on edge devices near the data source. This reduces reliance on central servers, lowers latency, improves response speed, and reduces bandwidth requirements during data transmission. They have wide applications, particularly in scenarios requiring rapid decision support, such as autonomous vehicles, smart manufacturing, and urban security monitoring. By processing data locally, edge computing systems can provide more real-time data analysis and processing, optimizing the overall system performance and efficiency.

[0004] Existing technologies lack mechanisms for sensing and adjusting the dynamic states of nodes, resulting in insufficient inter-node collaboration and low resource utilization efficiency. During data transmission, the inability to effectively allocate redundant information flexibly easily leads to packet integrity loss, affecting transmission stability. The lack of real-time dynamic adjustment in path selection and transmission timing results in reduced transmission efficiency and significantly increased latency under high load. Task allocation relies heavily on the central server, failing to fully consider the collaboration capabilities and resource allocation optimization among edge nodes, easily leading to idle or overloaded node computing power, weakening overall system performance. The lack of real-time feedback and analysis of task execution status hinders rapid response and dynamic optimization, making it unsuitable for the refined needs of complex real-time scenarios and limiting the system's adaptability to diverse environments. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a data acquisition and computing system based on edge computing.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a data acquisition and computing system based on edge computing includes:

[0007] The dynamic sensing and adjustment module collects the signal strength and network load level of the edge nodes, calculates the transmission frequency of the edge nodes, calculates the dynamic working range value of the edge nodes based on the transmission coverage, and calculates the collaborative working parameters of the edge nodes based on the determined results and the signal coverage distribution, generating dynamic node collaborative configuration data.

[0008] The edge redundancy packet grouping module performs grouping processing based on the dynamic node collaborative configuration data, calculates the redundancy bit allocation ratio of data packets, counts the amount of redundancy coding, adds redundancy information to each group of data packets according to the statistical results and performs integrity verification, and performs data packet validity grouping based on the integrity verification results to form a valid data grouping table.

[0009] The priority transmission scheduling module calculates the priority ranking value of the data packets according to the effective data packet table, adjusts the transmission path and transmission timing distribution, records the transmission delay distribution according to the path selection and distribution, and obtains the path transmission efficiency distribution record.

[0010] Based on the path transmission efficiency distribution record, the distributed computing resource management module calculates the resource allocation requirements of edge nodes, counts the task load distribution, adjusts the task processing content of edge nodes, records the collaborative processing relationship between edge nodes in combination with the task distribution, and forms the edge node collaborative processing analysis results.

[0011] The computing task integration module calculates the task execution time based on the collaborative processing analysis results of the edge nodes, records the completion status of each edge node's task, and obtains a statistical record of computing task completion.

[0012] As a further aspect of the present invention, the calculation steps for the dynamic working range value are as follows:

[0013] The signal strength and network load level of edge nodes are collected, and the average signal strength and network load occupancy rate are calculated respectively. By comparing the fluctuation range of signal strength with the ratio of network load occupancy, the transmission frequency of edge nodes is generated.

[0014] Based on the transmission frequency of the edge nodes and their transmission coverage, the following formula is used:

[0015] ;

[0016] Signal coverage radius of computing nodes The transmission coverage of the edge nodes is obtained, where, For transmission frequency, This is the standardized value of the signal strength. This is a normalized parameter for the load level;

[0017] Based on the transmission coverage of the edge node, combined with the dynamic change rate of the coverage and the actual working area limitation of the edge node, the distribution density of the signal strength within the coverage area is adjusted to determine the boundary value of the working area and generate the dynamic working range value of the edge node.

[0018] As a further aspect of the present invention, the step of obtaining the dynamic node collaborative configuration data is as follows:

[0019] Based on the dynamic working range value of the edge node, analyze the distribution characteristics of signal strength within the coverage area, record the maximum and minimum signal strength, the average signal strength, and the intersection area of ​​the coverage area, analyze the uniformity of signal distribution within the coverage area, and generate a preliminary node collaboration range.

[0020] Based on the preliminary node coordination range, and considering the node's transmission frequency and signal distribution characteristics within the coverage area, the following formula is used:

[0021] ;

[0022] Calculate the collaborative efficiency value of edge nodes Generate a record of the distribution of node collaboration efficiency, where... For the first The area covered by the collaborative operation of each node. This represents the average signal strength within the corresponding range. The total number of nodes participating in the collaboration;

[0023] Based on the node collaboration efficiency distribution record, the collaboration matching between edge nodes is analyzed, the numerical distribution analysis of signal strength and collaboration efficiency is performed simultaneously, the matching characteristics of overlapping areas of signal coverage are determined, and dynamic node collaboration configuration data is generated.

[0024] As a further aspect of the present invention, the statistical steps for the amount of redundant coding are as follows:

[0025] Based on the dynamic node collaborative configuration data, the basic information of node groups and data packets is extracted. According to the signal coverage range and signal strength distribution characteristics within the group, the average signal strength and the number of data packets in each group are calculated to generate a basic characteristic table of group data packets.

[0026] Based on the aforementioned basic characteristics table of packet data, and combined with the average signal strength and number of data packets within the packet, the following formula is used:

[0027] ;

[0028] Calculate the first Redundancy bit allocation ratio of group data packets Generate a grouped redundancy bit allocation table, where... The average signal strength The number of data packets in the current group. The maximum number of packets in all groups;

[0029] Based on the packet redundancy bit allocation table, and combined with the number of data packets and the redundancy bit allocation ratio, the redundancy coding amount of each data packet is determined group by group, and the total redundancy coding amount of each group is calculated to generate a packet redundancy coding amount distribution table.

[0030] As a further aspect of the present invention, the step of obtaining the effective data grouping table is as follows:

[0031] Based on the aforementioned packet redundancy coding distribution table, redundant information is added to the corresponding data packets one by one according to the redundancy coding amount of each group of data packets. By merging the original content of the data packets with the redundant information, a data packet with enhanced redundancy is generated.

[0032] Based on the data packets with enhanced redundancy, check the integrity of the data packets one by one, analyze the matching between the redundancy information in each group of data packets and the actual received data, record the integrity characteristic parameters of each group of data packets, and generate a data packet integrity characteristic table.

[0033] Based on the data packet integrity characteristic table, the data packet groups are filtered for validity. Data packets that meet the integrity criteria are classified as valid groups, while data packets that do not meet the integrity criteria are marked as invalid, thus generating a valid data packet table.

[0034] As a further aspect of the present invention, the step of obtaining the path transmission efficiency distribution record is as follows:

[0035] Based on the effective data grouping table, the number of target nodes, data volume, and transmission time limit for each data packet are extracted using the following formula:

[0036] ;

[0037] Calculate the first Initial priority value of group data packets By normalizing the priority values ​​of each group of data, preliminary data packet priority ranking values ​​are generated. For data volume, For transmission time limit, For the maximum transmission time limit, The target number of nodes;

[0038] Based on the initial data packet priority ranking value, and combined with path capacity, path load status and data packet transmission requirements, priority ranking is adjusted group by group. The path transmission timing distribution is optimized by adjusting and the distribution order is dynamically adjusted according to the path load, generating the adjusted transmission path and transmission timing distribution record.

[0039] Based on the adjusted transmission path and transmission timing distribution record, the transmission delay of each path is calculated, the relationship between delay and path capacity and load is analyzed, and a path transmission efficiency distribution record is generated.

[0040] As a further aspect of the present invention, the step of obtaining the edge node collaborative processing analysis result is as follows:

[0041] The path transmission efficiency distribution record is called to extract the transmission efficiency value of each path and the task distribution of the corresponding edge nodes. The resource allocation requirement value of each node is calculated, and the task requirement and transmission efficiency are normalized to generate the preliminary resource allocation result of the edge nodes.

[0042] Based on the preliminary resource allocation results and the task load distribution of the edge nodes, the task processing content of each node is adjusted, the matching degree between the load and resource requirements of each node is analyzed, the task processing content is dynamically allocated, and the resource and task transfer between edge nodes during the task adjustment process is recorded to generate collaborative processing records between nodes.

[0043] Based on the collaborative processing records between the nodes, the collaborative processing relationship between edge nodes is analyzed. By statistically analyzing the interaction of task allocation and processing resources between each node, the collaborative processing analysis results of edge nodes are formed.

[0044] As a further aspect of the present invention, the step of obtaining the statistical records upon completion of the computational task is as follows:

[0045] Based on the analysis results of edge node collaborative processing, the task distribution and collaborative processing relationship of each edge node are analyzed. Combining the start and end times of the tasks, the actual execution time of each task is calculated, and the execution time of each task is summarized to form an edge node task execution time record.

[0046] Based on the task execution time records of the edge nodes, the total task completion time of each edge node is calculated by comparing the task execution time with the resource allocation efficiency, and the task completion status record of the node is obtained.

[0047] Based on the task completion records of the nodes, combined with the task completion ratio and execution status of each edge node, the overall task completion distribution is statistically analyzed, the task completion status of each node is summarized, and a statistical record of task completion is formed.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In this invention, by dynamically sensing network load and signal strength and dynamically adjusting working parameters in conjunction with transmission coverage, the configuration optimization of node collaborative work is achieved. By processing data packets in groups and combining redundancy coding statistics and integrity verification, data transmission stability and security are achieved. By prioritizing and dynamically adjusting path and timing, transmission latency is reduced, data transmission efficiency and path utilization are improved. Based on the optimized allocation of collaborative relationships, balanced utilization of computing resources is achieved, reducing system bottlenecks. The task execution status is recorded and analyzed in real time, task processing strategies are optimized, and the task processing efficiency of edge devices is improved. Attached Figure Description

[0050] Figure 1 This is a system flowchart of the present invention;

[0051] Figure 2 This is a flowchart illustrating the determination of the dynamic working range value of the present invention.

[0052] Figure 3 This is a flowchart illustrating the process of acquiring dynamic node collaborative configuration data in this invention.

[0053] Figure 4 This is a flowchart illustrating the statistical process of redundant coding in this invention.

[0054] Figure 5 This is a flowchart illustrating the process of obtaining the effective data grouping table in this invention.

[0055] Figure 6 This is a flowchart illustrating the process of obtaining the path transmission efficiency distribution record in this invention.

[0056] Figure 7 This is a flowchart illustrating the process of obtaining the analysis results of edge node collaborative processing in this invention.

[0057] Figure 8 This is a flowchart illustrating the process of obtaining statistical records for the completion of computational tasks in this invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0059] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0060] Please see Figure 1 Edge computing-based data acquisition and computing systems include:

[0061] The dynamic sensing and adjustment module collects the signal strength and network load level of the edge nodes, calculates the transmission frequency of the edge nodes, determines the dynamic working range value of the edge nodes based on the transmission coverage, and calculates the collaborative working parameters of the edge nodes based on the determined results and the signal coverage distribution, generating dynamic node collaborative configuration data.

[0062] The edge redundancy packet grouping module performs grouping processing based on dynamic node collaborative configuration data, calculates the redundancy bit allocation ratio of data packets, counts the amount of redundancy coding, adds redundancy information to each group of data packets according to the statistical results and performs integrity verification, and groups data packets based on the integrity verification results to form an effective data grouping table.

[0063] The priority transmission scheduling module calculates the priority ranking value of data packets based on the effective data packet table, adjusts the transmission path and transmission timing distribution, records the transmission delay distribution based on the path selection and distribution, and obtains the path transmission efficiency distribution record.

[0064] The distributed computing resource management module calculates the resource allocation requirements of edge nodes based on the path transmission efficiency distribution record, counts the task load distribution, adjusts the task processing content of edge nodes, records the collaborative processing relationship between edge nodes in combination with the task distribution, and forms the edge node collaborative processing analysis results.

[0065] The computing task integration module analyzes the collaborative processing results of edge nodes, calculates the task execution time, records the completion status of each edge node's task, and obtains a statistical record of computing task completion.

[0066] The dynamic node collaborative configuration data includes transmission frequency, dynamic working range value, and collaborative working parameters. The effective data grouping table includes the redundancy bit allocation ratio, redundancy coding amount, and data packet integrity verification status. The path transmission efficiency distribution record specifically includes the transmission path, transmission timing distribution record, and transmission delay distribution record. The edge node collaborative processing analysis results include resource allocation requirement value, task load distribution amount, and collaborative processing relationship between edge nodes. The calculation task completion statistics record includes task execution time and edge node task completion status.

[0067] Please see Figure 2 The steps for determining the dynamic working range value are as follows:

[0068] The signal strength and network load level of edge nodes are collected, and the average signal strength and network load occupancy rate are calculated respectively. By comparing the fluctuation range of signal strength with the ratio of network load occupancy, the transmission frequency of edge nodes is generated.

[0069] The system collects signal strength and network load levels of edge nodes. Signal strength at different time points is recorded using a signal receiving device to form a time-series dataset. The trend of signal strength variation is extracted, and its average value is calculated. Network load monitoring equipment is used to obtain node load occupancy data. Load levels are subdivided according to the time series to generate a time distribution table of load levels. The correlation between signal strength fluctuations and network load occupancy ratios is analyzed. A ratio of signal strength to load level is used for comparison, and the calculation step size is gradually increased based on signal strength to fit a correlation curve between the two parameters. Finally, the transmission frequency of the edge nodes under current conditions is determined based on the extreme points of the curve.

[0070] Based on the transmission frequency of the edge nodes and their transmission coverage, the following formula is used:

[0071] ;

[0072] Signal coverage radius of computing nodes The transmission coverage of the edge nodes is obtained, where, For transmission frequency, This is the standardized value of the signal strength. This is a normalized parameter for the load level;

[0073] assumed (Unit: Hz). Signal strength normalization value ( The collected signal strength is obtained through normalization, and its calculation formula is as follows:

[0074]

[0075] Assuming signal strength range and The monitoring value is ,but:

[0076]

[0077] Load level normalization parameter ( Network load level monitoring values ​​are obtained through normalization. Assume the monitoring value is... Maximum load is The calculation yields:

[0078]

[0079] Substitute the above parameters:

[0080]

[0081] Calculate the numerator:

[0082]

[0083] Calculate the denominator:

[0084]

[0085] Calculate the score:

[0086]

[0087] Calculate the square root:

[0088]

[0089] The results show that the signal transmission coverage radius of the edge node is 5.64 units, which directly reflects the effective transmission range of the node under the current signal strength and load level. It is not only used to define the coverage area of ​​the node, but also provides initial parameters for subsequent dynamic adjustments, ensuring the adaptability and accuracy of the working range.

[0090] Based on the transmission coverage of the edge node, combined with the dynamic change rate of the coverage and the actual working area limitation of the edge node, the distribution density of the signal strength within the coverage area is adjusted to determine the boundary value of the working area and generate the dynamic working range value of the edge node.

[0091] Based on the transmission coverage range parameters of edge nodes, signal strength distribution data within the transmission coverage range is collected in conjunction with real-time environmental parameters to form a signal strength distribution density table within the coverage range. For the regional data in the distribution density table, the signal strength value of each region is refined in a gridded manner to generate a spatial model of signal strength distribution. The mean signal strength within each grid region is extracted and normalized. The rate of change of signal strength in different regions is calculated through the statistical distribution model. Combined with the actual working area constraints of the nodes, the coverage range threshold of each region is gradually adjusted according to the signal strength distribution density and the dynamic rate of change within the coverage range, thereby optimizing the signal coverage range of the edge nodes and finally generating the dynamic working range value of the nodes.

[0092] Please see Figure 3 The steps for obtaining dynamic node collaborative configuration data are as follows:

[0093] Based on the dynamic working range values ​​of edge nodes, analyze the distribution characteristics of signal strength within the coverage area, record the maximum and minimum signal strength, the average signal strength, and the intersection area of ​​the coverage area, analyze the uniformity of signal distribution within the coverage area, and generate a preliminary node coordination range.

[0094] Based on the determined dynamic working range values ​​of the edge nodes, signal coverage distribution data is collected. The signal receiving area of ​​the edge nodes is divided into equally spaced grid regions, with each grid cell recording the signal strength range. The data for each region is categorized and statistically analyzed, extracting the maximum, minimum, and average signal strength values. The signal strength distribution density within the grid is calculated. Combined with the collected coverage area data, the area value of each grid is calculated according to the geometric characteristics of the grid division, and the total coverage area and total signal strength density are generated. The uniformity of the signal strength distribution is analyzed through iterative steps, extracting characteristic values ​​of the overlapping parts within the coverage area, such as the maximum intersection range of signal strength and the area value of the overlapping signal part. Simultaneously, combined with the signal uniformity index within the coverage area, signal coverage area data for collaborative analysis is extracted, ultimately generating a preliminary node collaboration range.

[0095] Based on the initial node coordination range, and considering the node's transmission frequency and signal distribution characteristics within the coverage area, the following formula is used:

[0096] ;

[0097] Calculate the collaborative efficiency value of edge nodes Generate a record of the distribution of node collaboration efficiency, where... For the first The area covered by the collaborative operation of each node. This represents the average signal strength within the corresponding range. The total number of nodes participating in the collaboration;

[0098] Collaborative coverage area ( ): By monitoring the signal coverage area of ​​each node, the coverage area is calculated one by one using a geometric segmentation method. For example, the coverage area of ​​node 1. Node 2 is Node 3 is (Unit: square meters).

[0099] Mean signal strength ( ): Based on the signal acquisition values ​​within the coverage area, calculate the average signal strength within the coverage area of ​​each node. For example, the average signal strength of node 1. Node 2 is Node 3 is (Unit: dBm).

[0100] Total number of nodes ( ): The number of nodes participating in the computation within the collaborative coverage area, in this example. .

[0101] Substitute the above parameters:

[0102]

[0103] Right now:

[0104]

[0105] Step-by-step calculation of the molecule:

[0106]

[0107] Sum of numerators:

[0108]

[0109] Calculate the denominator:

[0110]

[0111] Final calculation of collaborative efficiency:

[0112]

[0113] The results show that the node collaboration efficiency is 68.33 (dBm·m²), representing the collaborative working capability of each node under the current coverage and signal strength conditions. Higher collaboration efficiency indicates a better match between the coverage and signal distribution of the nodes. This result guides parameter optimization for dynamic node collaboration configuration, ensuring that the collaboration relationship between signal strength and coverage reaches an optimal state, and provides important basis for subsequent dynamic adjustments.

[0114] Based on the node collaboration efficiency distribution records, analyze the collaboration matching between edge nodes, simultaneously analyze the numerical distribution of signal strength and collaboration efficiency, determine the matching characteristics of overlapping areas of signal coverage, and generate dynamic node collaboration configuration data.

[0115] Based on the collaborative efficiency distribution data and detailed records of signal strength coverage distribution, the coverage area is further divided into sub-regions. The actual signal distribution value and area of ​​each sub-region are calculated. By extracting the regional distribution data of signal strength, the signal distribution relationship and intensity changes between regions are analyzed. The regional distribution data are compared with the initial calculated value of collaborative efficiency. The signal coverage of each region is optimized by setting the proportional relationship of regional distribution. Based on the proportional relationship between coverage and signal strength, the overlapping parts of the signal within the signal coverage area are gradually adjusted to non-overlapping areas to further reduce the possibility of signal interference. Finally, the optimized signal coverage data of each region is summarized, and the signal matching value and the final collaborative coverage area are calculated in combination with the actual area area to generate dynamic node collaborative configuration data.

[0116] Please see Figure 4 The steps for calculating redundant coding quantity are as follows:

[0117] Based on the dynamic node collaborative configuration data, the basic information of node groups and data packets is extracted. According to the signal coverage range and signal strength distribution characteristics within the group, the average signal strength and the number of data packets in each group are calculated to generate a basic characteristic table of group data packets.

[0118] The system invokes dynamic node collaborative configuration data, parses the node grouping information and data packet attributes contained within the data, and divides the data into several groups based on the node's coverage area and signal strength characteristics. For each group, the numerical range of its signal strength is progressively calculated, including the maximum, minimum, and average values. The statistical signal strength data comes from real-time acquired signal values ​​of the coverage area, and the average signal strength of each group is calculated by segmenting the data by region to ensure the accuracy of the signal characteristics of each group. Subsequently, the number of data packets in each group is counted, and the number of data packets is combined with the signal strength characteristics to generate a basic characteristic table of group data packets based on the average signal strength of the group and the number of data packets. The integrity of the group data is verified through repeated sampling and calculation, ultimately ensuring the accuracy and reliability of the basic characteristic table of group data packets, providing accurate input for subsequent calculations.

[0119] Based on the basic characteristics table of packet data, combined with the average signal strength and number of data packets within a packet, the following formula is used:

[0120] ;

[0121] Calculate the first Redundancy bit allocation ratio of group data packets Generate a grouped redundancy bit allocation table, where... The average signal strength The number of data packets in the current group. The maximum number of packets in all groups;

[0122] Mean signal strength ( ): The average value of each group is calculated from the grouped signal strength data, for example, group 1. (Unit: dBm), Group 2 Group 3 .

[0123] Maximum number of packets ( ): Retrieves the maximum number of packets across all packets, for example... .

[0124] Number of packets in the current group ( ): Extract the first from the grouped basic characteristic table The total number of data packets in a group, for example, group 1. Group 2 Group 3 .

[0125] Group 1 Calculation

[0126] Input parameters:

[0127]

[0128] Calculate the numerator:

[0129]

[0130] Calculate the denominator:

[0131]

[0132] Calculate the square root:

[0133]

[0134] Group 2 calculations

[0135] Input parameters:

[0136]

[0137] Calculate the numerator:

[0138]

[0139] Calculate the denominator:

[0140]

[0141] Calculate the square root:

[0142]

[0143] Group 3 calculations

[0144] Input parameters:

[0145]

[0146] Calculate the numerator:

[0147]

[0148] Calculate the denominator:

[0149]

[0150] Calculate the square root:

[0151]

[0152] The calculation results show that the redundancy bit allocation ratio is 9.66 for group 1, 11.55 for group 2, and 8.16 for group 3. These values ​​illustrate the proportion of redundant resources allocated to each group of data packets based on the actual needs of the average signal strength and the number of data packets. A higher redundancy bit allocation ratio indicates that the group of data requires more redundancy support to ensure data transmission integrity and reliability.

[0153] Based on the packet redundancy bit allocation table, combined with the number of packets and the redundancy bit allocation ratio, the redundancy coding amount of each packet is determined, the total redundancy coding amount of each packet is calculated, and a packet redundancy coding amount distribution table is generated.

[0154] The process involves calling the packet redundancy bit allocation table, parsing the redundancy bit allocation ratio for each data group, combining the packet information with the allocation ratio, and calculating the redundancy coding amount for each data group by multiplying the number of data packets in each group by the redundancy bit allocation ratio. The redundancy coding amounts for all groups are recorded, and a group statistics table is generated. The statistical results for each group's redundancy coding amount are then summed to generate the total redundancy coding amount for each group. During the calculation of redundancy coding amount, the accuracy of each group of data needs to be checked individually to avoid allocation deviations caused by signal fluctuations or data errors. The rationality of the grouping results is verified through repeated calculations, and a packet redundancy coding amount distribution table is generated after statistical completion to ensure accurate and reliable data input for subsequent processing.

[0155] Please see Figure 5 The steps to obtain the effective data grouping table are as follows:

[0156] Based on the packet redundancy coding distribution table, redundant information is added to the corresponding data packets one by one according to the redundancy coding of each group of data packets. By merging the original content of the data packets with the redundant information, a data packet with enhanced redundancy is generated.

[0157] Based on the packet redundancy coding distribution table, the required redundant information content and bit length are determined according to the redundancy coding value of each packet. The original content of each packet is extracted sequentially, and the data structure and insertion positions of the redundant information are analyzed packet by packet. For each packet, the redundant information is added to the designated position with a predetermined number of bits. By checking the length and structural consistency of the added packet, it is ensured that the addition of redundant information does not compromise the integrity of the original data. Redundancy information is superimposed on all packet packets sequentially. During processing, the effectiveness of the redundant information is monitored in real time, and the complete packet content after addition is recorded. Finally, a packet data table containing the redundancy enhancement data is formed, providing input data for subsequent integrity verification.

[0158] Based on the data packets with enhanced redundancy, check the integrity of the data packets one by one, analyze the matching between the redundancy information in each group of data packets and the actual received data, record the integrity characteristic parameters of each group of data packets, and generate a data packet integrity characteristic table.

[0159] Based on the redundancy-enhanced packet data table, the content integrity of each packet group is checked one by one. For each packet, the redundancy-enhanced content is extracted and compared with the actual received data content, and the degree of matching between the two is analyzed. During the matching check, the difference between the redundancy-enhanced data and the received data is calculated bit-by-bit, and the number of differing bits in each packet group is counted. Simultaneously, the number and location of lost redundant information are recorded, generating a packet content integrity parameter record table. During the check, according to the established content integrity standards, key parameters such as the difference index and loss rate of each packet group are recorded to ensure that the integrity check results reflect the actual transmission and reception status of each packet group. Finally, a packet integrity characteristic table is generated, providing a direct reference for subsequent packet selection.

[0160] Based on the data packet integrity characteristic table, the data packet groups are filtered for validity. Data packets that meet the integrity standard are classified as valid groups, and data packets that do not meet the integrity standard are marked as invalid, thus generating a valid data packet table.

[0161] Based on the data packet integrity characteristic table, integrity characteristic parameters are analyzed group by group, and valid data packets are selected according to the minimum integrity standard set by the integrity verification results. For each group of data packets, integrity parameters, including content difference values ​​and loss rates, are compared item by item with the integrity standard. For packets that meet the standard, their validity status is recorded and they are classified as valid data packets; for packets that do not meet the integrity standard, their invalid status is recorded and they are marked as invalid data. During the selection process, the number of valid packets is accumulated successively to generate a final statistical record of valid data. Simultaneously, data packets marked as invalid are separated to ensure the accuracy and independence of valid data packets. Finally, a valid data packet table is formed, providing reliable data support for subsequent data analysis and transmission.

[0162] Please see Figure 6 The steps for obtaining the path transmission efficiency distribution record are as follows:

[0163] Based on the effective data grouping table, the number of target nodes, data volume, and transmission time limit for each data packet are extracted using the following formula:

[0164] ;

[0165] Calculate the first Initial priority value of group data packets By normalizing the priority values ​​of each group of data, preliminary data packet priority ranking values ​​are generated. For data volume, For transmission time limit, For the maximum transmission time limit, The target number of nodes;

[0166] Data volume ( ): Obtained by extracting the total amount of data transmitted in the packets, for example (Unit: KB)

[0167] Maximum transmission time limit ( ): Extract the maximum allowed transmission time limit from all packets, for example (Unit: milliseconds)

[0168] Transmission time limit ( ): This represents the actual transmission time limit of the current packet data, for example... (Unit: milliseconds)

[0169] Number of target nodes ( ): Reflects the target number of distribution nodes for the data packet, for example .

[0170] Calculate the numerator:

[0171]

[0172] Calculate the denominator:

[0173]

[0174] Calculate the priority ranking value:

[0175]

[0176] The results show that the initial priority ranking value of the first group of data packets is 166.67. This value indicates that this group of data packets is of high importance under the constraints of transmission time limits and the number of target nodes. The higher the priority ranking value, the higher the transmission priority it needs during scheduling. This result provides an important reference for subsequent path optimization and timing adjustment.

[0177] Based on the initial data packet priority ranking value, and combined with path capacity, path load status and data packet transmission requirements, the priority ranking is adjusted group by group. The path transmission timing distribution is optimized by adjusting the priority ranking, and the distribution order is dynamically adjusted according to the path load to generate the adjusted transmission path and transmission timing distribution record.

[0178] Based on the initial data packet priority ranking, the initial priority values ​​are adjusted one by one, taking into account the transmission path capacity, path load status, and data packet transmission requirements for each group of data packets. During the adjustment process, the current transmission path capacity for each group of data packets is first extracted, and the available path capacity is corrected according to the real-time load value of the path, matching the path capacity with the data packet transmission requirements. For each group of data packets, priority values ​​are reallocated based on the dynamic load of the transmission path, lowering the priority of packets with higher path loads and raising the priority of packets with lower path loads to balance path resource utilization efficiency. By recording the priority values ​​after each adjustment and comparing them with the original values, the rationality and effectiveness of the adjustment results are ensured. Finally, adjusted transmission path and transmission timing distribution records are generated, and the adjustment results are verified group by group to ensure that the priority ranking and timing distribution can meet the transmission requirements of each group of data packets.

[0179] Based on the adjusted transmission path and transmission timing distribution records, the transmission delay of each path is calculated, the relationship between delay and path capacity and load is analyzed, and a path transmission efficiency distribution record is generated.

[0180] Based on the adjusted transmission paths and transmission timing distribution records, the distribution records and reception time information of data packets are extracted path by path to analyze the actual transmission delay on each path. For each path, the path capacity and load status are extracted to analyze the path transmission efficiency. First, the transmission delay value of each group of data packets on that path is recorded. The ratio of the path's transmission delay to its corresponding data volume is calculated to determine the unit data transmission efficiency of the path. Subsequently, the path's load status and transmission efficiency are compared and analyzed, and the comparison results are recorded as the path's transmission efficiency distribution value. Through path-by-path statistical analysis, the transmission efficiency distribution is categorized by path, and the overall transmission efficiency of the paths is summarized and recorded to ensure that the transmission efficiency results of each path accurately reflect its performance in actual transmission. Finally, a path transmission efficiency distribution record table is generated.

[0181] Please see Figure 7 The steps for obtaining the analysis results of edge node collaborative processing are as follows:

[0182] The path transmission efficiency distribution record is called to extract the transmission efficiency value of each path and the task distribution of the corresponding edge nodes. The resource allocation requirement value of each node is calculated, and the task requirement and transmission efficiency are normalized to generate the preliminary resource allocation result of the edge nodes.

[0183] The process involves retrieving path transmission efficiency distribution records, extracting transmission efficiency values ​​and task distribution for each path, and establishing the correlation between resource utilization and transmission efficiency for each path. The task requirements of edge nodes are then matched one-to-one with the path's transmission efficiency to calculate the current task load and resource allocation requirements for each node. During the calculation, path transmission efficiency is used as the benchmark to analyze the optimal allocation of resources required by each node, and the initial resource allocation for each node is recorded. Simultaneously, the correlation between node resource allocation and task load is analyzed to ensure the feasibility of the allocation results. The initial resource allocation results for all nodes are summarized and compiled into a preliminary resource allocation result table for subsequent adjustments and verification.

[0184] Based on the preliminary resource allocation results and the task load distribution of edge nodes, the task processing content of each node is adjusted. The matching degree between the load and resource requirements of each node is analyzed, the task processing content is dynamically allocated, and the resource and task transfer between edge nodes during the task adjustment process is recorded to generate collaborative processing records between nodes.

[0185] The initial resource allocation results table is accessed to compare and analyze the task load and allocated resource requirements of each node. The matching degree between resource allocation and task processing is extracted sequentially, and adjustments are made to nodes with unbalanced task ratios. During the adjustment process, changes in task transfers are recorded node-by-node to analyze the dynamic relationship between resource allocation and task processing content. Task processing content is then reallocated based on the task distribution volume of each node. Simultaneously, resource flow and task load changes are considered during the adjustment process, recording the interaction between tasks and resource transfers between nodes to ensure that optimized resource and task allocation further supports collaborative processing relationships. Finally, a task processing adjustment record table is generated, fully reflecting the resource and task adjustment status of each node, providing support for subsequent collaborative relationship analysis.

[0186] Based on the collaborative processing records between nodes, the collaborative processing relationship between edge nodes is analyzed. By statistically analyzing the interaction volume of task allocation and processing resources between each node, the collaborative processing analysis results of edge nodes are formed.

[0187] The task processing adjustment record table is invoked to statistically analyze changes in resource allocation and task processing content node by node. By analyzing the task interaction volume and resource sharing between nodes, the collaborative processing relationships between nodes are recorded. Within these records, parameters such as the frequency of task interaction and the proportion of resource sharing between nodes are analyzed one by one. By analyzing the dynamic trends of task and resource interactions between nodes, collaborative characteristic parameters are extracted to form a dynamic description of the node collaborative relationships. Based on the records of collaborative processing relationships, the stability of task allocation and resource flow between nodes is further statistically analyzed, generating a distribution map of node collaborative processing. Finally, the analysis results of edge node collaborative processing are compiled, providing a basis for the formulation of further strategies for resource optimization and task allocation.

[0188] Please see Figure 8 The steps for obtaining statistical records upon completion of the computation task are as follows:

[0189] Based on the analysis results of edge node collaborative processing, the task distribution and collaborative processing relationship of each edge node are analyzed. Combining the start and end times of the tasks, the actual execution time of each task is calculated, and the execution time of each task is summarized to form an edge node task execution time record.

[0190] The task distribution of each edge node is extracted from the collaborative processing analysis results. The task timestamp data is called node by node. By recording the start and end times of the tasks, the actual execution time of the tasks is calculated. The start and end times of each task are compared with the current system time to correct the timestamp deviation. The calculated task durations are stored in array form. At the same time, the tasks are grouped by node, and the task execution time distribution of each group is stored in the corresponding node statistics table. Finally, by summarizing all the node statistics tables, a distribution table containing the execution time of all tasks is generated.

[0191] Based on the task execution time records of edge nodes, the total task completion time of each edge node is calculated by comparing the task execution time with the resource allocation efficiency, thus obtaining the task completion status record of the node;

[0192] The task execution time distribution table is invoked to extract the actual execution time of each task. This time is then compared with the preset task schedule time one by one. By determining whether the actual duration of each task meets the scheduled duration, the completion status of the task is marked with a Boolean variable. The results of each determination are organized by task number and node to generate a task completion mark table. Subsequently, the Boolean variables of all task completion in the mark table are statistically analyzed to calculate the total task completion amount and completion rate of each node. Finally, the statistical results of each node are merged to generate a global task completion record table.

[0193] Based on the node task completion records, combined with the task completion ratio and execution status of each edge node, the overall task completion distribution is statistically analyzed, the task completion status of each node is summarized, and a statistical record of computing task completion is formed.

[0194] The task completion record table is accessed, and the completion distribution information of tasks is extracted by node and task number. The interval values ​​of task completion time are counted one by one. By calculating the mean and variance of the time intervals, the statistical characteristics of task completion distribution are analyzed. These characteristic values ​​are matched with the relationship between collaborative tasks between nodes to summarize the characteristic patterns of task distribution. Then, the distribution patterns and time interval records of all tasks are summarized to generate a task distribution record table. Finally, based on this record table, the statistical characteristic data of task completion are calculated and saved as a task completion statistical record.

[0195] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A data acquisition and computing system based on edge computing, characterized in that, The system includes: The dynamic sensing and adjustment module collects the signal strength and network load level of the edge nodes, calculates the transmission frequency of the edge nodes, calculates the dynamic working range value of the edge nodes based on the transmission coverage, and calculates the collaborative working parameters of the edge nodes in combination with the signal coverage distribution, generating dynamic node collaborative configuration data. The edge redundancy packet grouping module performs grouping processing based on the dynamic node collaborative configuration data, calculates the redundancy bit allocation ratio of data packets, counts the amount of redundancy coding, adds redundancy information to each group of data packets according to the statistical results and performs integrity verification, and performs data packet validity grouping based on the integrity verification results to form a valid data grouping table. The priority transmission scheduling module calculates the priority ranking value of the data packets according to the effective data packet table, adjusts the transmission path and transmission timing distribution, records the transmission delay distribution according to the path selection and distribution, and obtains the path transmission efficiency distribution record. Based on the path transmission efficiency distribution record, the distributed computing resource management module calculates the resource allocation requirements of edge nodes, counts the task load distribution, adjusts the task processing content of edge nodes, records the collaborative processing relationship between edge nodes in combination with the task distribution, and forms the edge node collaborative processing analysis results. The computing task integration module calculates the task execution time and records the completion status of each edge node task based on the collaborative processing analysis results of the edge nodes, thus obtaining a statistical record of computing task completion. The steps for obtaining the dynamic node collaborative configuration data are as follows: Based on the dynamic working range value of the edge node, analyze the distribution characteristics of signal strength within the coverage area, record the maximum and minimum values ​​of signal strength, the average value of signal strength, and the intersection area of ​​the coverage area, analyze the uniformity of signal distribution within the coverage area, and generate a preliminary node collaboration range. Based on the preliminary node coordination range, and considering the node's transmission frequency and signal distribution characteristics within the coverage area, the following formula is used: ； Calculate the collaborative efficiency value of edge nodes Generate a record of the distribution of node collaboration efficiency, where... For the first The area covered by the collaborative operation of each node. This represents the average signal strength within the corresponding range. The total number of nodes participating in the collaboration; Based on the node collaboration efficiency distribution record, the collaboration matching between edge nodes is analyzed, the numerical distribution analysis of signal strength and collaboration efficiency is performed simultaneously, the matching characteristics of overlapping areas of signal coverage are determined, and dynamic node collaboration configuration data is generated.

2. The data acquisition and computing system based on edge computing according to claim 1, characterized in that, The calculation steps for the dynamic working range value are as follows: The signal strength and network load level of edge nodes are collected, and the average signal strength and network load occupancy rate are calculated respectively. By comparing the fluctuation range of signal strength with the ratio of network load occupancy, the transmission frequency of edge nodes is generated. Based on the transmission frequency of the edge nodes and their transmission coverage, the following formula is used: ； Signal coverage radius of computing nodes The transmission coverage of the edge nodes is obtained, where, For transmission frequency, This is the standardized value of the signal strength. This is a normalized parameter for the load level; Based on the transmission coverage of the edge node, combined with the dynamic change rate of the coverage and the actual working area limitation of the edge node, the distribution density of the signal strength within the coverage area is adjusted to determine the boundary value of the working area and generate the dynamic working range value of the edge node.

3. The data acquisition and computing system based on edge computing according to claim 1, characterized in that, The statistical steps for the amount of redundant coding are as follows: Based on the dynamic node collaborative configuration data, the basic information of node groups and data packets is extracted. According to the signal coverage range and signal strength distribution characteristics within the group, the average signal strength and the number of data packets in each group are calculated to generate a basic characteristic table of group data packets. Based on the aforementioned basic characteristics table of packet data, and combined with the average signal strength and number of data packets within the packet, the following formula is used: ； Calculate the first Redundancy bit allocation ratio of group data packets Generate a grouped redundancy bit allocation table, where... The average signal strength The number of data packets in the current group. The maximum number of packets in all groups; Based on the packet redundancy bit allocation table, and combined with the number of data packets and the redundancy bit allocation ratio, the redundancy coding amount of each data packet is determined group by group, and the total redundancy coding amount of each group is calculated to generate a packet redundancy coding amount distribution table.

4. The data acquisition and computing system based on edge computing according to claim 3, characterized in that, The steps for obtaining the effective data grouping table are as follows: Based on the aforementioned packet redundancy coding distribution table, redundant information is added to the corresponding data packets one by one according to the redundancy coding amount of each group of data packets. By merging the original content of the data packets with the redundant information, a data packet with enhanced redundancy is generated. Based on the data packets with enhanced redundancy, check the integrity of the data packets one by one, analyze the matching between the redundancy information in each group of data packets and the actual received data, record the integrity characteristic parameters of each group of data packets, and generate a data packet integrity characteristic table. Based on the data packet integrity characteristic table, the data packet groups are filtered for validity. Data packets that meet the integrity criteria are classified as valid groups, while data packets that do not meet the integrity criteria are marked as invalid, thus generating a valid data packet table.

5. The data acquisition and computing system based on edge computing according to claim 4, characterized in that, The steps for obtaining the path transmission efficiency distribution record are as follows: Based on the effective data grouping table, the number of target nodes, data volume, and transmission time limit for each data packet are extracted using the following formula: ； Calculate the first Initial priority value of group data packets By normalizing the priority values ​​of each group of data, preliminary data packet priority ranking values ​​are generated. For data volume, For transmission time limit, For the maximum transmission time limit, The target number of nodes; Based on the initial data packet priority ranking value, and combined with path capacity, path load status and data packet transmission requirements, priority ranking is adjusted group by group. The path transmission timing distribution is optimized by adjusting and the distribution order is dynamically adjusted according to the path load, generating the adjusted transmission path and transmission timing distribution record. Based on the adjusted transmission path and transmission timing distribution record, the transmission delay of each path is calculated, the relationship between delay and path capacity and load is analyzed, and a path transmission efficiency distribution record is generated.

6. The data acquisition and computing system based on edge computing according to claim 5, characterized in that, The steps for obtaining the edge node collaborative processing analysis results are as follows: The path transmission efficiency distribution record is called to extract the transmission efficiency value of each path and the task distribution of the corresponding edge nodes. The resource allocation requirement value of each node is calculated, and the task requirement and transmission efficiency are normalized to generate the preliminary resource allocation result of the edge nodes. Based on the preliminary resource allocation results and the task load distribution of the edge nodes, the task processing content of each node is adjusted, the matching degree between the load and resource requirements of each node is analyzed, the task processing content is dynamically allocated, and the resource and task transfer between edge nodes during the task adjustment process is recorded to generate collaborative processing records between nodes. Based on the collaborative processing records between the nodes, the collaborative processing relationship between edge nodes is analyzed. By statistically analyzing the interaction of task allocation and processing resources between each node, the collaborative processing analysis results of edge nodes are formed.

7. The data acquisition and computing system based on edge computing according to claim 6, characterized in that, The steps for obtaining the statistical records upon completion of the computation task are as follows: Based on the analysis results of edge node collaborative processing, the task distribution and collaborative processing relationship of each edge node are analyzed. Combining the start and end times of the tasks, the actual execution time of each task is calculated, and the execution time of each task is summarized to form an edge node task execution time record. Based on the task execution time records of the edge nodes, the total task completion time of each edge node is calculated by comparing the task execution time with the resource allocation efficiency, and the task completion status record of the node is obtained. Based on the task completion records of the nodes, combined with the task completion ratio and execution status of each edge node, the overall task completion distribution is statistically analyzed, the task completion status of each node is summarized, and a statistical record of task completion is formed.

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