Industrial Internet of Things data transmission method, device, equipment, storage medium and product

Abstract

The invention relates to an industrial Internet of Things data transmission method, device and equipment, a storage medium and a product. The method comprises the following steps: for a transmission path from a plurality of nodes to a target node, clustering and packaging data to be processed for a first node on the transmission path, and transmitting the packaged data to a next node of the first node; for a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node of the non-first node; after the to-be-processed data is transmitted to the target node, restoring the currently packaged data; wherein the target node is a server or a client. By adopting the method, the burden of data transmission of the industrial Internet of Things on the transmission node can be reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an industrial Internet of Things data transmission method, device, equipment, storage medium and product. Background Art

[0002] In the scenario where the Industrial Internet of Things uses an API gateway as a proxy, a large amount of data will be generated. This data has the characteristics of a wide variety of types, large data volume, high value, fast speed, and complex processing. One of the most important characteristics is that the data is highly similar. When this type of data is requested through the API gateway, it will burden the gateway's load, which will lead to difficulties in data transmission and calling. Summary of the invention

[0003] Based on this, it is necessary to provide an industrial Internet of Things data transmission method, device, computer equipment, computer-readable storage medium and computer program product that can effectively reduce the amount of data transmission without losing the amount of transmitted information in response to the above technical problems.

[0004] In a first aspect, the present application provides an industrial Internet of Things data transmission method, comprising:

[0005] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0006] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0007] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0008] In one embodiment, clustering and packaging the data to be processed includes:

[0009] Clustering the data to be processed to obtain multiple clustering results of the data to be processed;

[0010] Generate data packets of each clustering result, and integrate the data packets of each clustering result to obtain a total data packet of the data to be processed.

[0011] In one embodiment, the data packets for generating each clustering result include:

[0012] For each clustering result, determining the median of a plurality of to-be-processed data under the clustering result;

[0013] Calculate the difference between the remaining data to be processed except the median under the clustering result and the median;

[0014] A data packet of the clustering result is generated based on the difference and the median.

[0015] In one embodiment, the data packets of each clustering result are integrated to obtain the total data packet of the data to be processed, including:

[0016] The data packets of each clustering result, the class identifier of each clustering result and the node identifier of the current node are packaged into a total data packet.

[0017] In one embodiment, restoring the packaged data includes:

[0018] For each clustering result covered by the packaged data, obtaining the difference and median corresponding to the clustering result in the packaged data;

[0019] Based on the corresponding differences and medians of the clustering results, the data to be processed under the clustering results are restored.

[0020] In one embodiment, the method further comprises:

[0021] In the case where the target node is a server, if a non-target node on the transmission path receives a client request, identity verification is performed on the client request;

[0022] If the client request passes the identity authentication, forwarding the client request to the server via the transmission path;

[0023] In the event that the client request fails authentication, a failure message is returned to the client.

[0024] In a second aspect, the present application also provides an industrial Internet of Things data transmission device, including:

[0025] A first transmission module is used for clustering and packaging the data to be processed for a first node on a transmission path from a plurality of nodes to a target node, and transmitting the packaged data to a node next to the first node;

[0026] A second transmission module is used for restoring the packaged data transmitted by the previous node for a node other than the first node on the transmission path, clustering and packaging the restored data, and transmitting the packaged data to a next node other than the first node;

[0027] A restoration module is used to restore the currently packaged data after the data to be processed is transmitted to the target node; wherein the target node is a server or a client.

[0028] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0030] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0031] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0033] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0034] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0035] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0036] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0037] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0038] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0039] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0040] The above-mentioned industrial Internet of Things data transmission method, device, computer equipment, computer-readable storage medium and computer program product compress data into smaller data packets by clustering and packaging, and transmit them between nodes based on data packets, which can effectively reduce the amount of data transmission. The clustering method will not lose information and can reduce the burden on the transmission nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] FIG1 is a schematic flow chart of an industrial Internet of Things data transmission method in one embodiment;

[0043] FIG2 is a schematic diagram of an industrial Internet of Things data transmission process in one embodiment;

[0044] FIG3 is a schematic diagram of differential data processing in the prior art;

[0045] FIG4 is a schematic diagram of a process flow of identity authentication in one embodiment;

[0046] FIG5 is a structural block diagram of an industrial Internet of Things data transmission device in one embodiment;

[0047] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] API Gateway is a proxy between the client and the server. It can receive requests from the client, aggregate and process the requested data, and then forward the data to the server. Currently, API Gateway has a very wide range of applications in practice, especially in microservice structures, mobile applications, the Internet of Things or enterprise-level application scenarios. Therefore, data processing is a crucial link in API Gateway.

[0050] Please refer to Figure 2, which is a schematic diagram of the data transmission process of the Industrial Internet of Things, where ELB refers to Elastic Load Balance. Consider such a scenario: in the scenario where the Industrial Internet of Things uses the API gateway as a proxy, for example, in my country, mine safety has always been a research focus that has received widespread attention. In order to avoid safety accidents, the monitoring installed in the mine will continuously monitor the environment 24 hours a day, which will generate a large amount of data, such as temperature, wind speed, gas concentration, etc. They have the main characteristics of big data: a wide variety of types, large data volume, high value, fast speed, and complex processing process. The most important feature is that the data similarity is extremely high, such as the temperature is the same over a period of time. When this type of data is requested through the API gateway, it will burden the gateway's load, so the data needs to be compressed. In addition to processing large amounts of data, data security is also crucial, especially in some applications involving the transmission of sensitive or even confidential data, so data security verification, identity authentication, access control and other strategies in the API gateway are also essential.

[0051] Therefore, the present application proposes a method that can effectively reduce the amount of data transmission without losing any amount of information transmitted, while also ensuring the secure transmission of data.

[0052] In one embodiment, as shown in FIG1 , an industrial Internet of Things data transmission method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0053] Step 101, for a transmission path from multiple nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a next node of the first node;

[0054] Step 102, for a non-first node on the transmission path, restore the packaged data transmitted by the previous node, cluster and package the restored data, and transmit the packaged data to a next node that is not the first node;

[0055] Step 103, after the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0056] For example, this embodiment can be applied to a scenario where a user initiates a request to a server, that is, the target node is a server; it can also be applied to a scenario where a server returns data to a user, that is, the target node is a client. In the above two scenarios, the data processing method is the same, but the data flow direction is different.

[0057] In this embodiment, the node on the transmission path can be an API Gateway or a load balancer (SLB), and the specific type is not limited here. API Gateway is a key basic component in the full life cycle management of API, responsible for API configuration, release, version rollback, security, load balancing, etc. API Gateway is the entrance of all terminal traffic, responsible for routing terminal API requests to the correct upstream service for processing, and then returning the returned data to the original requester.

[0058] The traditional differential data processing can be seen in FIG3, which shows a differential data processing process. For example, the differential data processing shown in FIG3 adopts the LEACH protocol (Low Energy Adaptive Clustering Hierarchy). The basic idea of ​​the LEACH protocol is to randomly select cluster head nodes in a cyclic manner and evenly distribute the energy load of the entire network to each sensor node, thereby achieving the purpose of reducing network energy consumption and increasing the overall network survival time.

[0059] Differential Data Processing is a data processing method that involves calculating the differences or changes between adjacent data points in a data set. This method is often used in fields such as time series data, monitoring data, and sensor data to better understand trends, changes, and patterns in the data. This embodiment combines differential data processing with API gateways, and designs a new reference value-based packaged data algorithm. For ease of description, the algorithm is named Differential Data Processing with Median algorithm based on API Gateway (DDPM-API) algorithm, which can be used for data request and processing in the oil, gas, and coal mining industries.

[0060] In the DDPM-API algorithm, considering that it will pass through multiple transmission nodes before accessing the API gateway, it is assumed that each transmission node can perform relevant processing on the received data, rather than passing all of it to the gateway for processing. Based on this, the DDPM-API algorithm proposed in this application adopts a multi-benchmark reference value optimization method: first, the node that receives the data (such as SLB or Gateway) restores the data back to the original data, and then clusters all the data using a clustering algorithm to obtain multiple classes with high cohesion, and then packages the clustering results of each cluster based on the reference value, and finally transmits them. In this way, even if there are a lot of data with large differences in industrial networks such as coal mining, after being processed by the DDPM-API algorithm, the highly correlated data can be clustered together, thereby effectively reducing the amount of data that the nodes need to transmit.

[0061] It should be noted that each node of this application needs to cluster and restore the data, which is more suitable for situations where there are continuous and concurrent requests within a period of time, or one request corresponds to a large amount of data. In practical applications, the calculation of the node can be reduced according to the actual situation. For example, if there is only one request within a period of time, the intermediate node does not need to perform the restoration and clustering process, but directly passes the clustered and packaged data in sequence until it reaches the target node.

[0062] In the above-mentioned industrial Internet of Things data transmission method, data is compressed into smaller data packets through clustering and packaging, and transmitted between nodes based on data packets, which can effectively reduce the amount of data transmission. The clustering method will not lose information and can reduce the burden on the transmission nodes.

[0063] In an exemplary embodiment, clustering and packaging the data to be processed includes:

[0064] The data to be processed are clustered to obtain a plurality of clustering results of the data to be processed; a data packet of each clustering result is generated, and the data packets of each clustering result are integrated to obtain a total data packet of the data to be processed.

[0065] Specifically, the mean shift clustering algorithm can be used to cluster the data. Mean Shift Clustering is an algorithm based on a sliding window that aims to discover dense areas of data points. This algorithm does not require the number of clusters to be specified in advance. It automatically discovers the number of clusters through an iterative process, which is the main difference between it and algorithms such as K-means. The core idea of ​​the mean shift algorithm is to continuously update the position of data points in an iterative process so that they move to areas with higher density until the convergence condition is met. After obtaining k clusters through the clustering algorithm, a data packet for each clustering result can be generated, and the data packets of all clustering results can be integrated into the total data packet of the data to be processed.

[0066] In an exemplary embodiment, the generating of the data packets of each clustering result includes:

[0067] For each clustering result, determine the median of multiple data to be processed under the clustering result; calculate the difference between the remaining data to be processed under the clustering result except the median and the median; and generate a data packet of the clustering result based on the difference and the median.

[0068] Specifically, this step is suitable for differential processing of data, and the median of each clustering result is used as the benchmark reference value for differential processing. For example, the median can be found based on the idea of ​​quick sorting, and after finding the median, the difference between the other numbers in the clustering result except the median and the median is calculated. Each difference obtained is expressed in binary, and the total number of binary digits of each class is calculated, and then the total number of binary digits of the node including k classes is calculated, that is, the total data packet size.

[0069] Using the median as a benchmark reference value can make the difference distribution more uniform. Furthermore, in the same clustering result, the difference between other numbers except the median and the median is small, and the number of bits corresponding to the difference represented by binary is also small, so the size of the data packet can be reduced.

[0070] In an exemplary embodiment, the data packets of each clustering result are integrated to obtain the total data packet of the data to be processed, including: packaging the data packets of each clustering result, the class identifier (class ID) of each clustering result and the node identifier (node ​​ID) of the current node into a total data packet.

[0071] Compared with the original data and traditional differential data, the data packet size of the transmitted data has been greatly reduced. For details, please refer to the following table:

[0072] Table 1:

[0073]

[0074] Exemplarily, this application provides a set of data, Table 1 shows the request data packet size without data processing, which is 391 bits;

[0075] Table 2:

[0076]

[0077] Table 2 shows the request data packet size using traditional differential data processing. The value corresponding to each cluster ID in Table 2, the first data at the beginning is the cluster head node data, and the remaining data is the difference between the member node data and the cluster head data. At this time, the total data packet size is 371 bits, which is lower than without processing.

[0078] Table 3:

[0079]

[0080] Table 3 shows the data packet size of the data processed by the DDPM-API algorithm provided by this application. For ease of expression, different types of data are separated by semicolons, and the median is the value in the middle of the queue in a class. Specifically, 126 and 372 are the medians for the values ​​of cluster ID 1; 5, 126, 247, 377, and 498 are the medians for the values ​​of cluster ID 2; 0, 79, 125, 255, 372, and 513 are the medians for the values ​​of cluster ID 3; -260, 0, 33, 125, 255, 377, 513, and 876 are the medians for the values ​​of cluster ID 4; -260, 0, 31, 63, 125, 255, 377, 513, and 876 are the medians for the values ​​of cluster ID 5; 68 is the median for the values ​​of cluster ID 6; and 2 is the median for the values ​​of cluster ID 7. The data packet size of the data processed by the DDPM-API algorithm in Table 3 is 234 bits, which is a significant improvement compared to the original data and traditional differential data processing. And because this method does not actually change the data itself, but changes the way the data is represented, it can reduce the amount of data transmission without information loss.

[0081] The DDPM-API algorithm proposed in this application selects the median of each cluster as the reference value. Compared with the traditional differential data processing, which roughly selects the first data value as the reference value, so that the difference may exceed the data itself, the selection of the median can make the difference distribution more uniform, and the degree of data packet reduction is further improved.

[0082] In an exemplary embodiment, restoring the packaged data includes:

[0083] For each clustering result covered by the packaged data, obtain the corresponding difference and median of the clustering result in the packaged data; based on the corresponding difference and median of the clustering result, restore the data to be processed under the clustering result.

[0084] By summing the median and the difference, we can directly calculate the corresponding data in the original data, and restore the original data without loss. After all the requests or data of the current batch arrive, we can proceed to the next round of clustering, packaging and transmission. If the current node receives multiple groups of data in batches, the restored data needs to be re-clustered to ensure that the size of the data packet is small during each round of transmission.

[0085] Exemplarily, the above-mentioned waiting for all requests or data of the current batch to arrive refers to the situation where there are continuous and concurrent requests within a period of time, or one request corresponds to a large amount of data. In practical applications, for example, if there is only one request within a period of time, there is no need to wait or only a short wait time. Furthermore, the calculation of the node can be appropriately reduced depending on the amount of data requested and called. For example, when the data and requests to be transmitted are small, the intermediate node does not need to perform the reduction and clustering process, but directly passes the clustered and packaged data in sequence until it reaches the target node.

[0086] In an exemplary embodiment, the method further comprises:

[0087] In the case where the target node is a server, if a non-target node on the transmission path receives a client request, the client request is authenticated; if the client request passes the authentication, the client request is forwarded to the server via the transmission path; if the client request fails the authentication, a failure message is returned to the client.

[0088] For example, please refer to Figure 4. This embodiment uses an external authentication service to implement data security verification. When the API gateway receives a client request, it forwards the request to the external authentication service and decides whether to allow the request to pass based on the response of the service. This mechanism allows the authentication logic to be independent of the API gateway, thereby achieving a more flexible and scalable security policy. Users can configure the third-party authentication service address, forwarding request header, response content, request method, etc. on the API, and can support custom response content when authentication fails.

[0089] For example, an external authentication service can use the forward-auth external authentication routing strategy. Forward Auth is a mechanism that delegates authentication and authorization functions to external services. The workflow of forward-auth is:

[0090] Request interception: When the client request reaches the APISIX gateway, Forward Auth will first intercept it.

[0091] Forward request: forwards the intercepted request information to the configured external authentication service.

[0092] Authentication response: The external service processes the request and returns a response, typically an HTTP response containing the authentication result.

[0093] Decision: Based on the response from the external service, Forward Auth decides whether to allow the original request to pass. If the verification passes, the request will be forwarded to the backend service via the transmission path; if the verification fails, the corresponding error response will be returned to the client.

[0094] To explain the industrial IoT data transmission method in detail, the following is a most detailed embodiment:

[0095] The main idea of ​​the DDPM-API algorithm proposed in this application is to reduce the size of the data packet requesting the server by clustering and selecting the median differential data transmission. The DDPM-API method mainly has the following steps:

[0096] Cluster the requested data using a clustering algorithm for each SLB or Gateway. After clustering, select the median of each class as the benchmark value and calculate the difference between other values ​​in the class and the median.

[0097] The processed data is passed to the next node, and then the next node restores the data to the original data after receiving the data packet.

[0098] After waiting for a while, all requests arrive at the node (including requests within a period of time, or a large amount of data corresponding to a request), the clustering process is performed again, and then sent to the next node until the data is sent to the server / client. The following shows the detailed implementation process of each step.

[0099] The mean shift clustering algorithm is used to cluster the data. The pseudo code of the algorithm is as follows:

[0100] 1:BEGIN

[0101] 2: shift = 0

[0102] 3:FOR each data DO

[0103] 4:FLAG[ ] = false

[0104] 5:S(x)=

[0105] 6:END FOR

[0106] 7: Randomly select a data as the center point

[0107] 8: WHILE data Its FLAG[ ] = false DO

[0108] 9: WHILE shift DO

[0109] 10:FOR Each data DO

[0110] 11:

[0111] 12:

[0112] 13:FLAG[j]=true

[0113] 14: END FOR

[0114] 15:FOR each data DO

[0115] 16:

[0116] 17:END FOR

[0117] 18:center=center+shift

[0118] 19:WHEN There are multiple sliding windows overlapping DO

[0119] 20: Keep the window containing the most data points

[0120] 21:END WHEN

[0121] 22:END WHILE

[0122] 23:END WHILE

[0123] 24:END / / End.

[0124] After using the clustering algorithm in each node to obtain k clusters, select each cluster C i The median M i And calculate the difference between other data and the median. Use the quick sort algorithm:

[0125] Algorithm 2: Use the quick sort idea to select the median:

[0126] 1:BEGIN

[0127] 2: Partition(A[low...high]): / / Separation function, the data on the left side of the array is not greater than the pivot, and the data on the right side is not less than the pivot

[0128] 3: l = low, h = high

[0129] 4:pivot=A[low]

[0130] 5:REPEAT

[0131] 6:REPEAT l=l+1 UNTIL A[l] pivot

[0132] 7:REPEAT h=h-1 UNTIL A[h] pivot

[0133] 8:UNTIL

[0134] 9:swap(A[l],A[h])

[0135] 10:swap(A[low],A[h])

[0136] 11:RETURN h

[0137] 12:FindMedian(A[low...high]): / / Divide and conquer method to find the median

[0138] 13:m=Partition(A[low...high])

[0139] 14:IF m== THEN

[0140] 15:RETURN A[ ]

[0141] 16:END IF

[0142] 17:IF m> THEN

[0143] 18:Partition(A,low,m-1)

[0144] 19:ELSE Partition(A,m+1,high)

[0145] 20:END IF

[0146] 21:END / / End.

[0147] According to the above algorithm, find class C i The median M i Then, calculate the other numbers C in the class i j The difference between the median and the median. Each difference is expressed in binary, and the total number of binary digits of the node under the number of classifications is calculated, that is, the total data packet size S i .

[0148] Algorithm 4: Processing data in a node:

[0149] 1:BEGIN

[0150] 2:Sum=0

[0151] 3: Use the mean shift clustering algorithm in the node to get k clusters

[0152] 4:FOR each class DO

[0153] 5: = FindMedian(A[low...high])

[0154] 6: FOR Class Each data in ( ) DO

[0155] 7:

[0156] 8:END FOR

[0157] 9: Computing Total packet size

[0158] 10:Sum=Sum+

[0159] 11:END FOR

[0160] 12:END / / End.

[0161] Finally, after a period of request data arrives at the node, it is restored to the original data before the next round of clustering, packaging and transmission.

[0162] Algorithm 4: Restore data to original data:

[0163] 1:BEGIN

[0164] 2:FOR in each node of API Gateway transmission DO

[0165] 3:WHEN Collected the data packets sent by the previous API request DO

[0166] 4: For each class in the FOR node DO

[0167] 5: FOR Class Each data in ( ) DO

[0168] 6:

[0169] 7:END FOR

[0170] 8:END FOR

[0171] 9:END WHEN

[0172] 10:END FOR

[0173] 11:END / / End.

[0174] Therefore, the overall DDPM-API algorithm overview can be expressed as:

[0175] Algorithm 5: DDPM-API algorithm:

[0176] 1:BEGIN

[0177] 2: WHILE Node Not the target node DO / / The target node is the server or client

[0178] 3: FOR each node transmitted in the API gateway DO

[0179] 4: Process the data collected from the previous node and restore it to the original data

[0180] 5: Processing Node Data within

[0181] 6: Pack the median and difference with the node ID and class ID and send them to the next node NN

[0182] 7: =NN

[0183] 8:END FOR

[0184] 9:END WHILE

[0185] 10:END / / End.

[0186] As mentioned above, the DDPM-API algorithm uses a multi-reference value optimization method. First, the SLB or Gateway that receives the data restores the data back to the original data, and then clusters all the data using a clustering algorithm to obtain multiple classes with high cohesion. The median of each cluster is then selected as the reference value for differential processing, and finally differential data processing technology is used for transmission. In this way, even if there are a lot of data with large differences in industrial networks such as coal mining, after being processed by the DDPM-API algorithm, the highly correlated data can be clustered together, thereby effectively reducing the amount of data that the nodes need to transmit.

[0187] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0188] Based on the same inventive concept, the embodiment of the present application also provides an industrial Internet of Things data transmission device for implementing the industrial Internet of Things data transmission method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more industrial Internet of Things data transmission device embodiments provided below can refer to the limitations of the industrial Internet of Things data transmission method above, and will not be repeated here.

[0189] In an exemplary embodiment, as shown in FIG5 , an industrial Internet of Things data transmission device is provided, including: a first transmission module 501, a second transmission module 502 and a restoration module 503, wherein:

[0190] The first transmission module 501 is used for clustering and packaging the data to be processed for the first node on the transmission path from the plurality of nodes to the target node, and transmitting the packaged data to the next node of the first node;

[0191] The second transmission module 502 is used for restoring the packaged data transmitted by the previous node for the non-first node on the transmission path, clustering and packaging the restored data, and transmitting the packaged data to the next node which is not the first node;

[0192] The restoration module 503 is used to restore the currently packaged data after the data to be processed is transmitted to the target node; wherein the target node is a server or a client.

[0193] The first transmission module 501 is further used for:

[0194] Clustering the data to be processed to obtain multiple clustering results of the data to be processed;

[0195] Generate data packets of each clustering result, and integrate the data packets of each clustering result to obtain a total data packet of the data to be processed.

[0196] The first transmission module 501 is further used for:

[0197] For each clustering result, determining the median of a plurality of to-be-processed data under the clustering result;

[0198] Calculate the difference between the remaining data to be processed except the median under the clustering result and the median;

[0199] Based on the difference and the median, a data packet of the clustering result is generated.

[0200] The first transmission module 501 is further used for:

[0201] The data packets of each clustering result, the class identifier of each clustering result and the node identifier of the current node are packaged into a total data packet.

[0202] The second transmission module 502 is further configured to:

[0203] For each clustering result covered by the packaged data, obtaining the difference and median corresponding to the clustering result in the packaged data;

[0204] Based on the corresponding differences and medians of the clustering results, the data to be processed under the clustering results are restored.

[0205] The industrial Internet of Things data transmission device also includes:

[0206] Authentication module for:

[0207] In the case where the target node is a server, if a non-target node on the transmission path receives a client request, identity verification is performed on the client request;

[0208] If the client request passes the identity authentication, forwarding the client request to the server via the transmission path;

[0209] In the event that the client request fails authentication, a failure message is returned to the client.

[0210] Each module in the above industrial Internet of Things data transmission device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0211] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, an industrial Internet of Things data transmission method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0212] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0213] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0214] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0215] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0216] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0217] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0218] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0219] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0220] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0221] For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node;

[0222] For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node;

[0223] After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

[0224] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0225] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0226] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0227] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An industrial Internet of Things data transmission method, characterized in that: The method comprises: For a transmission path from a plurality of nodes to a target node, for a first node on the transmission path, clustering and packaging the data to be processed, and transmitting the packaged data to a node next to the first node; For a non-first node on the transmission path, restoring the packaged data transmitted by the previous node, clustering and packaging the restored data, and transmitting the packaged data to a next node that is not the first node; After the data to be processed is transmitted to the target node, the currently packaged data is restored; wherein the target node is a server or a client.

2. The method according to claim 1, characterized in that The clustering and packaging of the data to be processed includes: Clustering the data to be processed to obtain multiple clustering results of the data to be processed; Generate data packets of each clustering result, and integrate the data packets of each clustering result to obtain a total data packet of the data to be processed.

3. The method according to claim 2, characterized in that The data packets for generating each clustering result include: For each clustering result, determining the median of a plurality of to-be-processed data under the clustering result; Calculate the difference between the remaining data to be processed except the median under the clustering result and the median; A data packet of the clustering result is generated based on the difference and the median.

4. The method according to claim 3, characterized in that The data packets of each clustering result are integrated to obtain the total data packet of the data to be processed, including: The data packets of each clustering result, the class identifier of each clustering result and the node identifier of the current node are packaged into a total data packet.

5. The method according to claim 3, characterized in that: Restoring the packaged data includes: For each clustering result covered by the packaged data, obtaining the difference and median corresponding to the clustering result in the packaged data; Based on the corresponding differences and medians of the clustering results, the data to be processed under the clustering results are restored.

6. The method according to claim 1, characterized in that The method further comprises: In the case where the target node is a server, if a non-target node on the transmission path receives a client request, identity verification is performed on the client request; If the client request passes the identity authentication, forwarding the client request to the server via the transmission path; In the event that the client request fails authentication, a failure message is returned to the client.

7. An industrial Internet of Things data transmission device, characterized in that: The device comprises: A first transmission module is used for clustering and packaging the data to be processed for a first node on a transmission path from a plurality of nodes to a target node, and transmitting the packaged data to a node next to the first node; A second transmission module is used for restoring the packaged data transmitted by the previous node for a node other than the first node on the transmission path, clustering and packaging the restored data, and transmitting the packaged data to a next node other than the first node; A restoration module is used to restore the currently packaged data after the data to be processed is transmitted to the target node; wherein the target node is a server or a client.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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