Data planning system based on multi-node distributed real-time topological algorithm

Abstract

The invention discloses a data planning system based on a multi-node distributed real-time topological algorithm, which relates to the technical field of data processing and comprises a node management module, a data distribution module, a real-time topological algorithm module, a data processing module and a result summarization module. The node management module is used for managing registration, state monitoring and fault detection and recovery of all nodes; the data distribution module is used for distributing the data to the corresponding processing nodes according to the optimal distribution strategy provided by the real-time topological algorithm module; the real-time topological algorithm module is used for calculating and maintaining an optimal processing path of data and dynamically adjusting a data flow direction according to data characteristics and node capability; the data processing module is used for executing a data processing task; and the result summarizing module is used for acquiring and summarizing the processing results of the nodes. According to the invention, a time-staggered topology updating mechanism is provided, so that resource conflicts caused by simultaneous updating are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, in particular to a data planning system based on a multi-node distributed real-time topology algorithm. Background Art

[0002] In the current era of big data and cloud computing, the efficiency and low latency of data transmission systems have become crucial factors in various application scenarios, especially in systems that need to process large amounts of sensor data, real-time information flows, and multiple concurrent tasks. How to optimize data transmission and routing decisions has become a core issue for improving the overall performance of the system. Traditional data planning systems usually adopt a centralized architecture, relying on one or several central nodes to make routing and scheduling decisions for all data. This architecture can ensure the consistency of the system and the centralization of management in the early stages, but as the scale of the system increases, the computing and scheduling pressure of a single central node gradually increases, and the efficiency and stability of the system are seriously affected. In order to address this problem, in recent years, researchers have proposed some new distributed system architectures, such as drone path planning systems based on neural networks and deep learning. These systems optimize the task allocation and path planning of drones through the collaboration of the main update node and multiple sub-computing nodes, thereby improving the flexibility and robustness of the system.

[0003] However, the existing data planning system still has limitations. Especially when the number of nodes continues to increase, the scheduling pressure of the central controller will increase rapidly, leading to a bottleneck in the system's performance. Although the existing technology optimizes data transmission and routing decisions through the collaborative work of the main update node and multiple sub-computing nodes, as the number of sub-nodes increases, the gradient update information and network parameters that the central node needs to process will grow exponentially, causing bottleneck problems in computing and communication. Specifically, the central control node needs to frequently receive the gradient information of each sub-node and update the network parameters, which may cause network communication delays in large-scale nodes and complex task scenarios, thereby affecting data planning efficiency and system stability. In addition, the connection relationship and data transmission path between distributed computing nodes may be constantly adjusted as the system changes dynamically. This real-time optimization may cause data transmission delays and unbalanced resource allocation in some cases, thereby affecting the overall performance of the system. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a data planning system based on a multi-node distributed real-time topology algorithm to solve the problem in the prior art that when the number of nodes increases, the scheduling pressure of the central controller increases, which may lead to a decline in system performance and affect the performance and stability of the system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a data planning system based on a multi-node distributed real-time topology algorithm, which includes a node management module, a data distribution module, a real-time topology algorithm module, a data processing module and a result summary module; the node management module is used to manage the registration, status monitoring, fault detection and recovery of all nodes; the data distribution module is used to distribute data to corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module; the real-time topology algorithm module is used to calculate and maintain the optimal processing path of the data, and dynamically adjust the data flow direction according to the data characteristics and node capabilities; the data processing module is used to perform data processing tasks; the result summary module is used to obtain the processing results of each node and summarize them.

[0008] As a preferred solution of the data planning system based on a multi-node distributed real-time topology algorithm described in the present invention, the optimal distribution strategy refers to dynamically distributing data on different nodes based on the access frequency, size, processing capability and storage capacity of the nodes of the data.

[0009] As a preferred solution of the data planning system based on the multi-node distributed real-time topology algorithm of the present invention, wherein: the data is distributed to the corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module, and the data is distributed to the corresponding processing nodes based on the processing capacity and current load of the nodes, including the following steps: based on the processing capacity and current load of the nodes, the weight of each node is calculated, and the specific formula is as follows:

[0010] W i =f(C i ,L i )

[0011]

[0012] Among them, W i is the weight of each node; f is a linear function; a and b are weight coefficients; C i is the processing capacity of node i; L i is the current load of node i; according to the weight W of each node i Allocate data to each node, and the data allocation ratio is as follows:

[0013]

[0014] Among them, D i is the amount of data allocated to node i; D totalis the total amount of data to be allocated; n is the total number of nodes; the weight is adjusted dynamically based on the system operation status. The specific formula for dynamic adjustment is as follows:

[0015] W i (t+1)=W i (t)+ΔW i

[0016] ΔW i =g(P i ,L i ,T)

[0017] Where, ΔW i is the weight adjustment; g is the adjustment function; P i is the real-time performance index of node i; T is time; the specific formula of the adjustment rule is as follows:

[0018] ΔW i = k·(P target -P i )-λ·(L i -L avg )

[0019] Among them, k and λ are adjustment coefficients; P target is the target performance indicator; L avg is the average load.

[0020] As a preferred solution of the data planning system based on the multi-node distributed real-time topology algorithm of the present invention, wherein: for the instantaneous increase of system load caused by the same time update, the method of the real-time topology algorithm module includes the following steps: decentralized planning of the update time of all nodes so that only some nodes perform topology update at any time, the specific formula is as follows:

[0021]

[0022] Among them, T update is the update cycle; N is the total number of nodes; t i Assign an update time point to each node i; dynamically adjust the update time of each node based on the importance of the node and the load conditions. The specific formula is as follows:

[0023] α i =h(I i ,L i )

[0024]

[0025] Among them, α i is the adjustment factor; h is the function; I i is the importance of node i; Li is the load condition of node i; ∈ is a small positive number; the specific formula of the adjusted update time is as follows:

[0026] t i =t i +α i ·Δt

[0027] Among them, t' i is the adjusted update time; Δt is the time adjustment step; through the inter-node communication protocol, the topology information during the update period is correctly transmitted to avoid communication errors caused by staggered update time. The specific formula for defining the communication protocol P is as follows:

[0028] R confirm (i) = (t′ i ,status)

[0029] S j (t′ i )=P(R confirm (i),S j (t))

[0030] Among them, R confirm (i) Node i sends a confirmation request before updating; S j (t' i ) is the node j at time t' i The synchronization state of S j (t) is the state of node j at time t.

[0031] As a preferred solution of the data planning system based on a multi-node distributed real-time topology algorithm described in the present invention, the inter-node communication protocol adopts a link encryption method, and encryption devices are installed at the nodes at both ends of the link. The encryption devices encrypt the transmitted data and decrypt it at the receiving end.

[0032] As a preferred solution of the data planning system based on a multi-node distributed real-time topology algorithm described in the present invention, wherein: for synchronous updates that cause peak loads on network bandwidth and computing resources, the method of the real-time topology algorithm module includes the following steps: dividing the entire network into multiple subnets, each subnet containing a certain number of nodes; independently performing topology updates within each subnet, and the update operation is limited to nodes within the subnet; topology information updates between subnets are performed asynchronously to avoid synchronous updates of the entire network; and designing a communication protocol between subnets to make the topology information between subnets consistent.

[0033] As a preferred solution of the data planning system based on multi-node distributed real-time topology algorithm described in the present invention, wherein: the inter-subnet communication protocol adopts symmetric encryption, and the same key is used for encryption and decryption.

[0034] On the second aspect, in order to further solve the security problems existing in data processing, the present invention provides a data planning method based on a multi-node distributed real-time topology algorithm, which includes: using a node management module to register and monitor the initial status of all nodes; when data is input into the system, the data distribution module distributes the data to the corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module; using the data processing module of the target node to process the distributed data; sending the processing results to the result summary module, and the result summary module summarizes all the results; using the node management module to monitor the system status, if a fault or load abnormality is found, adjust the topology structure or restart the task of the faulty node.

[0035] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the data planning system based on a multi-node distributed real-time topology algorithm as described in the first aspect of the present invention is implemented.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the data planning system based on a multi-node distributed real-time topology algorithm as described in the first aspect of the present invention is implemented.

[0037] Beneficial effects of the present invention: The present invention provides a time-staggered topology update mechanism, where different nodes are updated at different times, thereby reducing resource conflicts caused by simultaneous updates. In addition, by dividing the nodes into multiple subnets, each subnet independently performs topology updates, thereby reducing synchronization overhead across the entire network, thereby improving system scalability while maintaining system real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0039] Figure 1 This is a system block diagram of the data planning system of the present invention in Example 1.

[0040] Figure 2 This is a flow chart of the data planning system based on the multi-node distributed real-time topology algorithm of the present invention in Example 1.

[0041] Figure 3This is a flowchart of the time stagger of the multi-node distributed real-time topology algorithm of the present invention in Example 1.

[0042] Figure 4 This is a flowchart of the multi-node distributed real-time topology node division of the present invention in Example 1.

[0043] Figure 5 This is a schematic diagram of the structure of the computer device in Example 3. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1

[0048] Reference Figure 1 to Figure 4 , which is the first embodiment of the present invention, provides a data planning system based on a multi-node distributed real-time topology algorithm, including:

[0049] Node management module, data distribution module, real-time topology algorithm module, data processing module and result summary module, among which Figure 1 Shown is a system block diagram of the data planning system of the present invention.

[0050] It should be noted that, in the system initialization stage of the present invention, the node management module is first started to complete the registration and initial status monitoring of all nodes; when data is input into the system, the data distribution module distributes the data to the corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module; the distributed data is processed by the data processing module of the target node; after the processing is completed, the result is sent to the result summary module, and the result summary module summarizes all the results; the node management module monitors the system status, and if a fault or load abnormality is found, the topology structure is adjusted or the task of the faulty node is restarted to ensure the stable operation of the system.

[0051] Preferably, the node management module is used to manage the registration, status monitoring, and fault detection and recovery of all nodes, and to achieve real-time updating of node information by monitoring the node status.

[0052] Preferably, Figure 2 The figure shows a flow chart of the data planning system based on a multi-node distributed real-time topology algorithm of the present invention. The data distribution module is used to distribute data to corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module, taking into account the processing capacity and current load conditions of the nodes to achieve load balancing.

[0053] Specifically, the optimal distribution strategy refers to dynamically distributing data on different nodes based on the access frequency, size, processing power and storage capacity of the data, so as to ensure efficient access and processing of the data.

[0054] Specifically, distributing data to corresponding processing nodes means distributing data to corresponding processing nodes based on the processing capacity and current load of the nodes according to the optimal distribution strategy provided by the real-time topology algorithm module, including the following steps: based on the processing capacity and current load of the nodes, calculating the weight of each node, the specific formula is as follows:

[0055] W i =f(C i ,L i )

[0056]

[0057] Among them, W i is the weight of each node, where the weight of each node is defined as W i is a comprehensive index; f is a linear function; a and b are weight coefficients; C i is the processing capacity of node i; L i is the current load of node i.

[0058] According to the weight W of each node i Allocate data to each node to achieve reasonable distribution of data among multiple nodes. The data allocation ratio is as follows:

[0059]

[0060] Among them, D i is the amount of data allocated to node i; D total is the total amount of data to be allocated; n is the total number of nodes.

[0061] Combined with the system operation situation, the weight is adjusted dynamically to ensure the stable operation of the system. The specific formula for dynamic adjustment is as follows:

[0062] W i(t+1)=W i (t)+ΔW i

[0063] ΔW i =g(P i ,L i ,T)

[0064] Where, ΔW i is the weight adjustment amount, which is calculated according to the real-time performance and load changes of the node; g is the adjustment function; P i is the real-time performance index of node i; T is time.

[0065] The specific formula for adjusting the rules is as follows:

[0066] ΔW i = k·(P target -P i )-λ·(L i -L avg )

[0067] Among them, k and λ are adjustment coefficients; P target is the target performance indicator; L avg is the average load.

[0068] Preferably, the present invention dynamically adjusts the distribution strategy by comprehensively considering the node processing capabilities and load conditions, effectively avoiding resource waste and ensuring maximum data processing efficiency. Compared with traditional data distribution, which is difficult to balance load balancing and efficient processing, the present invention achieves efficient data distribution through weight calculation and dynamic adjustment.

[0069] Preferably, Figure 3 The figure shows a time-staggered flowchart of a multi-node distributed real-time topology algorithm of the present invention. The real-time topology algorithm module is used to calculate and maintain the optimal processing path of data and dynamically adjust the data flow according to data characteristics and node capabilities.

[0070] Specifically, Figure 4 The flowchart of the multi-node distributed real-time topology node division of the present invention is shown. For the instantaneous increase of system load caused by the same time update, the method of the real-time topology algorithm module includes the following steps: the update time of all nodes is decentralized and planned so that only some nodes perform topology update at any time. The specific formula is as follows:

[0071]

[0072] Among them, T update is the update cycle; N is the total number of nodes; t i Assign an update time point to each node i to achieve decentralized update.

[0073] According to the importance of the node and the load conditions, the update time of each node is dynamically adjusted to achieve the most optimized update processing. The specific formula is as follows:

[0074] α i =h(I i ,L i )

[0075]

[0076] Among them, α i is the adjustment factor; h is the function; I i is the importance of node i; L i is the load condition of node i; ∈ is a small positive number.

[0077] The specific formula for the adjusted update time is as follows:

[0078] t i =t i +α i ·Δt

[0079] Among them, t' i is the updated time after adjustment; Δt is the step size of time adjustment, which is used to control the amplitude of adjustment.

[0080] Through the inter-node communication protocol, the topology information during the update period is correctly transmitted to avoid communication errors caused by staggered update times. The specific formula for defining the communication protocol P is as follows:

[0081] R confirm (i) = (t′ i ,status)

[0082] S j (t′ i )=P(R confirm (i),S j (t))

[0083] Among them, R confirm (i) Node i sends a confirmation request before updating; S j (t' i ) is the node j at time t' i The synchronization state indicates that other nodes j receive the confirmation request R confirm 9i), synchronize its status according to the communication protocol P; S j (t) is the state of node j at time t.

[0084] Preferably, independent updates within a subnet reduce the synchronization overhead of the entire network and improve system scalability. Asynchronous updates reduce communication delays and resource usage. Compared with the high cost and long latency of synchronous updates of the entire network, the present invention optimizes the update mode by introducing subnet division.

[0085] Furthermore, the inter-node communication protocol adopts a link encryption method. By installing encryption devices at the nodes at both ends of the link, the encryption devices encrypt the transmitted data and decrypt it at the receiving end to ensure the security of data transmission.

[0086] Specifically, for the peak load of network bandwidth and computing resources caused by synchronous updating, the method of the real-time topology algorithm module includes the following steps: dividing the entire network into multiple subnets, each subnet containing a certain number of nodes.

[0087] Topology updates are performed independently within each subnet, and update operations are limited to nodes within the subnet.

[0088] Topology information between subnets is updated asynchronously to avoid synchronous updates of the entire network.

[0089] Design inter-subnet communication protocols to ensure consistent topology information between subnets.

[0090] Furthermore, the inter-subnet communication protocol adopts symmetric encryption, using the same key for encryption and decryption to ensure the security of large amounts of data during transmission.

[0091] Preferably, a data processing module is located in each node and is used to perform data processing tasks, including data cleaning, integration and analysis.

[0092] Preferably, the result summary module is used to obtain and summarize the processing results of each node.

[0093] In summary, the present invention provides a time-staggered topology update mechanism, where different nodes are updated at different times, reducing resource conflicts caused by simultaneous updates; and by dividing the nodes into multiple subnets, each subnet performs topology updates independently, reducing the synchronization overhead across the entire network, thereby improving the scalability of the system while maintaining the real-time performance of the system.

[0094] Embodiment 2 is an embodiment of the present invention, which provides a data planning method based on a multi-node distributed real-time topology algorithm, including: using a node management module to register and monitor the initial status of all nodes; when data is input into the system, the data distribution module distributes the data to the corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module; using the data processing module of the target node to process the distributed data; sending the processing results to the result summary module, and the result summary module summarizes all the results; using the node management module to monitor the system status, and if a fault or load abnormality is found, adjusting the topology structure or restarting the task of the faulty node.

[0095] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that:

[0096] like Figure 5 As shown, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0100] Example 4 is an embodiment of the present invention, which provides a data planning system based on a multi-node distributed real-time topology algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0101] This example simulates a distributed system with 100 nodes. The 100 nodes are divided into 10 groups, each with 10 nodes. A group of nodes is randomly selected for topology update at a fixed time interval of 1 hour, while other groups of nodes keep working normally. During the update process, the data between the updated node and other nodes is synchronized through the inter-node negotiation protocol. After the update is completed, the next group of nodes is selected for update in the next round.

[0102] By utilizing the design of the time staggered strategy, the system load is balanced during the update process; the update time is dynamically adjusted to adapt to changes in the system operating status; the inter-node communication protocol is optimized to ensure the correct transmission of topology information during the update. Table 1 shows the comparison between node load and update efficiency.

[0103] Table 1 Comparison of node load and update efficiency

[0104] Experimental subjects Node 1 Node 2 Node 3 Node 4 Initial load (%) 25 30 20 35 Load after topology update (%) 22 28 18 32 Task completion time (seconds) 120 115 130 110

[0105] It can be seen from the above table that the node load of the present invention is evenly distributed. After the topology is updated, the load is reduced by about 10%. Compared with the traditional system, the present invention significantly improves the data processing efficiency and avoids the phenomenon of overload of a single node. By adopting a distributed topology update algorithm and optimizing the update strategy, the system resource consumption is significantly reduced. Compared with the traditional synchronous update mechanism, the update efficiency is effectively improved.

[0106] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data planning system based on a multi-node distributed real-time topology algorithm, characterized in that: include: Node management module, data distribution module, real-time topology algorithm module, data processing module and result summary module; The node management module is used to manage the registration, status monitoring, and fault detection and recovery of all nodes; The data distribution module is used to distribute data to corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module; The real-time topology algorithm module is used to calculate and maintain the best processing path for data, and dynamically adjust the data flow according to data characteristics and node capabilities; The data processing module is used to perform data processing tasks; The result summary module is used to obtain and summarize the processing results of each node.

2. The data planning system based on a multi-node distributed real-time topology algorithm as claimed in claim 1, characterized in that: The optimal distribution strategy refers to dynamically distributing data on different nodes according to the access frequency and size of the data, the processing capability and the storage capability of the nodes.

3. The data planning system based on multi-node distributed real-time topology algorithm as claimed in claim 2, characterized in that: Distributing the data to the corresponding processing nodes refers to distributing the data to the corresponding processing nodes based on the processing capacity and current load of the nodes according to the optimal distribution strategy provided by the real-time topology algorithm module, including the following steps: Based on the node processing capacity and current load, the weight of each node is calculated. The specific formula is as follows: W i =f(C i ,L i ) Among them, W i is the weight of each node; f is a linear function; a and b are weight coefficients; C i is the processing capacity of node i; L i is the current load of node i; According to the weight W of each node i Allocate data to each node, and the data allocation ratio is as follows: Among them, D i is the amount of data allocated to node i; D total is the total amount of data to be allocated; n is the total number of nodes; Combined with the system operation situation, the weight is adjusted dynamically. The specific formula for dynamic adjustment is as follows: W i (t+1)=W i (t)+ΔW i ΔW i =g ( P i ,L i ,T ) Where, ΔW i is the weight adjustment; g is the adjustment function; P i is the real-time performance index of node i; T is time; The specific formula for adjusting the rules is as follows: ΔW i =k·(P target -P i) -λ·(L i -L avg ) Among them, k and λ are adjustment coefficients; P target is the target performance indicator; L avg is the average load.

4. The data planning system based on multi-node distributed real-time topology algorithm as claimed in claim 3, characterized in that: For simultaneous updates that cause a transient increase in system load, the method of the real-time topology algorithm module includes the following steps: The update time of all nodes is decentralized so that only some nodes perform topology updates at any time. The specific formula is as follows: Among them, T update is the update cycle; N is the total number of nodes; t i Assign an update time point to each node i; According to the importance of the node and the load conditions, the update time of each node is dynamically adjusted. The specific formula is as follows: a i =h ( I i ,L i ) Among them, α i is the adjustment factor; h is the function; I i is the importance of node i; L i is the load condition of node i; ∈ is a small positive number; The specific formula for the adjusted update time is as follows: t' i =t i +a i ·Δt Among them, t' i is the adjusted update time; Δt is the step size of time adjustment; Through the inter-node communication protocol, the topology information during the update period is correctly transmitted to avoid communication errors caused by staggered update times. The specific formula for defining the communication protocol P is as follows: R confirm (i)=(t’ i ,status) S j (t’ i )=P(R confirm (i),S j (t)) Among them, R confirm (i) Node i sends a confirmation request before updating; S j (t' i ) is the node j at time t' i The synchronization state of S j (t) is the state of node j at time t.

5. The data planning system based on multi-node distributed real-time topology algorithm according to claim 4, characterized in that: The inter-node communication protocol adopts a link encryption method, in which encryption devices are installed at the nodes at both ends of the link. The encryption devices encrypt the transmitted data and decrypt it at the receiving end.

6. The data planning system based on multi-node distributed real-time topology algorithm according to claim 5, characterized in that: For synchronous updates that cause peak loads on network bandwidth and computing resources, the method of the real-time topology algorithm module includes the following steps: Divide the entire network into multiple subnets, each of which contains a certain number of nodes; Each subnet performs topology updates independently, and the update operation is limited to the nodes within the subnet; Topology information between subnets is updated asynchronously to avoid synchronous updates of the entire network; Design inter-subnet communication protocols to ensure consistent topology information between subnets.

7. The data planning system based on multi-node distributed real-time topology algorithm according to claim 6, characterized in that: The inter-subnet communication protocol adopts a symmetric encryption method, using the same key for encryption and decryption.

8. A data planning method based on a multi-node distributed real-time topology algorithm, based on the data planning system based on a multi-node distributed real-time topology algorithm according to any one of claims 1 to 7, characterized in that: include, Use the node management module to register and monitor the initial status of all nodes; When data is input into the system, the data distribution module distributes the data to the corresponding processing nodes according to the optimal distribution strategy provided by the real-time topology algorithm module; Using the data processing module of the target node to process the distributed data; The processing results are sent to the result summary module, which summarizes all the results; The node management module is used to monitor the system status. If a fault or load abnormality is found, the topology structure is adjusted or the task of the faulty node is restarted.

9. 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 data planning system based on a multi-node distributed real-time topology algorithm according to any one of claims 1 to 7 are implemented.

10. 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 data planning system based on a multi-node distributed real-time topology algorithm described in any one of claims 1 to 7 are implemented.

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