A network service management method and system for an office area

Abstract

The present invention relates to the technical field of network services, and specifically to a network service management method and system for an office area, including: a preset request node publishes a request task set to a preset management node, and the management node clusters the request task set to divide the request task set into different types of sub-request task sets. By clustering and analyzing the request task set through the management node, the present invention divides similar tasks into the same subset and determines the position of the task clustering center, thereby significantly reducing the complexity of task scheduling, avoiding repeated resource allocation, improving the overall efficiency of task processing, and dynamically allocating resources of edge nodes using an optimization algorithm to ensure the optimal configuration of computing, storage, and bandwidth resources. Through mathematical modeling and solution, it is possible to minimize resource waste and improve resource utilization rate while meeting task requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of network services, and particularly to a network service management method and system for an office area. Background Art

[0002] Traditional methods usually do not adopt task clustering analysis. Task scheduling is based on a single task, lacking the analysis of similarity between tasks, which will lead to a higher complexity of task scheduling and may cause duplicate allocation and waste of resources. Moreover, when traditional methods process a large number of tasks, the scheduling efficiency is low, and it is difficult to obtain good performance in a large-scale environment. In addition, the resource allocation of traditional methods is usually static or based on preset policies, lacking a mechanism for dynamic adjustment. This way cannot perform real-time optimization according to the actual load and resource status of edge nodes, which may lead to unbalanced allocation of computing, storage, and bandwidth resources, resulting in resource waste or processing bottlenecks, affecting the efficiency and performance of the overall system. Furthermore, in traditional methods, the location data of task scheduling and task processing nodes are usually transmitted in plain text, vulnerable to man-in-the-middle attacks or data leakage risks. Especially when dealing with sensitive information, traditional methods cannot guarantee the privacy of location data and user data, and it is easy for information to be stolen or tampered with. And traditional methods lack the support of edge computing. Tasks need to be transmitted to a central server far away from users for processing, resulting in a long response time and low efficiency. Moreover, due to the lack of optimization of dynamic resource allocation and scheduling, tasks may be delayed due to insufficient computing and storage resources or bandwidth limitations. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the above-mentioned disadvantages of the prior art and provide a network service management method and system for an office area.

[0004] The technical solution adopted to solve the above technical problem is: A network service management method for an office area, comprising:

[0005] A preset request node publishes a request task set to a preset management node, and the management node clusters the request task set to divide the request task set into sub-request task sets of different types, and obtains the positions of the task clustering centers of the different types of sub-request task sets;

[0006] Encrypt the positions of the task clustering centers to obtain a first encrypted position, and transmit the task clustering centers and the first encrypted position to an edge node set;

[0007] Construct a resource allocation optimization model between the edge node sets, and solve the resource allocation optimization model based on the ant algorithm to obtain the optimal resource allocation between the edge node sets;

[0008] The edge node set based on optimal resource allocation sends the task clustering center and the first encrypted location to a preset task processing node. If the task processing node decides to execute the task type corresponding to the task clustering center, encrypt the location of the task processing node to obtain a second encrypted location, and transmit the second encrypted location to the corresponding edge node;

[0009] The edge node calculates the distance between the first encrypted location and the second encrypted location, encrypts the distance to obtain an encrypted distance, and transmits the encrypted distance to the management node. The management node selects a task processing node according to the distance and the reliability of all task processing nodes:

[0010] Preferably, the management node clusters the request task set to divide the request task set into multiple sub-request task sets, including:

[0011] Extract the features of each request task in the request task set, where the features of the request task include the request task location, the request task quality requirement, and the request task budget;

[0012] Calculate the Euclidean distance between the features of each request task in the request task set, and calculate the local density of the features of the request task based on the Euclidean distance;

[0013] Calculate the relative distance of the features of each request task based on the local density of the features of the request task;

[0014] Calculate the decision value of the initial cluster based on the local density and relative distance of the features of the request task, and select the cluster center based on the decision value;

[0015] Assign the features of the remaining request tasks to the cluster to which the features of the nearest request task with the highest local density belong one by one in descending order of local density to complete the clustering.

[0016] Preferably, encrypt the location of the task clustering center to obtain a first encrypted location, including:

[0017] Encrypt the location of the task clustering center based on the public key of the management node to obtain an encrypted location, where the encryption formula for the location of the task clustering center is as follows:

[0018]

[0019] Where, and represent the longitude and latitude of the encrypted location, Enc represents the encryption function, and The longitude and latitude indicating the position of the task clustering center, pk c Represents the public key of the management node;

[0020] Encrypt the encrypted position based on the public key of the edge node to obtain a first encrypted position, where the encryption formula for the encrypted position is as follows:

[0021]

[0022] Wherein, and Represent the longitude and latitude of the first encrypted position, pk e Represents the public key of the edge node.

[0023] Preferably, transmit the task clustering center and the first encrypted position to the edge node set, and then further include:

[0024] After receiving the task clustering center and the first encrypted position, the edge node set decrypts the first encrypted position based on the private key of the edge node to obtain the encrypted position, where the decryption formula for the first encrypted position is as follows:

[0025]

[0026] Wherein, Dec represents the decryption function, sk e Represents the private key of the edge node;

[0027] The edge node broadcasts the public key of the management node and the content of the task clustering center to the task processing nodes within its coverage area.

[0028] Preferably, constructing an optimization model for resource allocation among the edge node sets includes:

[0029] Calculate the response time of each edge node in the edge node set, where the calculation formula for the response time of the edge node is as follows:

[0030]

[0031] Wherein, t1 represents the response time of the edge node, D q Represents the data size corresponding to the requested task, v um Represents that edge node U sends the requested task q u To the task processing node E n Of the transmission rate, and B un Represents the bandwidth resource, σ 2 Represents Gaussian white noise, p un Represents that edge node U sends the requested task q uSent to task processing node E n The transmission power, g un Indicates the edge node U and the task processing node E n The channel gain, U n Indicates the set of edge nodes;

[0032] Calculate the processing time of each edge node in the set of edge nodes. Among them, the calculation formula for the processing time of the edge node is as follows:

[0033]

[0034] Among them, t2 represents the processing time of the edge node, W q Indicates the workload of the requested task, R qn Indicates the edge node E n Allocated to the requested task q u The computing resources.

[0035] Preferably, constructing an optimization model for resource allocation between the sets of edge nodes further includes:

[0036] Calculate the total energy consumption of the processing task based on the response time and processing time of the edge node. Among them, the calculation formula for the total energy consumption of the processing task is as follows:

[0037] E total =p un ·t1 + p E ·t2;

[0038] Among them, E total Represents the total energy consumption of the processing task, p E Represents the average power consumption of the edge node for processing the requested task;

[0039] Construct an objective function based on the total energy consumption of the processing task, construct constraint conditions based on the bandwidth resources and computing resources of the edge node, and construct a resource allocation optimization model based on the objective function and the constraint conditions. Among them, the expression of the constraint condition is as follows:

[0040]

[0041] Among them, B n Represents the total bandwidth of the edge node, R n Represents the total computing resources of the edge node, p max Represents the maximum transmission power.

[0042] Preferably, solve the resource allocation optimization model based on the ant algorithm to obtain the optimal resource allocation between the sets of edge nodes, including:

[0043] Initialize the parameters of the ant algorithm. Among them, the parameters of the ant algorithm include the number of ants, the maximum number of iterations, the initial pheromone, the global decay coefficient, and the local decay coefficient. Spread the ants at the positions of the edge nodes, and set the pheromone at the positions of the task processing nodes.

[0044] Calculate the objective function values assigned by each edge node to each task processing node, and calculate the assignment probabilities of each task processing node based on the objective function values.

[0045] Determine the task assignment node based on the assignment probabilities of the task processing nodes.

[0046] Preferably, the calculation formula of the assignment probability is as follows:

[0047]

[0048] Among them, pr represents the assignment probability that the edge node is assigned to the task processing node, τ i,j,k represents the pheromone, represents the heuristic information, β represents the relative importance of determining between the pheromone and the heuristic information, N(u i ) represents multiple task processing nodes that the edge node can be assigned to, represents the sub-channel in the edge node, and

[0049] The determination formula of the task assignment node is as follows:

[0050]

[0051] Among them, represents the task processing node E determined to be assigned by the edge node U n , q represents a random number distributed on [0, 1[, q0 represents a preset threshold parameter, E n-1 represents the processing node E assigned by the edge node U last time n-1 .

[0052] The technical solution adopted to solve the above technical problems is: A network service management system for an office area, which is applicable to the above-mentioned network service management method for an office area, including:

[0053] A task classification unit, which is used for a preset request node to publish a request task set to a preset management node, and the management node clusters the request task set to divide the request task set into different types of sub-request task sets, and obtains the positions of the task clustering centers of the different types of sub-request task sets.

[0054] A location encryption unit, which is used to encrypt the location of the task clustering center to obtain a first encrypted location, and transmit the task clustering center and the first encrypted location to the edge node set;

[0055] A resource allocation unit, which is used to construct an optimization model for resource allocation among the edge node sets, and solve the resource allocation optimization model based on the ant algorithm to obtain the optimal resource allocation among the edge node sets;

[0056] A task transmission unit, which is used to send the task clustering center and the first encrypted location to a preset task processing node based on the edge node set with the optimal resource allocation. If the task processing node decides to execute the task type corresponding to the task clustering center, encrypt the location of the task processing node to obtain a second encrypted location, and transmit the second encrypted location to the corresponding edge node;

[0057] A task selection unit, which is used for the edge node to calculate the distance between the first encrypted location and the second encrypted location, encrypt the distance to obtain an encrypted distance, and transmit the encrypted distance to the management node. The management node selects a task processing node according to the distance and the reliability of all task processing nodes.

[0058] The beneficial effects of the present invention are as follows: (1) The present invention conducts clustering analysis on the request task set through the management node, divides similar tasks into the same subset, and determines the location of the task clustering center, thereby significantly reducing the complexity of task scheduling, avoiding repeated resource allocation, improving the overall efficiency of task processing, and dynamically allocating the resources of the edge nodes by using an optimization algorithm to ensure the optimal configuration of computing, storage, and bandwidth resources. Through mathematical modeling and solution, it can minimize resource waste and improve resource utilization on the premise of meeting task requirements; (2) The present invention encrypts the location of the task clustering center and the location of the task processing node to ensure that sensitive information is not stolen or tampered with during transmission. Moreover, the edge node calculates the distance of the encrypted location information and transmits the result to the management node in an encrypted form. This mechanism avoids the plaintext exposure of location data and effectively prevents man-in-the-middle attacks or location privacy leaks; (3) The present invention sinks task scheduling and resource allocation to the edge nodes, reducing the latency of data transmission to the central server. The edge nodes process tasks nearby and can quickly respond to requests, especially suitable for office scenarios with high real-time requirements. Moreover, from task publication to final execution, all links involving location and user data adopt encrypted transmission to meet enterprise data privacy protection. Description of the Drawings

[0059] Figure 1Schematic diagram of the step flow of the overall method in an embodiment proposed by the present invention;

[0060] Figure 2 Schematic diagram of the system architecture of the overall system in an embodiment proposed by the present invention.

[0061] Reference numerals: 1, task classification unit; 2, location encryption unit; 3, resource allocation unit; 4, task transmission unit; 5, task selection unit. Detailed implementation manners

[0062] Embodiment 1, as Figure 1 shown, a network service management method for an office area proposed by the present invention includes:

[0063] S1. A preset request node publishes a request task set to a preset management node, and the management node clusters the request task set to divide the request task set into sub-request task sets of different types, and obtains the positions of the task clustering centers of the sub-request task sets of different types;

[0064] S2. Encrypt the position of the task clustering center to obtain a first encrypted position, and transmit the task clustering center and the first encrypted position to the edge node set;

[0065] S3. A resource allocation optimization model between the edge node sets is solved based on the ant algorithm to obtain the optimal resource allocation between the edge node sets;

[0066] S4. The edge node set based on the optimal resource allocation sends the task clustering center and the first encrypted position to a preset task processing node. If the task processing node decides to execute the task type corresponding to the task clustering center, encrypt the position of the task processing node to obtain a second encrypted position, and transmit the second encrypted position to the corresponding edge node;

[0067] S5. The edge node calculates the distance between the first encrypted position and the second encrypted position, encrypts the distance to obtain an encrypted distance, and transmits the encrypted distance to the management node. The management node selects a task processing node according to the distance and the reliability of all task processing nodes.

[0068] In the present invention, the requesting node refers to the node that initiates a request task in the network system. These request tasks are usually tasks that need to be processed by task processing nodes in the network. The management node is the node in the entire system responsible for receiving and managing the set of request tasks. The main tasks of the management node are to classify, allocate, schedule, and make relevant decisions on the request tasks. The task clustering center refers to the central position of each type of task after being processed by the clustering algorithm in task clustering analysis. Clustering means dividing tasks into different sub-task sets according to certain characteristics. The clustering center usually represents the "representative" task or core task of this type of task. The edge node set refers to a group of nodes located at the edge of the network, which usually have the functions of processing, storing, or transmitting information. Edge computing places data processing on devices close to the data source, thereby reducing latency and bandwidth requirements. The edge node set is used to store and forward encrypted task information. The resource allocation optimization model refers to a mathematical model used to describe how to reasonably allocate resources such as computing, storage, and bandwidth among multiple nodes or systems to achieve the optimization of system performance or benefits. The task processing node refers to the node in the network responsible for executing tasks. It receives the tasks assigned by the management node and processes them according to the task type. In this process, the task processing node may select to execute specific tasks according to the task type and workload. Reliability usually refers to the stability and fault tolerance ability of a system, node, or network. The management node selects the most suitable node to process tasks based on the reliability of the task processing node. Reliability can include node availability, performance, historical performance, etc.

[0069] Embodiment 2. A network service management method for an office area proposed by the present invention. Compared with Embodiment 1, this embodiment further includes: The management node clusters the set of request tasks to divide the set of request tasks into multiple sub-request task sets, including:

[0070] A1. Extract the characteristics of each request task in the set of request tasks. Among them, the characteristics of the request task include the request task location, the quality requirement of the request task, and the budget of the request task;

[0071] A2. Calculate the Euclidean distance between the characteristics of each request task in the set of request tasks, and calculate the local density of the characteristics of the request task based on the Euclidean distance;

[0072] A3. Calculate the relative distance of the characteristics of each request task based on the local density of the characteristics of the request task;

[0073] A4. Calculate the decision value of the initial cluster based on the local density and relative distance of the characteristics of the request task, and select the cluster center based on the decision value;

[0074] A5. Assign the features of the remaining request tasks to the cluster to which the features of the nearest request task with the highest local density belong one by one in descending order of local density to complete the clustering.

[0075] In this embodiment, the request task location refers to the geographical location or spatial location of the request task, usually represented by coordinates, etc.; the request task quality requirement refers to the quality standard or requirement of the request task, which may be the requirements for aspects such as the task completion effect and time; the request task budget refers to the cost or budget that the request task can bear.

[0076] It should be noted that the local density is an index describing the degree of data point distribution density in its neighborhood. In the clustering algorithm, the local density is often used to measure the relationship between a data point and its neighboring points.

[0077] In an alternative embodiment, encrypt the location of the task clustering center to obtain a first encrypted location, including:

[0078] B1. Encrypt the location of the task clustering center based on the public key of the management node to obtain an encrypted location. The encryption formula for the location of the task clustering center is as follows:

[0079]

[0080] Among them, and represent the longitude and latitude of the encrypted location, Enc represents the encryption function, and represent the longitude and latitude of the location of the task clustering center, pk c represents the public key of the management node;

[0081] B2. Encrypt the encrypted location based on the public key of the edge node to obtain a first encrypted location. The encryption formula for the encrypted location is as follows:

[0082]

[0083] Among them, and represent the longitude and latitude of the first encrypted location, pk e represents the public key of the edge node.

[0084] It should be noted that the encryption function is a mathematical algorithm that performs encryption operations. It accepts the original plaintext data (such as longitude and latitude) and the key as inputs and outputs encrypted data after a certain transformation.

[0085] In an alternative embodiment, after transmitting the task clustering center and the first encrypted location to the edge node set, it further includes:

[0086] After the edge node set receives the task clustering center and the first encrypted location, it decrypts the first encrypted location based on the private key of the edge node to obtain the encrypted location. The decryption formula for the first encrypted location is as follows:

[0087]

[0088] where Dec represents the decryption function, and sk e represents the private key of the edge node;

[0089] C2. The edge node broadcasts the public key of the management node and the content of the task clustering center to the task processing nodes within its coverage area.

[0090] It should be noted that the first encrypted location refers to the result of encrypting the longitude and latitude of the task clustering center with the public key of the management node and then performing secondary encryption with the public key of the edge node. Through this double-encryption method, the location information is protected, and only the node with the corresponding private key can decrypt and restore the original information. Broadcasting means sending information to all nodes or devices in a network. The edge node will broadcast the public key of the management node and the content of the task clustering center to the task processing nodes within its coverage area, which means that all nodes receiving this broadcast can use the broadcast information for data encryption or task processing.

[0091] In an optional embodiment, an optimization model for resource allocation among edge node sets is constructed, including:

[0092] D1. Calculate the response time of each edge node in the edge node set. The calculation formula for the response time of the edge node is as follows:

[0093]

[0094] where t1 represents the response time of the edge node, D q represents the data size corresponding to the requested task, v um represents the transmission rate at which edge node U sends the requested task q u to task processing node E n , and B un represents the bandwidth resource, σ 2 represents the Gaussian white noise, p un represents the transmission power at which edge node U sends the requested task q u to task processing node E n , g un represents the channel gain between edge node U and task processing node E n , and U n represents the edge node set;

[0095] D2. Calculate the processing time of each edge node in the edge node set. The calculation formula for the processing time of an edge node is as follows:

[0096]

[0097] where t2 represents the processing time of the edge node, W q represents the workload of the request task, and R qn represents the computing resources allocated by the edge node E n to the request task q u of.

[0098] It should be noted that the transmission rate refers to the transmission rate when the edge node sends data to the task processing node, usually expressed in bits per second (bps). It determines the speed of data transmission. The higher the transmission rate, the more data can be transmitted in a shorter time; the bandwidth is the amount of data that the network can transmit per unit time, usually measured in bits per second (bps). Bandwidth resources refer to the network capacity available for data transmission. The size of the bandwidth affects the data transmission rate. The larger the bandwidth, the faster the data transmission speed; Gaussian white noise is a signal noise model in which the amplitude of the noise follows a Gaussian distribution; the channel gain refers to the gain or attenuation of the transmitted signal during the transmission process due to the propagation environment (such as distance, obstacles, reflections, etc.).

[0099] In an alternative embodiment, constructing an optimization model for resource allocation between edge node sets further includes:

[0100] D3. Calculate the total energy consumption of the processing task based on the response time and processing time of the edge node. The calculation formula for the total energy consumption of the processing task is as follows:

[0101] E total = p un ·t1 + p E ·t2;

[0102] where E total represents the total energy consumption of the processing task, and p E represents the average power consumption of the edge node for processing the request task;

[0103] D4. Construct an objective function based on the total energy consumption of the processing task, construct constraint conditions based on the bandwidth resources and computing resources of the edge node, and construct an optimization model for resource allocation based on the objective function and the constraint conditions. The expression of the constraint condition is as follows:

[0104]

[0105] where B n represents the total bandwidth of the edge node, and Rn represents the total computing resources of the edge nodes, p max represents the maximum transmission power.

[0106] In an optional embodiment, the resource allocation optimization model is solved based on the ant algorithm to obtain the optimal resource allocation among the edge node sets, including:

[0107] E1. Initialize the parameters of the ant algorithm. Among them, the parameters of the ant algorithm include the number of ants, the maximum number of iterations, the initial pheromone, the global attenuation coefficient, and the local attenuation coefficient. Scatter the ants at the positions of the edge nodes, and set the pheromone at the positions of the task processing nodes;

[0108] E2. Calculate the objective function value of each edge node allocated to each task processing node, and calculate the allocation probability of each task processing node based on the objective function value;

[0109] E3. Determine the task allocation nodes based on the allocation probability of the task processing nodes.

[0110] It should be noted that the ant algorithm is a heuristic algorithm that simulates the foraging behavior of ants in nature and is mainly used to solve combinatorial optimization problems; the number of ants refers to the total number of ant individuals simulated in the ant algorithm. Each ant represents a possible solution. In each iteration, all ants will traverse the problem space and search for solutions. The setting of the number of ants affects the breadth and convergence speed of the search; the global attenuation coefficient represents the attenuation rate of the pheromone during global update. Global attenuation is usually applied to the paths passed by all ants and affects the pheromone concentration of these paths; the allocation probability refers to the probability that a task is allocated to a certain task processing node. This probability is usually calculated based on the pheromone concentration and the objective function value of each path.

[0111] In an optional embodiment, the calculation formula for the allocation probability is as follows:

[0112]

[0113] where pr represents the allocation probability of the edge node allocated to the task processing node, τ i,j,k represents the pheromone, represents the heuristic information, β represents the relative importance of determining the pheromone and the heuristic information, N(u i ) represents multiple task processing nodes that the edge node can be allocated to, represents the sub-channel in the edge node, and

[0114] The formula for determining the task allocation node is as follows:

[0115]

[0116] Among them, represents that the edge node U determines the allocated task processing node E n , q represents a random number distributed on [0, 1], q0 represents a preset threshold parameter, and E n-1 represents the processing node E allocated by the edge node U last time n-1 .

[0117] Example 3. As Figure 2 shown, a network service management system for an office area proposed by the present invention, and a network service management method applicable to the office area, include:

[0118] Task classification unit 1. The task classification unit 1 is used for a preset request node to publish a request task set to a preset management node, and the management node clusters the request task set to divide the request task set into sub-request task sets of different types, and obtains the positions of the task clustering centers of the sub-request task sets of different types;

[0119] Location encryption unit 2. The location encryption unit 2 is used to encrypt the positions of the task clustering centers to obtain a first encrypted position, and transmit the task clustering centers and the first encrypted position to the edge node set;

[0120] Resource allocation unit 3. The resource allocation unit 3 is used to construct a resource allocation optimization model between the edge node sets, and solve the resource allocation optimization model based on the ant algorithm to obtain the optimal resource allocation between the edge node sets;

[0121] Task transmission unit 4. The task transmission unit 4 is used to send the task clustering centers and the first encrypted position to a preset task processing node based on the edge node set with the optimal resource allocation. If the task processing node decides to execute the task type corresponding to the task clustering center, encrypt the position of the task processing node to obtain a second encrypted position, and transmit the second encrypted position to the corresponding edge node;

[0122] Task selection unit 5. The task selection unit 5 is used for the edge node to calculate the distance between the first encrypted position and the second encrypted position, encrypt the distance to obtain an encrypted distance, and transmit the encrypted distance to the management node. The management node selects the task processing node according to the distance and the reliability of all task processing nodes.

[0123] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to this. Various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims

1. A network service management method for an office area, characterized in that: include: The preset request node publishes the request task set to the preset management node, and the management node clusters the request task set to divide the request task set into different types of sub-request task sets, and obtains the positions of the task clustering centers of the different types of sub-request task sets; Encrypting the position of the task cluster center to obtain a first encrypted position, and transmitting the task cluster center and the first encrypted position to the edge node set; Constructing a resource allocation optimization model between the edge node sets, and solving the resource allocation optimization model based on an ant algorithm to obtain an optimal resource allocation between the edge node sets; The edge node set based on the optimal resource allocation sends the task cluster center and the first encrypted position to a preset task processing node. If the task processing node decides to execute the task type corresponding to the task cluster center, the position of the task processing node is encrypted to obtain a second encrypted position, and the second encrypted position is transmitted to the corresponding edge node. The edge node calculates the distance between the first encrypted position and the second encrypted position, encrypts the distance to obtain an encrypted distance, transmits the encrypted distance to the management node, and the management node selects a task processing node based on the distance and the reliability of all task processing nodes.

2. A network service management method for an office area according to claim 1, characterized in that: The management node clusters the request task set to divide the request task set into a plurality of sub-request task sets, including: Extracting features of each request task in the request task set, wherein the features of the request task include a request task location, a request task quality requirement, and a request task budget; Calculating the Euclidean distance between the features of each request task in the request task set, and calculating the local density of the features of the request task based on the Euclidean distance; Calculating relative distances of features of each request task based on local densities of features of the request task; Calculating a decision value of an initial cluster based on the local density and relative distance of the features of the requested task, and selecting a cluster center based on the decision value; The remaining features of the requested task are assigned one by one in descending order of local density to the cluster to which the features of the requested task with the highest local density belong, so as to complete clustering.

3. A network service management method for an office area according to claim 1, characterized in that: Encrypting the position of the task cluster center to obtain a first encrypted position includes: The position of the task cluster center is encrypted based on the public key of the management node to obtain an encrypted position, wherein the encryption formula of the position of the task cluster center is as follows: in, and Indicates the longitude and latitude of the encrypted location, Enc indicates the encryption function, and pk represents the longitude and latitude of the location of the task cluster center. c Indicates the public key of the management node; The encrypted position is encrypted based on the public key of the edge node to obtain a first encrypted position, wherein the encryption formula of the encrypted position is as follows: in, and represents the longitude and latitude of the first encrypted position, pk e Indicates the public key of the edge node.

4. A network service management method for an office area according to claim 3, characterized in that: The task cluster center and the first encrypted position are transmitted to the edge node set, and then the method further includes: After receiving the task cluster center and the first encrypted position, the edge node set decrypts the first encrypted position based on the private key of the edge node to obtain an encrypted position, wherein the decryption formula of the first encrypted position is as follows: Among them, Dec represents the decryption function, sk e Represents the private key of the edge node; The edge node broadcasts the public key of the management node and the content of the task cluster center to the task processing nodes within its coverage.

5. The network service management method for an office area according to claim 1, characterized in that: Constructing a resource allocation optimization model between the edge node sets, including: Calculate the response time of each edge node in the edge node set, wherein the calculation formula of the response time of the edge node is as follows: Among them, t1 represents the response time of the edge node, D q Indicates the data size corresponding to the request task, v um Indicates that edge node U will request task q u Send to task processing node E n The emission rate is B un represents bandwidth resources, σ 2 represents Gaussian white noise, p un Indicates that edge node U will request task q u Send to task processing node E n Transmitting power, g un Represents edge nodes U and task processing nodes E n The channel gain, U n Represents a set of edge nodes; The processing time of each edge node in the edge node set is calculated, wherein the calculation formula of the processing time of the edge node is as follows: Where t2 represents the processing time of the edge node, W q represents the workload of the request task, R qn Represents the edge node E n Assigned to request task q u computing resources.

6. A method for managing network services in an office area according to claim 5, characterized in that: Constructing a resource allocation optimization model between the edge node sets also includes: The total energy consumption of the processing task is calculated based on the response time and processing time of the edge node, wherein the calculation formula of the total energy consumption of the processing task is as follows: AND total =p un ·t1+p E ·t2; Among them, E total represents the total energy consumption of processing tasks, p E It represents the average power consumption of edge nodes in processing request tasks; An objective function is constructed based on the total energy consumption of the processing task, a constraint condition is constructed based on the bandwidth resources and computing resources of the edge node, and a resource allocation optimization model is constructed based on the objective function and the constraint condition, wherein the constraint condition is expressed as follows: Among them, B n represents the total bandwidth of the edge node, R n represents the total computing resources of the edge nodes, p max Indicates the maximum transmit power.

7. A method for managing network services in an office area according to claim 6, characterized in that: Solving the resource allocation optimization model based on the ant algorithm to obtain the optimal resource allocation between the edge node sets includes: Initializing the parameters of the ant algorithm, wherein the parameters of the ant algorithm include the number of ants, the maximum number of iterations, the initial pheromone, the global attenuation coefficient and the local attenuation coefficient, scattering the ants at the positions of the edge nodes, and setting the pheromone at the positions of the task processing nodes; Calculating the objective function value assigned by each edge node to each task processing node, and calculating the allocation probability of each task processing node based on the objective function value; The task allocation node is determined based on the allocation probability of the task processing node.

8. A method for managing network services in an office area according to claim 7, characterized in that: The calculation formula of the allocation probability is as follows: Among them, pr represents the probability of allocating edge nodes to task processing nodes, τ i,j,k Indicates pheromone, represents the heuristic information, β represents the relative importance between pheromone and heuristic information, N(u i ) represents multiple task processing nodes that can be assigned to edge nodes, represents a subchannel in an edge node, and The determination formula of the task allocation node is as follows: in, Indicates that the edge node U determines the assigned task processing node E n , q represents a random number distributed on [0,1], q0 represents the preset threshold parameter, E n-1 Indicates the processing node E that was last assigned to the edge node U n-1 .

9. A network service management system for an office area, which is applicable to a network service management method for an office area as claimed in any one of claims 1 to 8, characterized in that: include: A task classification unit (1), wherein the task classification unit (1) is used for a preset request node to publish a request task set to a preset management node, wherein the management node clusters the request task set to divide the request task set into different types of sub-request task sets, and obtains the positions of task cluster centers of the different types of sub-request task sets; A position encryption unit (2), the position encryption unit (2) is used to encrypt the position of the task cluster center to obtain a first encrypted position, and transmit the task cluster center and the first encrypted position to the edge node set; A resource allocation unit (3), the resource allocation unit (3) is used to construct a resource allocation optimization model between the edge node sets, and solve the resource allocation optimization model based on an ant algorithm to obtain an optimal resource allocation between the edge node sets; A task transmission unit (4), the task transmission unit (4) is used to send the task cluster center and the first encrypted position to a preset task processing node based on the edge node set with the best resource allocation, if the task processing node decides to execute the task type corresponding to the task cluster center, then encrypt the position of the task processing node to obtain a second encrypted position, and transmit the second encrypted position to the corresponding edge node; A task selection unit (5), wherein the task selection unit (5) is used for the edge node to calculate the distance between the first encrypted position and the second encrypted position, encrypt the distance to obtain an encrypted distance, and transmit the encrypted distance to the management node, wherein the management node selects a task processing node based on the distance and the reliability of all task processing nodes.

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