Method and system for resource allocation of smart city based on data stability

Abstract

The application provides a method and system for resource allocation of smart cities based on data stability, wherein the method comprises the following steps: determining a smart city set composed of multiple smart cities with weakly associated resources; determining an initial data security benefit E i of each smart city when the smart city does not share resources with associated data resources, and a resource provision coefficient τ; determining a resource loss cost L i of each smart city due to data leakage caused by illegal intrusion when the smart city shares resources with associated data resources, and a loss cost coefficient μ i ; determining a data overflow coefficient ω of each smart city; randomly selecting a first and a second smart city from the smart city set to form a data sharing system; determining local stability based on an equilibrium point of the data sharing system, determining data stability of the sharing system based on the local stability, and allocating shared resources of the first and the second smart cities based on the data stability.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and more specifically to methods and systems for resource allocation in smart cities based on data stability, computer-readable storage media, and electronic devices. Background Technology

[0002] Since its inception, smart cities have been valued worldwide. While improving the level of urban intelligence, they have also provided more convenient conditions for people's lives. However, because smart cities rely heavily on new technologies such as cloud computing and the Internet of Things, the application of these technologies has brought the hidden danger of information risk spread, posing a multi-faceted impact on urban information security. How to rationally allocate the current digital resources of cities and minimize these information security risks has become a practical problem that must be faced for the healthy development of smart cities.

[0003] As smart city construction matures, people's lives become increasingly convenient, and the exchange of information resources between cities becomes more extensive. However, smart cities also face numerous information security threats. Smart city clusters participating in information security may have weakly correlated external resources. Due to the characteristics of weak correlation and incomparability, cities encounter new challenges in the allocation of information security resources. Summary of the Invention

[0004] This application first describes the problem based on the characteristics of weakly correlated external resources, establishes a mathematical model after making reasonable assumptions, then explores and analyzes the equilibrium point and the stability of the model, and verifies the above model through data simulation.

[0005] According to one aspect of the present invention, a method for resource allocation in smart cities based on data stability is provided, the method comprising:

[0006] A set of smart cities is defined as a collection of smart cities that have weakly correlated resources with each other, wherein a weakly correlated resource is defined as a resource in the smart city set where the ratio of the amount of correlated data resources between any two smart cities to the total amount of data resources of the two smart cities is less than or equal to a correlation threshold.

[0007] When it is determined that each smart city does not share related data resources, the initial data security benefit E obtained by each smart city based on information security configuration is... i And the resource provision coefficient τ, which determines the external systems of the smart city set that can provide data resources to the smart cities in the smart city set;

[0008] When determining the resource loss cost L caused by data leakage due to unauthorized intrusion during resource sharing for each smart city's shared data resources, the cost should be considered.i and loss cost coefficient μ i And determine the data spillover coefficient ω when each smart city shares related data resources;

[0009] Randomly select a first smart city and a second smart city from the set of smart cities, and combine the first smart city and the second smart city into a data sharing system; and

[0010] Local stability is determined based on the equilibrium point of the data sharing system, and the data stability of the sharing system is determined based on the local stability. Based on the data stability, the shared resources of the first smart city and the second smart city in the data sharing system are configured.

[0011] Preferably, the association threshold is 5%, 10%, 15%, or 20%.

[0012] Preferably, the first smart city is smart city i, and the second smart city is smart city j;

[0013] Scenario 1: When both smart city i and smart city j choose to share information, determine the data security benefits S for smart city i and smart city j. i1 and S j1 for:

[0014] S i1 =E i +ωL j +τL i -μ i L i (6.1)

[0015] S j1 =E j +ωL i +τL j -μ j L j (6.2)

[0016] Scenario 2: When smart city i chooses to share information, while smart city j chooses not to, determine the data security benefits S for smart city i and smart city j. i2 and S j2 for:

[0017] S i2 =E i +τL i -μ i L i (6.3)

[0018] S j2 =Ej +L i (6.4)

[0019] Scenario 3: When smart city i chooses not to share information, while smart city j chooses to share information, determine the data security benefits S for smart city i and smart city j. i3 and S j3 for:

[0020] S i3 =E i +L j (6.5)

[0021] S j3 =E j +τL j -μ j L j (6.6)

[0022] Scenario 4: When both smart city i and smart city j choose not to share information, determine the data security S of smart city i and smart city j. i4 and S j4 for:

[0023] S i4 =E i (6.7)

[0024] S j4 =E j (6.8)

[0025] Among them, S i1 For the first scenario, the data security benefits of smart city i; S j1 For the first scenario, the data security benefits of smart city j; S i2 For the second scenario, the data security benefits of smart city i; S j2 For the second scenario, the data security benefits of smart city j; S i3 For the third scenario, the data security benefits of smart city i; S j3 For the third scenario, the data security benefits of smart city j; S i4 For the fourth scenario, the data security benefits of smart city i; S j4 For the fourth scenario, the data security benefits of smart city j; E i E represents the initial data security benefits obtained by the i-th smart city based on information security configuration. j L represents the initial data security benefits obtained by the j-th smart city based on information security configuration. iL represents the resource loss cost of the i-th smart city due to unauthorized intrusion during resource sharing, resulting in data leakage; j Let τ be the resource loss cost of the j-th smart city due to unauthorized intrusion during resource sharing; τ is the resource provision coefficient of the external systems of the smart city set that can provide data resources to the smart cities in the smart city set; τL i Let τL be the amount of data resources that the i-th smart city can obtain from external systems of the smart city set; j Let μ be the amount of data resources that the j-th smart city can obtain from external systems within the smart city set; i Let μ be the cost coefficient for the data leakage caused by unauthorized intrusion during resource sharing in the i-th smart city. j Let ω be the loss cost coefficient of the j-th smart city due to illegal intrusion during resource sharing; ω is the effect coefficient of the data spillover effect when the smart city chooses to share information, and ω≥1; ωL i If both city i and city j choose the sharing strategy, then city j can obtain data security benefits from sharing with city i, ωL j If both city i and city j choose the sharing strategy, then city i can obtain data security benefits through sharing with city j.

[0026] Preferably, when S is determined ik (k = 1, 2, ..., 4) is the data acquisition function of the i-th smart city, and S jk When (k = 1, 2, ..., 4) is the data acquisition function for the j-th city, then:

[0027] (1) 0 ≤ S ik ≤T;

[0028] (2) 0 ≤ S jk ≤T′;

[0029] (3) The effect coefficient ω and the resource provision coefficient τ, for the data acquisition function S ik and S jk All are monotonically increasing, and the loss cost coefficient μ is related to the data acquisition function S. ik and S jk All are monotonically decreasing.

[0030] Preferably, for the i-th smart city, when acting as a data resource provider, if the proportion of information sharing is θ, then the proportion of information sharing is 1-θ.

[0031] For the j-th smart city, when acting as a data resource demander, the proportion of those choosing information sharing is: The proportion of those who choose not to share information is: but

[0032] Preferably, the expected benefit of data security for the i-th smart city, whether it chooses to share information or not, is:

[0033] In the case of information sharing, the expected benefit S of data security is for:

[0034]

[0035] Without information sharing, the expected benefit S of data security in for:

[0036]

[0037] The expected data security benefit for the j-th smart city, considering both the option to share information and the option not to share information, is:

[0038] When information is shared, the expected benefits of data security are:

[0039] S js =θS j1 +(1-θ)S j3

[0040] =θ(E j +ωL i +τL j -μ j L j )+(1-θ)(E j +τL j -μ j L j )

[0041] =θωL i +E j +τL j -μ j L j (6.11)

[0042] Without information sharing, the expected benefits of data security are:

[0043] S jn =θS j2 +(1-θ)S j4

[0044] =θ(E j +L i )+(1-θ)E j

[0045] =θL i+E j (6.12)

[0046] Preferably, it further includes determining the overall expected benefit S of data security for the i-th smart city. i The overall expected benefit S of data security for the j-th smart city j for:

[0047]

[0048]

[0049] The replication dynamic equation F(θ) of the i-th smart city and the replication dynamic equation of the j-th smart city will be used to share information. It is expressed as follows:

[0050]

[0051]

[0052] Preferably, it further includes determining the equilibrium point of the data sharing system or the replica dynamic system when the replica dynamic equations (6.15) and (6.16) are equal to zero. Therefore, based on the replicating dynamic equations (6.15) and (6.16), the following can be determined:

[0053] (1) The following four points are candidate evolutionary equilibrium points: O(0,0), A(1,0), B(0,1), C(1,1);

[0054] (2 points) It is also a candidate evolutionary equilibrium point, among which and

[0055] Based on the stability requirements of the replicating dynamic system and the differential equation, the equilibrium point of the data sharing system or replicating dynamic system must meet the following conditions:

[0056]

[0057] Substituting formulas (6.15) and (6.16) into formula (6.17), we obtain the following result:

[0058]

[0059] Preferably, it further includes determining local stability based on the Jacobian matrix J of the data sharing system or the replicating dynamic system, thereby determining the stability strategy of the data sharing system or the replicating dynamic system:

[0060]

[0061] in,

[0062]

[0063]

[0064]

[0065]

[0066] Determining the equilibrium point and stability of an evolutionary game using the properties of the Jacobian matrix includes: using the values ​​of the determinant det(J) and trace tr(J) to determine the equilibrium point and stability of the evolutionary game. If the determinant tr(J) is greater than zero and the trace tr(J) is less than zero, then the equilibrium point of the data sharing system or the replicating dynamic system is locally stable. This equilibrium point is then chosen as the evolutionary equilibrium point of the data sharing system or the replicating dynamic system, resulting in the following formula:

[0067] Determinant:

[0068] trace:

[0069] Preferably, it also includes;

[0070] when or At that time, the evolutionary equilibrium point is C(1,1);

[0071] Both the i-th and j-th smart cities have chosen to share information (information sharing is also known as data resource sharing, data sharing, or resource sharing).

[0072] Preferably, it also includes,

[0073] when At that time, the evolutionary equilibrium point is determined to be A(1,0);

[0074] The i-th smart city chooses to share information, while the j-th smart city chooses not to share information.

[0075] Preferably, it also includes,

[0076] when At that time, the evolutionary equilibrium points are determined to be O(0,0) and C(1,1);

[0077] When both the i-th and j-th smart cities choose to share information, the external system of the smart city set provides the first amount of data resources to the smart cities in the smart city set.

[0078] When both the i-th and j-th smart cities choose not to share information, the external system of the smart city set provides the smart cities in the smart city set with a second amount of data resources.

[0079] The second quantity is greater than the first quantity.

[0080] Preferably, it also includes,

[0081] when or At that time, the evolutionary equilibrium point is determined to be O(0,0);

[0082] Both the i-th and j-th smart cities choose not to share information, and the external systems of the smart city set provide a third number of data resources to the smart cities in the smart city set.

[0083] According to another aspect of the present invention, a system for resource allocation in smart cities based on data stability is provided, the system comprising:

[0084] A first determining device is used to determine a set of smart cities consisting of multiple smart cities that have weakly correlated resources with each other, wherein a weakly correlated resource is defined as the ratio of the amount of correlated data resources between any two smart cities in the set to the total amount of data resources of the two smart cities being less than or equal to a correlation threshold.

[0085] The second determining device is used to determine the initial data security benefit E obtained by each smart city based on information security configuration when each smart city does not share related data resources. i And the resource provision coefficient τ, which determines the external systems of the smart city set that can provide data resources to the smart cities in the smart city set;

[0086] The third determining device is used to determine the resource loss cost L caused by data leakage due to illegal intrusion during resource sharing when each smart city shares related data resources. i and loss cost coefficient μ i And determine the data spillover coefficient ω when each smart city shares related data resources;

[0087] A constitutive apparatus is used to randomly select a first smart city and a second smart city from the set of smart cities, and to form a data sharing system between the first smart city and the second smart city.

[0088] A configuration device is used to determine local stability based on the equilibrium point of the data sharing system, and to determine the data stability of the sharing system based on the local stability, and to configure the shared resources of the first smart city and the second smart city within the data sharing system based on the data stability.

[0089] According to another aspect of the present invention, a computer-readable storage medium is provided, characterized in that the storage medium stores a computer program for performing the methods described in any of the above embodiments.

[0090] According to another aspect of the present invention, an electronic device is provided, comprising:

[0091] processor;

[0092] Memory used to store the processor's executable instructions;

[0093] The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the above embodiments.

[0094] According to another aspect of the present invention, a computer program product is provided, including computer-readable code, which, when run on a device, enables a processor in the device to perform the method described in any of the above embodiments.

[0095] With the continuous development of smart city or network system construction and the continuous improvement of the informatization level of smart city or network systems, people's lives are becoming more and more convenient. At the same time, it has also brought many new information security problems to smart city or network systems. In the past, information security threats were only faced by a single smart city or network system. However, now it may be necessary to face not only internal threats of a single smart city or network system, but also public group threats. The characteristics of "weak correlation" and "incomparability" make it more difficult for smart city or network systems to deal with information security problems. Based on the characteristics of weakly correlated external resources, this application establishes a dynamic evolution game model for information security resource allocation in weakly correlated smart city or network systems after making reasonable assumptions. It also conducts an in-depth analysis of the dynamic evolution of participating subjects and influencing factors, and determines the impact of factors such as sharing costs, spillover effects, and resource provision coefficients of external systems or network management systems on the evolutionary path of information sharing by participating subjects. This is conducive to improving the efficiency of information sharing platforms of external systems or network management systems, and has important significance for improving the level of public information security. It also provides an improvement solution for information security problems of smart city or network systems with "weak correlation" and "incomparability" characteristics. Attached Figure Description

[0096] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0097] Figure 1 This is a schematic diagram illustrating the factors influencing information security according to an embodiment of the present invention;

[0098] Figure 2 This is a schematic diagram of the information security factor indicator system according to an embodiment of the present invention;

[0099] Figure 3 A schematic diagram illustrating factors affecting information security in smart cities for identifying threat sources according to embodiments of the present invention;

[0100] Figure 4 A schematic diagram illustrating factors affecting information security in smart cities for vulnerability identification according to an embodiment of the present invention;

[0101] Figure 5 This is a schematic diagram illustrating the factors affecting the information security of smart cities according to embodiments of the present invention.

[0102] Figure 6 This is a schematic diagram of an information security resource configuration framework according to an embodiment of the present invention;

[0103] Figure 7 This is a schematic diagram illustrating the relationships between information security entities within a smart city according to an embodiment of the present invention.

[0104] Figure 8 This is a schematic diagram illustrating the relationship between entities responsible for information security of complementary external resources in cities, according to an embodiment of the present invention.

[0105] Figure 9 This is a schematic diagram illustrating the relationships between entities responsible for information security of alternative external resources in cities, according to an embodiment of the present invention.

[0106] Figure 10 This is a schematic diagram illustrating the relationships between entities responsible for information security of weakly associated external resources in cities, according to an embodiment of the present invention.

[0107] Figure 11 A flowchart illustrating a method for resource allocation in smart cities based on data stability according to an embodiment of the present invention;

[0108] Figure 12 A flowchart illustrating a method for allocating resources in a network system based on data stability according to an embodiment of the present invention;

[0109] Figure 13a This is a phase diagram showing the dynamic evolution of scenario 1 according to an embodiment of the present invention;

[0110] Figure 13b This is a phase diagram showing the dynamic evolution of scenario 2 according to an embodiment of the present invention;

[0111] Figure 13c This is a phase diagram showing the dynamic evolution of scenario 3 according to an embodiment of the present invention;

[0112] Figure 13d This is a phase diagram showing the dynamic evolution of scenario 4 according to an embodiment of the present invention;

[0113] Figure 14a This is a schematic diagram illustrating the evolution path of a smart city or network system i under different cost coefficients according to an embodiment of the present invention;

[0114] Figure 14b This is a schematic diagram illustrating the evolution path of a smart city or network system j under different cost coefficients according to an embodiment of the present invention;

[0115] Figure 14c This is a schematic diagram illustrating the evolution path of a smart city or network system i under different spillover effects according to an embodiment of the present invention.

[0116] Figure 14d This is a schematic diagram illustrating the evolution path of a smart city or network system j under different spillover effects according to an embodiment of the present invention.

[0117] Figure 14e A schematic diagram illustrating the evolution path of a smart city or network system i under different external systems or network management system resource provision coefficients according to an embodiment of the present invention;

[0118] Figure 14f This is a schematic diagram illustrating the evolution path of a smart city or network system j under different external systems or network management system resource provision coefficients according to an embodiment of the present invention.

[0119] Figure 15 This is a schematic diagram of a system for resource allocation in smart cities based on data stability, according to an embodiment of the present invention.

[0120] Figure 16 This is a schematic diagram of the structure of a system for allocating resources in a network system based on data stability, according to an embodiment of the present invention. Detailed Implementation

[0121] It should be understood that, because this application primarily involves digital content such as data resources, data storage, data content, information resources, or digital information, the smart city in this application can be considered as a network cluster, network system, data system, data storage system, network resource collection, or network resource body. Therefore, this application essentially relates to methods and systems for internal resource allocation based on data parameters of smart cities, network clusters, network systems, data systems, data storage systems, network resource collections, or network resource bodies.

[0122] However, because network systems are highly dependent on new technologies such as cloud computing and the Internet of Things, the application of these technologies has brought about the potential for information risk diffusion, posing a multi-faceted impact on the information security of network systems. How to rationally allocate the resources of current network systems and minimize these information security risks has become a practical problem that must be addressed for the healthy development of current network systems.

[0123] Information security is a relatively abstract concept. It mainly involves checking system threats and vulnerabilities and using various methods to manage them, thereby preventing accidental or malicious information damage, leakage, and modification, and avoiding system malfunctions. The main characteristics of information security include: (1) Integrity. It requires that information is not deleted, modified, or forged during its transmission or storage, and that there be no delays, loss, or out-of-order actions, ensuring the integrity of the data. That is, the information is completely and accurately delivered from the source to the true destination without any illegal tampering. (2) Confidentiality. It requires strict control over all checkpoints where information leakage may occur, ensuring that information is not eavesdropped on or leaked. That is, information cannot be leaked to any unauthorized user, process, or entity during each process of generation, transmission, storage, and processing. (3) Availability. It requires ensuring that authorized entities can obtain the information and resources they require, and that the information and resources are available. (4) Controllability. The system must be able to control how information resource users use the information, meaning that information resource applicants are always under the effective control of the information system. (5) Non-repudiation. The system must establish an effective accountability mechanism, meaning that information users must be held responsible for their own actions.

[0124] Information resource allocation refers to the rational combination and distribution of information resources based on information security needs, in order to achieve the best security results. Information resources (or data resources, digital resources) generally include data resources, software resources, equipment resources, human resources, service resources, and other resources.

[0125] Data resources primarily refer to the physical or electronic data stored in the system, including documents and electronic files. Documents include contracts, faxes, reports, plans, proposals, daily data, and incoming / outgoing documents; electronic files include technical solutions, technical reports, information reports, system configuration files, program source code, and database forms. Software resources primarily refer to the software installed in the connected information system used to process, store, or transmit various types of information. This includes application software, utility software, and system software. Equipment resources primarily refer to the hardware facilities or physical equipment connected to the information system, forming the foundation of information resources. This includes host equipment, network equipment, storage equipment, security equipment, and cabling systems. Service resources refer to services that can be ordered or purchased and provide assistance or convenience to certified users. This includes system maintenance services, technical support services, and monitoring and management services. Other resources refer to resources other than those listed above that can provide corresponding direct or hidden value.

[0126] Game theory refers to the process by which multiple participants make decisions based on known information, with their decisions mutually constraining each other, and through continuous reasoning, choose the strategy that maximizes efficiency. Essentially, game theory involves obtaining basic information about participants from a complex environment, constructing appropriate mathematical models to simulate their behavior, and seeking the optimal outcome.

[0127] A Nash equilibrium is a solution found in the mathematical model described above that yields optimal decisions for all participants. In a Nash equilibrium, if some participants' equilibrium policy points remain unchanged, and the remaining participants cannot influence others by altering their own decisions, then the Nash equilibrium is stable.

[0128] Evolutionary game theory offers a novel perspective on game equilibrium, proposing an evolutionarily stable strategy, a dynamic equilibrium that provides a new approach to Nash equilibrium and the selection of equilibrium strategies. This theory posits that if the vast majority of participants choose an evolutionarily stable strategy, mutations by a minority of participants cannot infiltrate the group.

[0129] A game theory problem can be transformed into the following mathematical expression:

[0130] GT = {P, St} i Ut i} (2.1)

[0131] In the formula, GT represents the game problem;

[0132] P represents the set of participants, P = {1, 2, ..., n}, where n is the total number of participants;

[0133] {St i} represents the participant's policy set, St i Let represent the strategy of the i-th participant, where i ∈ P;

[0134] Ut i Let represent the payoff function for the i-th participant, where i ∈ P.

[0135] Evolutionary stable strategies in evolutionary game theory can also be transformed into mathematical descriptions:

[0136] Assume that the participant set P contains The proportion of participants is mutated, with the mutation strategy being y and the normal strategy being x. That is, there is a probability γ that a participant will choose strategy y, and a probability 1-γ that a participant will choose strategy x. The payoff for the mutation strategy is Ut(y, γy+(1-γ)x). If for any mutation strategy y≠x, and if there exists... The inequality Ut(x, γy+(1-γ)x)>Ut(y, γy+(1-γ)x) is satisfied for all If both of these hold true, then x is an evolutionarily stable strategy.

[0137] As can be seen from the above description, an evolutionarily stable strategy needs to satisfy the following conditions simultaneously:

[0138] For any strategy that satisfies y≠x, the following must be true:

[0139] (1) Balance, i.e., Ut(x, x) ≥ Ut(y, x);

[0140] (2) Stability, that is, if Ut(x, x) = Ut(y, x), then Ut(x, y) > Ut(y, y).

[0141] In one embodiment, determining the information security resource allocation influencing factor index system includes: identifying the influencing factors of resource allocation related to information security and establishing a corresponding index system. This is fundamental to reducing information security risks of network systems or data systems in smart cities under the background of big data. From an information security perspective, and considering the current situation of smart cities, the primary indicators of the information security influencing factor index system can be summarized into four aspects: resources, threat sources, vulnerabilities, and security measures. Figure 1 As shown.

[0142] Information resources encompass many types, but it's clear that the higher the value of a resource, the greater the potential risks in practice. Based on the definitions of smart cities and information resources, resource influencing factors are subdivided into three secondary indicators: technological factors, infrastructure, and incremental data resources. Further information security risk analysis is then conducted to obtain tertiary indicators, the results of which are as follows: Figure 2 As shown.

[0143] The number of information security technicians has a significant impact on information security. These individuals, possessing cybersecurity skills, provide assurance for the information security of smart cities. Technician certification is crucial because uncertified technicians who gain unauthorized access could easily lose control of information, thus compromising security. Core equipment is important because most information security infrastructure and key technologies can be controlled by uncontrollable entities, posing significant security risks. Some systems may contain vulnerabilities or backdoors, making information easily tampered with or stolen. The Internet of Things (IoT) infrastructure is vital because, with the continuous development of IoT, its role in smart cities is becoming increasingly important, supporting various urban application services. Attacks on IoT infrastructure can easily compromise personal privacy and trade secrets. Data leaks could even lead to system paralysis. Wireless network equipment is primarily considered because WIFI is an essential part of urban infrastructure, providing many conveniences for smart cities, but there is also a risk of information leakage during data transmission. Application systems can directly affect the construction and development of smart cities, and their maturity reflects the level of urban information security. Direct data resource increments refer to the data resources (including various software, data, hardware, or a combination of software, data, and hardware) added directly for information security construction in smart cities. The amount of data resource increments plays a decisive role in the construction and protection of information security to a large extent. Indirect data resource increments mainly refer to the resource increments added for information security through other means or for other purposes, which also play a certain role in protecting information security.

[0144] Threat Sources: A threat is an objectively existing factor that may pose a potential risk to the information security of a smart city. Threat source influencing factors are further subdivided into two secondary indicators: technical threats and operational threats. Further information security risk analysis is then conducted on these secondary indicators to obtain tertiary indicators, as shown below. Figure 3As shown in the diagram. The physical environment primarily considers system interruptions caused by external disasters, leading to the loss of important data or files and increasing the probability of information security risks. Hardware and software primarily consider their failure rates; since smart city systems contain a large amount of hardware and software, failures can lead to service interruptions, data corruption, or loss, causing information security risks. Data primarily considers data theft and data tampering, which are currently the most prominent problems facing smart cities. Hacker intrusions can lead to the leakage of personal information and trade secrets, and sensitive data is easily lost or mismanaged, making it difficult to guarantee data confidentiality. Operational management focuses on preventing uncertainties and unreasonable network operation states, taking a risk-oriented approach and emphasizing the development of reasonable regulations for medium- and high-risk systems to ensure the healthy development of smart city information security. Technical threats are the most difficult security factor to control in threat source identification; in many incidents, internal system operations have compromised the integrity, confidentiality, and availability of information systems.

[0145] Vulnerability: Vulnerability primarily considers the possibility of attacks targeting smart city information systems in the context of big data, where vulnerabilities can be exploited. Vulnerability influencing factors are further subdivided into two secondary indicators: technical vulnerability and operational vulnerability. Further information security risk analysis is conducted to obtain tertiary indicators, as shown in the following results. Figure 4 As shown. Among them, IoT devices are the foundation of smart cities, but many important devices are vulnerable to damage due to their widespread and open distribution; the network mainly considers system vulnerabilities, defects in network components, and incorrect system configurations, and preventing these potential threats can effectively ensure the information security of smart cities; applications are numerous and varied in smart cities, many of which use open-source software, creating vulnerabilities for malicious attacks; data is constantly generated in smart cities and is crucial, but its storage, transmission, access, and encryption are prone to various vulnerabilities, leading to data theft and tampering; the physical environment mainly considers the internal and external environment surrounding the devices, supporting protective equipment, and security equipment.

[0146] Setting up operational strategies is primarily based on the consideration that, in the context of big data in smart cities, standardizing information security work and formulating secure operational strategies to achieve information security risk prevention and control is an inevitable path. Operation and maintenance technology can promote the effective implementation of information security work and ensure the stable operation of information systems. At the same time, operation and maintenance technology can implement protection responsibilities and prevent information security risks from occurring. Security operation and maintenance management is mainly considered because, with the continuous advancement of information security construction in smart cities, its importance is gradually being recognized. It mainly focuses on the daily maintenance and management of information security, and once unstable factors are discovered, reasonable measures are taken immediately.

[0147] Security measures: Security measures serve as a barrier to protect the information security of smart cities, effectively reducing the risk of security incidents, minimizing vulnerabilities, and providing technical support and management mechanisms for certain resources. The influencing factors of security measures are further subdivided into two categories of secondary indicators: preventative measures and protective measures. Further information security risk analysis is conducted on these secondary indicators to obtain tertiary indicators, as shown in the following results. Figure 5 As shown. Intrusion prevention and detection is a crucial component of information security, effectively preventing denial-of-service attacks on network infrastructure. Antivirus software is essential because network viruses have become a high-risk area threatening information security; currently, the best approach is to install powerful antivirus software. However, since viruses often precede antivirus software, the coverage of antivirus software is quite important. Patch upgrades are necessary because application software vulnerabilities are constantly emerging, leading to diverse attack methods. Updating with patches can effectively prevent information security incidents. Emergency plans are crucial because network information security incidents are often sudden and can cause significant losses; developing reasonable information security emergency plans can effectively reduce information security risks and harms.

[0148] Threat identification primarily considers training automatic identification models using basic public information security knowledge and professional information security expertise to enhance the information security risk identification capabilities of smart cities. The operational status detection mechanism addresses the need for an orderly, standardized, and unified operational status detection mechanism for the healthy and efficient operation of smart cities. Access control is crucial because numerous open application interfaces in the system create conditions for unauthorized access; restricting user permissions controls information security risks and ensures information is not illegally accessed. Identity authentication is also an effective information security risk prevention measure; by identifying the visitor's identity, the types of resources they can access are determined, preventing access to information outside their authorized scope, and facilitating the tracking of stolen information. Data encryption and auditing are primarily considered in the context of big data; encrypting data effectively prevents information from being spied on and can guarantee data integrity to a certain extent. Data backup and recovery are critical for ensuring data security; when a system malfunctions or data is lost, the system can be immediately restored to its original state.

[0149] An Evolutionary Game Theory Framework for Information Security Resource Allocation in Smart Cities

[0150] The basic framework, for example, is that with the continuous development and progress of new technologies such as artificial intelligence, big data, the Internet of Things, cloud computing, and virtual reality, the development and construction of smart cities are constantly being realized, but they also face great threats and challenges in areas such as information security. To effectively address these threats and challenges, based on a full understanding of the factors influencing the allocation of information security resources, this paper utilizes the currently popular evolutionary game theory to construct a reasonable and effective theoretical framework for information security resource allocation, enabling it to play its due role in ensuring information security. Through the analysis of the influencing factor indicator system, it can be seen that in smart cities, hardware and software, data, networks, applications, the external environment, and management are common aspects that all influencing factors need to address. For a city within a city, one issue to consider is how to plan limited resources effectively, avoid the limitations of the aforementioned influencing factors, and maximize the efficiency of all resources to ensure better information security. For a city that interacts with the outside world, considering all resources within the city as a whole, some external resources can complement internal resources, some can be substituted for each other, and some have weak correlations. How to rationally allocate these resources to improve the effectiveness of urban information security is another issue that needs to be considered. In summary, the information security resource allocation problem for a smart city involves analyzing how to allocate resources both internally and externally. Based on game theory, this application derives its information security resource allocation framework, as follows: Figure 6 As shown.

[0151] Allocation of resources within the city

[0152] Within a city, information security stakeholders can be categorized into service providers, users, and network management systems (or external systems, external data systems). Users can be further divided into legitimate users and illegitimate users. The relationships between these information security stakeholders are as follows: Figure 7 As shown. The service provider is the bearer of information security and the provider of data / services, and it has certain connections with both the network management system and users. The service provider provides sufficient information security guarantees to the network management system and users, preventing unauthorized users from seizing resources, and must provide normal data services to legitimate users (including the network management system). The network management system plays a certain supervisory role over the service provider's behavior through incentive and punishment measures. Users decide their asset allocation based on whether to purchase or supplement the service provider's products and services. Therefore, for the service provider, to realize its own interests, it must consider both service and economic factors when selecting and allocating various information security products and services, aiming to achieve the best results with the least output. Users are the users of information security; they can choose to legally obtain the data and services provided by the service provider, or they can choose to illegally intrude into the information system, profiting through theft, spying, or tampering. The network management system is the supervisor of information security and also one of the users; it can obtain the data and services provided by the service provider and can also supervise the service provider through incentive and punishment measures.

[0153] Allocation of complementary external resources

[0154] Regarding complementary external resources for smart cities, if a smart city is compromised by malicious users, the intrusion may not affect other smart cities. For example, if components of a certain type of equipment are manufactured by smart cities A and B, a malicious user cannot obtain the final assembly information of the equipment if they only intrude into smart city A or smart city B. Only when both smart cities A and B are compromised can all the information about the equipment be obtained. This increases the difficulty of intrusion and thus protects information security to a certain extent. However, in reality, information security-related companies between the two smart cities may be unwilling to cooperate. Therefore, for complementary external resources for smart cities, resource allocation needs to be considered in both non-cooperative and fully cooperative scenarios. Furthermore, incentive agreements can be signed when companies cooperate, meaning that if a company in smart city A is attacked, resulting in the impact on company in smart city B, then company in smart city A must provide corresponding data resources to company in smart city B. The relationship between information security entities of complementary external resources for smart cities is as follows: Figure 8 As shown.

[0155] Alternative configuration of external resources

[0156] For smart cities to have substitutable external resources, if an unauthorized user successfully intrudes into smart city A, but the incremental gains from intruding into smart city B are significantly less than the costs, then the resources in smart cities A and B are substitutable. For substitutable external resources, an unauthorized user can obtain the required resources in either smart city and immediately cease the attack after obtaining the resources. Conversely, if the attack fails in smart city A, the unauthorized user may continue to attack smart city B. For example, smart city A is the production location of a certain device, and smart city B is the sales location of the same device. Smart cities A and B are connected by a network. A can query information such as the device's inventory, sales volume, and unit price in B. If an unauthorized user wants to obtain this information, they can achieve their goal by attacking either A or B. The relationship between the information security subjects regarding substitutable external resources in smart cities is as follows: Figure 9 As shown.

[0157] Configuration of weakly related external resources

[0158] For weakly interconnected external resources in smart cities, information security benefits are primarily achieved through information sharing, allowing smart cities to relatively reduce investment. For two smart cities, A and B, if information sharing is chosen, unauthorized user information, system vulnerabilities, and patch upgrades will be mutually accessible, enabling relevant service providers to prepare in advance. If information sharing is not chosen, it's equivalent to each smart city having to undertake information security construction alone, thus reverting to a problem of resource allocation within the smart city itself.

[0159] Figure 11 This is a flowchart of a method for resource allocation in smart cities based on data stability according to an embodiment of the present invention. Method 1100 includes: step 1101 determining a set of smart cities consisting of multiple smart cities with weakly correlated resources, wherein a weakly correlated resource is defined as the ratio of the amount of correlated data resources between any two smart cities in the set to the total amount of data resources of the two smart cities being less than or equal to a correlation threshold;

[0160] Step 1102 determines the initial data security benefit E obtained by each smart city based on information security configuration when each smart city does not share related data resources. i And the resource provision coefficient τ, which determines the external systems of the smart city set that can provide data resources to the smart cities in the smart city set;

[0161] Step 1103 determines the resource loss cost L caused by unauthorized intrusion during resource sharing when each smart city shares related data resources, resulting in data leakage. i and loss cost coefficient μ iAnd determine the data spillover coefficient ω when each smart city shares related data resources;

[0162] Step 1104: Randomly select a first smart city and a second smart city from the set of smart cities, and combine the first smart city and the second smart city into a data sharing system; and

[0163] Step 1105: Determine local stability based on the equilibrium point of the data sharing system, determine the data stability of the sharing system based on the local stability, and configure the shared resources of the first smart city and the second smart city within the data sharing system based on the data stability.

[0164] Figure 12 This is a flowchart of a method for allocating resources in a network system based on data stability according to an embodiment of the present invention. Method 1200 includes: step 1201 determining a set of network systems consisting of multiple network systems that have weakly correlated resources with each other, wherein a weakly correlated resource is defined as the ratio of the amount of correlated data resources between any two network systems in the set to the total amount of data resources of the two network systems being less than or equal to a correlation threshold;

[0165] Step 1202 determines the initial data security benefit E obtained by each network system based on information security configuration when each network system does not share associated data resources. i , and the resource provision coefficient τ that determines the external systems of the network system set can provide data resources to the network systems in the network system set;

[0166] Step 1203 determines the resource loss cost L caused by unauthorized intrusion during resource sharing when each network system shares related data resources, resulting in data leakage. i and loss cost coefficient μ i And determine the data spillover coefficient ω when each network system shares related data resources;

[0167] Step 1204: Randomly select a first network system and a second network system from the set of network systems, and combine the first network system and the second network system into a data sharing system; and

[0168] Step 1205: Determine local stability based on the equilibrium point of the data sharing system, determine the data stability of the sharing system based on the local stability, and configure the shared resources of the first network system and the second network system within the data sharing system based on the data stability.

[0169] In one embodiment, the association threshold is 5%, 10%, 15%, or 20%.

[0170] In one embodiment, the system further includes the first smart city or network system being smart city or network system i, and the second smart city or network system being smart city or network system j.

[0171] Scenario 1: When both smart city or network system i and smart city or network system j choose to share information, determine the data security benefit S for smart city or network system i and smart city or network system j. i1 and S j1 for:

[0172] S i1 =E i +ωL j +τL i -μ i L i (6.1)

[0173] S j1 =E j +ωL i +τL j -μ j L j (6.2)

[0174] Scenario 2: When smart city or network system i chooses to share information, while smart city or network system j chooses not to share information, determine the data security benefits S for smart city or network system i and smart city or network system j. i2 and S j2 for:

[0175] S i2 =E i +τL i -μ i L i (6.3)

[0176] S j2 =E j +L i (6.4)

[0177] Scenario 3: When smart city or network system i chooses not to share information, while smart city or network system j chooses to share information, determine the data security benefits S for smart city or network system i and smart city or network system j. i3 and S j3 for:

[0178] S i3 =E i +L j (6.5)

[0179] Sj3 =E j +τL j -μ j L j (6.6)

[0180] Scenario 4: When both smart city or network system i and smart city or network system j choose not to share information, determine the data security S of smart city or network system i and smart city or network system j. i4 and S j4 for:

[0181] S i4 =E i (6.7)

[0182] S j4 =E j (6.8)

[0183] Among them, S i1 For the first scenario, the data security benefits of the smart city or network system i; S j1 For the first scenario, the data security benefits of the smart city or network system j; S i2 In the second scenario, the data security benefits of the smart city or network system i; S j2 In the second scenario, the data security benefits of the smart city or network system j; S i3 For the third scenario, the data security benefits of the smart city or network system i; S j3 In the third scenario, the data security benefits of the smart city or network system j; S i4 For the fourth scenario, the data security benefits of smart city or network system i; S j4 For the fourth scenario, the data security benefits of smart city or network system j; E i E represents the initial data security benefits obtained by the i-th smart city or network system based on information security configuration; j L represents the initial data security benefits obtained by the j-th smart city or network system based on information security configuration; i L represents the resource loss cost of the i-th smart city or network system due to unauthorized intrusion during resource sharing, resulting in data leakage; j Let τ be the resource loss cost of the j-th smart city or network system due to unauthorized intrusion during resource sharing; τL be the resource provision coefficient of the external systems of the smart city or network system set that can provide data resources to the smart cities or network systems in the set; i Let τL be the amount of data resources that the i-th smart city or network system can obtain from external systems within the set of smart cities or network systems;j μ represents the amount of data resources that the j-th smart city or network system can obtain from external systems within the set of smart cities or network systems. i Let μ be the cost coefficient for the data leakage caused by unauthorized intrusion during resource sharing in the i-th smart city or network system. j Let ω be the loss cost coefficient of the j-th smart city or network system due to unauthorized intrusion during resource sharing; ω is the effect coefficient of the data spillover effect when the smart city or network system chooses to share information, and ω≥1; ωL i If both city i and city j choose the sharing strategy, then city j can obtain data security benefits from sharing with city i, ωL j If both city i and city j choose the sharing strategy, then city i can obtain data security benefits through sharing with city j.

[0184] In one embodiment, when S is determined ik (k = 1, 2, ..., 4) is the data acquisition function of the i-th smart city or network system, and S jk When (k = 1, 2, ..., 4) is the data acquisition function for the j-th city, then:

[0185] (1) 0 ≤ S ik ≤T;

[0186] (2) 0 ≤ S jk ≤T′;

[0187] (3) The effect coefficient ω and the resource provision coefficient τ, for the data acquisition function S ik and S jk All are monotonically increasing, and the loss cost coefficient μ is related to the data acquisition function S. ik and S jk All are monotonically decreasing.

[0188] In one embodiment, for the i-th smart city or network system, when acting as a data resource provider, if the proportion of information sharing is θ, then the proportion of information sharing is 1-θ.

[0189] For the j-th smart city or network system, when acting as a data resource demander, the proportion of information sharing chosen is: The proportion of those who choose not to share information is: but

[0190] In one embodiment, the expected benefit of data security for the i-th smart city or network system, whether it chooses to share information or not, is:

[0191] In the case of information sharing, the expected benefit S of data security is for:

[0192]

[0193] Without information sharing, the expected benefit S of data security in for:

[0194]

[0195] The expected data security benefit for the j-th smart city or network system, considering both information sharing and non-information sharing, is:

[0196] When information is shared, the expected benefits of data security are:

[0197] S js =θS j1 +(1-θ)S j3

[0198] =θ(E j +ωL i +τL j -μ j L j )+(1-θ)(E j +τL j -μ j L j )

[0199] =θωL i +E j +τL j -μ j L j (6.11)

[0200] Without information sharing, the expected benefits of data security are:

[0201] S jn =θS j2 +(1-θ)S j4

[0202] =θ(E j +L i )+(1-θ)E j

[0203] =θL i +E j (6.12)

[0204] In one embodiment, the method further includes determining the overall expected benefit of data security for the i-th smart city or network system and the j-th smart city or network system as follows:

[0205]

[0206]

[0207] The replication dynamic equations for the i-th and j-th smart city or network systems that share information are expressed as follows:

[0208]

[0209]

[0210] In one embodiment, the system further includes the ability to determine the equilibrium point of the data sharing system or the replica dynamic system when the replica dynamic equations (6.15) and (6.16) are equal to zero. Therefore, based on the replicating dynamic equations (6.15) and (6.16), the following can be determined:

[0211] (1) The following four points are candidate evolutionary equilibrium points: O(0,0), A(1,0), B(0,1), C(1,1);

[0212] (2 points) It is also a candidate evolutionary equilibrium point, among which and

[0213] Based on the stability requirements of the replicating dynamic system and the differential equation, the equilibrium point of the data sharing system or replicating dynamic system must meet the following conditions:

[0214]

[0215] Substituting formulas (6.15) and (6.16) into formula (6.17), we obtain the following result:

[0216]

[0217] In one embodiment, the method further includes determining local stability based on the Jacobian matrix J of the data sharing system or the replica dynamic system, thereby determining a stability strategy for the data sharing system or the replica dynamic system.

[0218]

[0219] in,

[0220]

[0221]

[0222]

[0223]

[0224] Determining the equilibrium point and stability of an evolutionary game using the properties of the Jacobian matrix includes: using the values ​​of the determinant det(J) and trace tr(J) to determine the equilibrium point and stability of the evolutionary game. If the determinant tr(J) is greater than zero and the trace tr(J) is less than zero, then the equilibrium point of the data sharing system or the replicating dynamic system is locally stable. This equilibrium point is then chosen as the evolutionary equilibrium point of the data sharing system or the replicating dynamic system, resulting in the following formula:

[0225] Determinant:

[0226] trace:

[0227] In one embodiment, it also includes;

[0228] when or At that time, the evolutionary equilibrium point is C(1,1);

[0229] Both the i-th smart city or network system and the j-th smart city or network system choose to share information (information sharing is also known as data resource sharing, data sharing, or resource sharing).

[0230] In one embodiment, it also includes, when At that time, the evolutionary equilibrium point is determined to be A(1,0);

[0231] The i-th smart city or network system chooses to share information, while the j-th smart city or network system chooses not to share information.

[0232] In one embodiment, it also includes, when At that time, the evolutionary equilibrium points are determined to be O(0,0) and C(1,1);

[0233] When both the i-th smart city or network system and the j-th smart city or network system choose to share information, the external system of the smart city or network system set provides the first amount of data resources to the smart city or network system in the smart city or network system set.

[0234] When both the i-th smart city or network system and the j-th smart city or network system choose not to share information, the external system of the smart city or network system set provides the second amount of data resources to the smart cities or network systems in the smart city or network system set.

[0235] The second quantity is greater than the first quantity.

[0236] In one embodiment, it also includes, when or At that time, the evolutionary equilibrium point is determined to be O(0,0);

[0237] Both the i-th and j-th smart city or network systems choose not to share information, and the external systems of the smart city or network system set provide a third amount of data resources to the smart cities or network systems in the smart city or network system set.

[0238] Smart city or network system information security weak correlation external resource allocation method

[0239] Modeling based on technical problems

[0240] Problem Description: The information security resource allocation and information sharing mechanism of smart cities or network systems are essentially strategically complementary. Smart cities or network systems can reduce their own information security resource allocation (or resource quantity) through information sharing, thereby freeing up resources for other core information areas. Conversely, smart cities or network systems can improve their information security level by increasing their information security resource allocation. Optimized resource allocation and information sharing can improve the return on investment of resource allocation in smart cities or network systems, thus promoting the sharing mechanism among smart city or network system groups. The purpose of building an information security vulnerability sharing platform is to enable external systems or network management systems to participate in information security maintenance and construction, integrating the information resources of each entity to address information security challenges and elevate the level of information security to a higher level.

[0241] Most current improvements in information security focus on strongly correlated scenarios such as complementary or substitutable external resources. Research on weakly correlated scenarios is limited. Therefore, how to achieve secure information sharing between smart cities or network systems using appropriate platforms is a crucial consideration. Furthermore, while secure information sharing between smart cities or network systems is highly beneficial, it doesn't necessarily mean they are willing to proactively allocate resources. A "prisoner's dilemma" arises during implementation: a smart city or network system can choose to allocate resources or not, but if other smart cities or network systems allocate resources while the system that didn't can still enjoy the security benefits of information sharing, it will inevitably hinder their motivation to allocate information security resources. Therefore, it is essential to comprehensively consider various factors, including the resource allocation strategies of each smart city or network system and the evolving game of information sharing, to improve the efficiency and level of information security sharing.

[0242] Given the characteristics of weakly correlated external resources, traditional information security resource allocation strategies are clearly unsuitable. Furthermore, the allocation of information security resources among smart cities or network systems is not a one-time investment but rather a continuous adjustment based on factors such as the resource allocation of other smart cities or network systems and the intrusion status of unauthorized users. This aligns well with evolutionary game theory, as it reflects the dynamic changes of smart cities or network systems while simultaneously addressing resource allocation issues. Therefore, this application considers using an evolutionary game theory model to establish a model for the information security resource allocation of smart city or network system clusters. Based on its characteristics, it explores the model's equilibrium point and conducts stability analysis, further analyzing the evolutionary paths of smart cities or network systems under different factors such as sharing costs, spillover benefits, and information security enhancement.

[0243] Problem modeling:

[0244] For weakly correlated external resources, if smart cities or network systems do not share information, each smart city or network system configures its information security resources independently, transforming the model into a resource allocation problem within the smart city or network system. If information sharing is implemented, smart cities or network systems share information such as illegal user information, system vulnerabilities, and virus information through an information security sharing platform. In this case, the game process can be understood as a symmetrical game, where smart city or network system A is the supplier and smart city or network system B is the demand side, and both parties can choose between sharing and not sharing strategies.

[0245] Assumption 1: If a smart city or network system i chooses not to implement a sharing strategy, its initial data security benefit (or data security benefit amount) in its own information security resource configuration is E. i .

[0246] Assumption 2: When a smart city or network system i chooses a sharing strategy, the smart city or network system i faces certain costs associated with the exposure of secure information (or the resource loss costs due to data leakage), denoted as L. i Its shared cost coefficient (or data leakage loss cost coefficient) is denoted as μ. i .

[0247] Assumption 3: When a smart city or network system i selects a sharing strategy, there will be a certain information spillover effect. This spillover effect (or data spillover effect) is denoted as ω-1, where ω is the effect coefficient (the effect coefficient of the data spillover effect), and ω≥1.

[0248] Assumption 3 states that information security resource allocation and information sharing are complementary. Smart cities or network systems can improve their information security levels by increasing the amount of information security resources allocated (resource allocation quantity) or by strengthening information security sharing. In the case of information security sharing, an information security spillover effect will occur. For example, if both smart city or network system i and smart city or network system j choose a sharing strategy, then smart city or network system i can obtain ωL through the sharing of smart city or network system j. j Data security benefits.

[0249] As can be seen from the problem description, the construction of an information sharing platform requires the participation of external system or network management system departments. Therefore, it is necessary to consider the role of external systems or network management systems in information construction, and to introduce the data resource supplementation, input, or improvement of external systems or network management systems into the game model. Assuming the resource provision coefficient (or data resource supplementation coefficient, data resource provision coefficient, resource provision coefficient, resource supplementation coefficient) of external systems or network management systems is τ, then the data security benefit that smart city or network system i can obtain from external systems or network management systems is τL. i .

[0250] Since each smart city or network system can have different strategies, namely information sharing and non-information sharing, a data security benefit matrix under different strategies can be established first, as follows:

[0251] Scenario 1: Both smart city or network system i and smart city or network system j choose to share information.

[0252] Based on the above assumptions, the data security benefits S of smart city or network system i and smart city or network system j can be calculated. i1 S j1 as follows:

[0253] S i1 =E i +ωL j +τL i -μ i L i (6.1)

[0254] S j1 =E j +ωL i +τL j -μ j L j (6.2)

[0255] Scenario 2: Smart city or network system i chooses to share information, while smart city or network system j chooses not to. Based on the above assumptions, the data security benefits S for smart city or network system i and smart city or network system j can be calculated. i2 S j2 as follows:

[0256] S i2 =E i +τL i -μ i L i (6.3)

[0257] S j2 =E j +L i (6.4)

[0258] Scenario 3: Smart city or network system i chooses not to share information, while smart city or network system j chooses to share information.

[0259] Based on the above assumptions, the data security benefits S of smart city or network system i and smart city or network system j can be calculated. i3 S j3 as follows:

[0260] S i3 =E i +L j (6.5)

[0261] S j3 =E j +τL j -μ j L j (6.6)

[0262] Scenario 4: Both smart city or network system i and smart city or network system j choose not to share information.

[0263] Based on the above assumptions, the data security benefits S of smart city or network system i and smart city or network system j can be calculated. i4 S j4 as follows:

[0264] S i4 =E i (6.7)

[0265] S j4 =E j (6.8)

[0266] Based on the above four situations, we can draw the following conclusion 1.

[0267] Conclusion 1: Assume S ik (k = 1, 2, ..., 4) is the data acquisition function (or payment function) of the smart city or network system i, S jk If (k = 1, 2, ..., 4) is the data acquisition function of the smart city or network system j, then:

[0268] (1) 0 ≤ S ik ≤T;

[0269] (2) 0 ≤ S jk ≤T′;

[0270] (3) For the effect coefficient (the effect coefficient of data spillover effect) ω and the resource provision coefficient τ, their relationship with the data acquisition function S ik and S jk All are monotonically increasing, while for the shared cost coefficient μ i Its data acquisition function S ik and S jk All are monotonically decreasing.

[0271] Conclusion 1 shows that if both smart city or network system i and smart city or network system j choose to share information, their expected benefits (expected information security benefits) will increase with the increase of the effect coefficient and resource provision coefficient, and decrease with the increase of sharing costs. The main reason for this is that increasing the spillover effect and resource provision coefficient can effectively improve the information security benefits of smart city or network systems, thereby further promoting information sharing. Information sharing can not only fuse discrete information, but also provide new information sources for each smart city or network system, thus promoting the generation of sharing spillover effects.

[0272] Formulas (6.1) to (6.8) are compiled into a matrix, as shown in Table 6.1. This table is a data security benefit matrix for each smart city or network system under different strategies.

[0273] Table 6.1 Data security benefit matrix under different strategies

[0274]

[0275] If for smart city or network system i, the proportion of its users choosing information sharing as a supplier is θ, then the proportion choosing not to share information is 1-θ; for smart city or network system j, the proportion of its users choosing information sharing as a demand side is... The proportion of those who choose not to share information is: but

[0276] Based on the above assumptions, the expected data security benefits of a smart city or network system i under the strategies of choosing to share information and not sharing information can be obtained as follows.

[0277] The expected data security benefits under information sharing scenarios include:

[0278]

[0279] The expected data security benefits without information sharing include:

[0280]

[0281] Similarly, the expected data security benefits for a smart city or network system j under the strategies of choosing to share information and not sharing information are as follows.

[0282] The expected data security benefits under information sharing scenarios include:

[0283] S js =θS j1 +(1-θ)S j3

[0284] =θ(E j +ωL i +τL j -μ j L j )+(1-θ)(E j +τL j -μ j L j )

[0285] =θωL i +E j +τL j -μ j L j (6.11)

[0286] The expected data security benefits without information sharing include:

[0287] S jn =θS j2 +(1-θ)S j4

[0288] =θ(E j +L i )+(1-θ)E j

[0289] =θL i +E j (6.12)

[0290] Based on the above, the overall expected data security benefits for smart city or network system i and smart city or network system j can be obtained as follows.

[0291]

[0292]

[0293] Based on the above results, the dynamic equations for information sharing in smart cities or network systems and the replication dynamic equations of smart cities or network systems are expressed as follows:

[0294]

[0295]

[0296] Equilibrium point and stability analysis:

[0297] According to problem modeling and the theory of replicated dynamic systems, setting equations (6.15) and (6.16) equal to zero yields the equilibrium point of the system. Therefore, we can draw the following conclusions.

[0298] Conclusion 2: According to the dynamic equation formulas (6.15) and (6.16), we have:

[0299] (1) The following four points are possible evolutionary equilibrium points: O(0,0), A(1,0), B(0,1), C(1,1);

[0300] (2) At point This is also an equilibrium point, where and

[0301] According to the theory of replicating dynamic systems and the stability requirements of differential equations, the equilibrium point of the system satisfies the following conditions:

[0302]

[0303] Substituting formulas (6.15) and (6.16) into formula (6.17) yields the following result:

[0304]

[0305] This proves that all five equilibrium points mentioned in Conclusion 2 are correct, namely O(0,0), A(1,0), B(0,1), C(1,1), and...

[0306] While the above solution process satisfies the stability requirements of the replica dynamics system theory and differential equations, the obtained equilibrium point is not necessarily the stable strategy of the system's evolution. According to evolutionary game theory, the stable strategy can be determined by the local stability analysis of the system's Jacobian matrix J:

[0307]

[0308] In the formula,

[0309]

[0310]

[0311]

[0312]

[0313] The properties of the Jacobian matrix can be used to evaluate the equilibrium point and stability of an evolutionary game system (mainly by using the signs of its determinant det(J) and trace tr(J)). If its determinant tr(J) is greater than zero and its trace tr(J) is less than zero, then the equilibrium point of the system is locally stable, and this point can be chosen as the evolutionary equilibrium point of the system, thus yielding the following formula:

[0314] Determinant:

[0315] trace:

[0316] In summary, the replication dynamic system can be evaluated using formulas (6.15) to (6.21), and the evaluation process may involve the following four scenarios:

[0317] Scenario 1: or

[0318] This scenario primarily addresses situations where the information sharing costs of both smart city or network system i and smart city or network system j are relatively low, and lower than the resource provision coefficients of external systems or network management systems. In such cases, all smart city or network systems would choose to share information. Alternatively, for smart city or network system j, the cost of information sharing might be higher than the resource provision coefficient of external systems or network management systems, but lower than the sum of the resource provision coefficient and the sharing spillover effect—the latter scenario (Scenario 1). In this case, the smart city or network system can still obtain information security benefits, and it will ultimately choose to share information as well.

[0319] Further analysis of the above situation yields the results of the discussion on the local stability of the equilibrium point, as shown in Table 6.2. Figure 13a This represents the corresponding dynamic evolution phase diagram.

[0320] Table 6.2 Results of the investigation into the local stability of the equilibrium point in Case 1

[0321]

[0322] Scenario 2:

[0323] This scenario primarily occurs when the sharing cost of a smart city or network system j is higher than the sum of the resource provision coefficient and the sharing spillover effect of external systems or network management systems. If this situation arises, the system's evolutionary equilibrium point is determined to be A(1,0). In this case, the smart city or network system j will choose not to share information, resulting in the smart city or network system borrowing data resources from other smart cities or network systems. Other equilibrium points are unstable. If this situation exists in the system, smart cities or network systems with low sharing costs will generally choose to share information, while those with high costs will choose not to share information and instead rely on the information infrastructure of other smart cities or network systems to obtain data security benefits.

[0324] Further analysis of the above situation yields the local stability results of the equilibrium point, as shown in Table 6.3. Figure 13b This represents the corresponding dynamic evolution phase diagram.

[0325] Table 6.3 Results of the investigation into the local stability of the equilibrium point in Case 2

[0326]

[0327] Scenario 3:

[0328] This scenario mainly applies to situations where the information sharing costs of smart city or network system i and smart city or network system j are both higher than the resource provision coefficient of external system or network management system, but lower than the sum of the resource provision coefficient and sharing spillover effect of external system or network management system. If this scenario occurs, the evolutionary equilibrium points of the system are O(0,0) and C(1,1).

[0329] Further analysis of the above situation yields the results of the discussion on the local stability of the equilibrium point, as shown in Table 6.4. Figure 13c This represents the corresponding dynamic evolution phase diagram.

[0330] Table 6.4 Results of the investigation into the local stability of the equilibrium point in Case 3

[0331]

[0332] In this scenario, the system boundary line BDA is composed of two unstable points (A, B) and a saddle point (D). If the game state of each smart city or network system in the system is in the BDAC part (i.e., in the BDAC part), then... Figure 13c When the upper right corner is selected, each smart city or network system will choose to share information; however, if the game state of each smart city or network system in the system is in the BDAO part (i.e., in the upper right corner), then the smart cities or network systems will choose to share information. Figure 13c When the area in the lower left corner is reached, the smart cities or network systems will choose not to share information. This is a highly undesirable stable state, in which case the sharing platform cannot function effectively. From the above analysis, it can be seen that saddle point D should be located as close as possible to... Figure 13c The bottom left corner is ideally the area of ​​the BDAO (Browser-Based Access Array) should be kept as small as possible to increase the probability of information sharing among smart cities or network systems. To achieve this, the information sharing cost among smart cities or network systems can be reduced, while simultaneously increasing the provision or supplementation of data resources by external systems or network management systems, and enhancing the spillover effect of data sharing.

[0333] Scenario 4: or

[0334] This scenario primarily occurs when the sharing cost of a smart city or network system j is consistently higher than the sum of the resource provision coefficient and the sharing spillover effect of an external system or network management system. In this case, regardless of whether smart city or network system j chooses to share information, its information security benefits will be less than zero, so smart city or network system j will choose not to share information. Simultaneously, because smart city or network system j does not share information, its related smart city or network system i cannot obtain the spillover effect from smart city or network system j. Therefore, even though smart city or network system i may have a sharing cost lower than the sum of the resource provision coefficient and the sharing spillover effect of an external system or network management system, smart city or network system i will still choose not to share information. If the sharing costs of both smart city or network systems are very high, and higher than the sum of the resource provision coefficient and the sharing spillover effect of an external system or network management system, then both smart city or network system i and smart city or network system j will choose not to share information.

[0335] Further analysis of the above situation yields the results of the discussion on the local stability of the equilibrium point, as shown in Table 6.5. Figure 13d This represents the corresponding dynamic evolution phase diagram.

[0336] Table 6.5 Results of the investigation into the local stability of the equilibrium point in case 4

[0337]

[0338] The results of comparing the four scenarios are shown in Table 6.6:

[0339] Table 6.6 Comparison results of the four scenarios

[0340]

[0341] Results and Analysis

[0342] As can be seen from the above, for weakly correlated external resource allocation problems, the time and outcome of the evolutionary game process are related to relevant parameters. Changing the initial value of any parameter will lead to changes in the game's time and equilibrium point. Although it is ideal for all smart cities or network systems to choose information sharing, for smart city or network system i and smart city or network system j, it is possible that both smart city or network system i and smart city or network system j share information, or it is possible that neither smart city or network system i nor smart city or network system j shares information. This is the final equilibrium result of the system's evolution, and it is not certain that all smart cities or network systems will choose to share information. The main reason for the above situation is the information sharing cost coefficient μ. i The shared spillover effect coefficient ω and the resource provision coefficient τ of the external system or network management system are the combined results. Therefore, this application sets other parameters based on specific actual conditions, and through simulation and evolution path analysis, explores the behavior of smart city or network system i and smart city or network system j in the following three cases, while evaluating the stability of the system:

[0343] 1. Through numerical simulation, compare smart city or network system i and smart city or network system j under different information sharing cost coefficients μ. i Evolutionary paths under different sharing costs, i.e., analysis of the evolutionary paths of the participating subjects' behavior;

[0344] 2. Through numerical simulation, compare the evolution paths of smart city or network system i and smart city or network system j under different shared spillover effect coefficients ω, that is, analyze the evolution path of the behavior of participating subjects under different shared spillover effects;

[0345] 3. Through numerical simulation, compare the evolution paths of smart city or network system i and smart city or network system j under different external system or network management system resource provision coefficients τ, that is, analyze the evolution path of participating subject behavior under different external system or network management system resource provision coefficients.

[0346] Analysis of the evolutionary paths of participating entities' behavior under different sharing costs:

[0347] Information security sharing costs mainly include: information security technology costs, information security human resource costs, and risk costs caused by information security leaks

[60] . For the convenience of research, information security sharing costs are divided into three levels: high cost, medium cost, and low cost, with values ​​of 1.9, 0.9, and 0.1, respectively. Assuming the spillover effect coefficient ω = 1.5, the resource provision coefficient of the external system or network management system τ = 0.6, and the sharing cost L of the smart city or network system i due to sharing, i =2, the sharing cost L resulting from sharing in a smart city or network system j. j =4. To reduce the influence of other parameters, when considering the evolution path of smart city or network system i, the relevant parameters of smart city or network system j are fixed, μ j =0.1.

[0348] Based on formulas (6.15) and (6.16) obtained in Section 6.1.2, the two-dimensional dynamical system of this system can be obtained, namely:

[0349]

[0350] Integrating formula (6.22) yields:

[0351]

[0352] Based on the above assumptions and formula (6.23), the evolution paths of smart city or network system i and smart city or network system j under different security sharing cost levels can be obtained, as shown in the figure. Figure 14a , Figure 14b As shown.

[0353] Figure 14a , Figure 14b The curves represent the information sharing evolution paths of smart city or network systems i and j under different cost coefficients, from... Figure 14a , Figure 14b As can be seen, the probability of information sharing in smart cities or network systems decreases as the cost coefficient increases. When it reaches a certain level, smart cities or network systems will choose not to share information, meaning that the use of data resources from other smart cities or network systems will occur. However, for systems like μ... i =0.1 or μ jSmart cities or network systems with a low information sharing cost of 0.1 will choose to share information. The lower the sharing cost, the faster the smart city or network system will choose to share information. Conversely, the higher the sharing cost, the faster the smart city or network system will choose not to share information. Therefore, low sharing cost can effectively accelerate the evolution of smart cities or network systems towards a proactive cooperative approach.

[0354] Analysis of the evolutionary paths of participating subjects' behavior under different sharing spillover effects

[0355] Spillover effects refer to the phenomenon where engaging in the same type of activity and introducing other beneficial effects into that activity to generate more revenue. This has a significant impact on organizational economic growth. Influencing factors mainly include spatial distance, the learner's learning ability, and knowledge gaps. To better determine the impact of spillover effects on the evolutionary path of agent behavior, it is divided into three levels: high effect, medium effect, and low effect, with values ​​of 1.9, 1.5, and 1.1 respectively. It is assumed that the shared cost coefficient of smart cities or network systems is the same, μ... i =μ j =0.9, the resource provision coefficient τ of the external system or network management system is 0.6, and the sharing cost L of the smart city or network system i due to sharing is 0.6. i =2, the sharing cost L resulting from sharing in a smart city or network system j. j =1.

[0356] Based on the above assumptions and formula (6.23), the evolution paths of smart city or network system i and smart city or network system j under different spillover effect levels can be obtained, as shown in the figure. Figure 14c , Figure 14d As shown.

[0357] Figure 14c , Figure 14d The middle curves represent the information sharing evolution paths of smart city or network systems i and j under different spillover effects, from... Figure 14c , Figure 14d It can be seen that the probability of information sharing in smart cities or network systems increases with the increase of spillover effects, and decreases conversely with the decrease of spillover effects. When it decreases to a certain level, smart cities or network systems will choose not to share information. The increase in spillover effects can effectively promote information sharing in smart cities or network systems. Furthermore, information sharing not only integrates and organizes scattered and different information, but also provides a foundation for each smart city or network system to absorb new information, thereby further promoting the increase of spillover effects. Figure 14c and Figure 14dIn comparison, under the same spillover effect, the information sharing willingness of smart city or network system i changes more than that of smart city or network system j. This phenomenon is mainly due to the impact of information sharing costs. As can be seen from the above, the lower the information sharing cost, the greater the willingness of smart city or network system to cooperate, and thus the faster the change in its willingness to cooperate.

[0358] Analysis of the evolution path of participating entities' behavior under the resource provision coefficients of different external systems or network management systems:

[0359] To reduce the gap between information sharing costs and spillover effects, resources from external systems or network management systems can be introduced to address this issue. After introducing these resources, the system must ensure the information security of the smart city or network system cluster and its legitimate users. This necessitates the construction of an information sharing platform to promote information sharing among smart city or network systems. To better study the impact of the resource provision coefficient of external systems or network management systems on the evolution path of subject behavior, it is divided into three levels: high support, medium support, and low support, with values ​​of 0.9, 0.6, and 0.4, respectively. It is assumed that the sharing cost coefficient of the smart city or network system is the same, μ... i =μ j =0.5, spillover effect coefficient ω=1.5, sharing cost L caused by sharing in a smart city or network system i. i =2, the sharing cost L resulting from sharing in a smart city or network system j. j =4.

[0360] Based on the above assumptions and formula (6.23), the evolution paths of smart city or network system i and smart city or network system j under different levels of resource provision coefficients of external systems or network management systems can be obtained, as shown in the figure. Figure 14e , Figure 14f As shown.

[0361] Figure 14e , Figure 14f The curves represent the information sharing evolution paths of smart city or network systems i and j under different external systems or network management system resource provision coefficients, from... Figure 14e and Figure 14fAs can be seen, the information sharing level of smart cities or network systems increases with the increased resource provision or supplementation from external systems or network management systems. This is mainly because the information sharing cost of smart cities or network systems decreases continuously with the resource provision or supplementation from external systems or network management systems, thus incentivizing smart cities or network systems to choose information sharing. However, comparing the change in the resource provision coefficient from 0.4 to 0.6 with the change from 0.6 to 0.9, we can find that the former shows a larger change than the latter. This is because excessive resource provision or supplementation from external systems or network management systems can create a "crowding-out effect" on the information security of smart cities or network systems, thus affecting their marginal benefits and consequently the probability of information sharing. Figure 14e and Figure 14f A comparison reveals that the smaller the resource provision coefficient, the more sensitive the smart city or network system is to information sharing. That is, with the same funding support, a smart city or network system with a lower resource provision coefficient will take less time to choose information sharing than one with a higher coefficient. This is because the smart city or network system benefits from resource provision or supplementation by external systems or network management systems, reducing its sharing costs. As mentioned above, lower sharing costs increase the probability of a smart city or network system choosing information sharing, thus greatly stimulating its willingness to do so.

[0362] Figure 15 This is a schematic diagram of a system for resource allocation in smart cities based on data stability according to an embodiment of the present invention. The system 1500 includes: a first determining device 1501, used to determine a set of smart cities consisting of multiple smart cities with weakly correlated resources, wherein a weakly correlated resource is defined as the ratio of the amount of correlated data resources between any two smart cities in the set to the total amount of data resources of the two smart cities being less than or equal to a correlation threshold;

[0363] The second determining device 1502 is used to determine the initial data security benefit E obtained by each smart city based on information security configuration when each smart city does not share associated data resources. i And the resource provision coefficient τ, which determines the external systems of the smart city set that can provide data resources to the smart cities in the smart city set;

[0364] The third determining device 1503 is used to determine the resource loss cost L caused by data leakage due to illegal intrusion during resource sharing when each smart city shares related data resources. i and loss cost coefficient μ iAnd determine the data spillover coefficient ω when each smart city shares related data resources;

[0365] The constitutive device 1504 is used to randomly select a first smart city and a second smart city from the set of smart cities, and to form a data sharing system between the first smart city and the second smart city.

[0366] The configuration device 1505 is used to determine local stability based on the equilibrium point of the data sharing system, and to determine the data stability of the sharing system based on the local stability, and to configure the shared resources of the first smart city and the second smart city in the data sharing system based on the data stability.

[0367] Figure 16 This is a schematic diagram of a system for allocating resources to a network system based on data stability according to an embodiment of the present invention. The system 1600 includes: a first determining device 1601, used to determine a set of network systems consisting of multiple network systems that have weakly correlated resources with each other, wherein a weakly correlated resource is defined as the ratio of the amount of correlated data resources between any two network systems in the set to the total amount of data resources of the two network systems being less than or equal to a correlation threshold;

[0368] The second determining device 1602 is used to determine the initial data security benefit E obtained by each network system based on information security configuration when each network system does not share associated data resources. i , and the resource provision coefficient τ that determines the external systems of the network system set can provide data resources to the network systems in the network system set;

[0369] The third determining device 1603 is used to determine the resource loss cost L caused by unauthorized intrusion during resource sharing when each network system shares associated data resources. i and loss cost coefficient μ i And determine the data spillover coefficient ω when each network system shares related data resources;

[0370] The constitutive device 1604 is used to randomly select a first network system and a second network system from the set of network systems, and to constitut the first network system and the second network system into a data sharing system.

[0371] The configuration device 1605 is used to determine local stability based on the equilibrium point of the data sharing system, determine the data stability of the sharing system based on the local stability, and configure the shared resources of the first network system and the second network system within the data sharing system based on the data stability.

[0372] In one embodiment, the association threshold is 5%, 10%, 15%, or 20%.

[0373] In one embodiment, the first smart city is smart city i, and the second smart city is smart city j;

[0374] The second determining device 1602 is specifically used for,

[0375] Scenario 1: When both smart city i and smart city j choose to share information, determine the data security benefits S for smart city i and smart city j. i1 and S j1 for:

[0376] S i1 =E i +ωL j +τL i -μ i L i (6.1)

[0377] S j1 =E j +ωL i +τL j -μ j L j (6.2)

[0378] Scenario 2: When smart city i chooses to share information, while smart city j chooses not to, determine the data security benefits S for smart city i and smart city j. i2 and S j2 for:

[0379] S i2 =E i +τL i -μ i L i (6.3)

[0380] S j2 =E j +L i (6.4)

[0381] Scenario 3: When smart city i chooses not to share information, while smart city j chooses to share information, determine the data security benefits S for smart city i and smart city j. i3 and S j3 for:

[0382] S i3 =E i +L j (6.5)

[0383] S j3 =E j +τL j -μ j L j (6.6)

[0384] Scenario 4: When both smart city i and smart city j choose not to share information, determine the data security S of smart city i and smart city j. i4 and S j4 for:

[0385] S i4 =E i (6.7)

[0386] S j4 =E j (6.8)

[0387] Among them, S i1 For the first scenario, the data security benefits of smart city i;

[0388] S j1 For the first scenario, the data security benefits of smart city j;

[0389] S i2 In the second scenario, the data security benefits of smart city i;

[0390] S j2 For the second scenario, the data security benefits of smart city j;

[0391] S i3 For the third scenario, the data security benefits of smart city i;

[0392] S j3 For the third scenario, the data security benefits of smart city j;

[0393] S i4 For the fourth scenario, the data security benefits of smart city i;

[0394] S j4 For the fourth scenario, the data security benefits of smart city j;

[0395] E i The initial data security benefits obtained by the i-th smart city based on information security configuration;

[0396] E j The initial data security benefits obtained by the j-th smart city based on information security configuration;

[0397] L iLet be the resource loss cost of the i-th smart city due to data leakage caused by illegal intrusion during resource sharing;

[0398] L j Let $\frac{j}{j}$ be the resource loss cost of the j-th smart city due to data leakage caused by illegal intrusion during resource sharing.

[0399] τ is the resource provision coefficient that external systems of the smart city cluster can provide data resources to the smart cities in the smart city cluster;

[0400] τL i Let i be the amount of data resources that the i-th smart city can obtain from external systems of the smart city set;

[0401] τL j Let j be the amount of data resources that the j-th smart city can obtain from external systems of the smart city set;

[0402] μ i Let μ be the cost coefficient for the data leakage caused by unauthorized intrusion during resource sharing in the i-th smart city. j Let be the loss cost coefficient of the j-th smart city due to data leakage caused by illegal intrusion during resource sharing;

[0403] ω is the effect coefficient of data spillover effect when a smart city chooses to share information, and ω≥1;

[0404] ωL i If both city i and city j choose the sharing strategy, then city j can obtain data security benefits from sharing with city i.

[0405] ωL j If both city i and city j choose the sharing strategy, then city i can obtain data security benefits through sharing with city j.

[0406] In one embodiment, when S is determined ik (k = 1, 2, ..., 4) is the data acquisition function of the i-th smart city, and S jk When (k = 1, 2, ..., 4) is the data acquisition function for the j-th city, then:

[0407] (1) 0 ≤ S ik ≤T;

[0408] (2) 0 ≤ S jk ≤T′;

[0409] (3) The effect coefficient ω and the resource provision coefficient τ, for the data acquisition function S ik and S jk All are monotonically increasing, and the loss cost coefficient μ is related to the data acquisition function S.ik and S jk All are monotonically decreasing.

[0410] In one embodiment, for the i-th smart city, when acting as a data resource provider, if the proportion of choosing to share information is θ, then the proportion of choosing not to share information is 1-θ.

[0411] For the j-th smart city, when acting as a data resource demander, the proportion of those choosing information sharing is: The proportion of those who choose not to share information is: but

[0412] In one embodiment, the expected benefit of data security for the i-th smart city, whether it chooses to share information or not, is:

[0413] In the case of information sharing, the expected benefit S of data security is for:

[0414]

[0415] Without information sharing, the expected benefit S of data security in for:

[0416]

[0417] The expected data security benefit for the j-th smart city, considering both the option to share information and the option not to share information, is:

[0418] When information is shared, the expected benefits of data security are:

[0419] S js =θS j1 +(1-θ)S j3

[0420] =θ(E j +ωL i +τL j -μ j L j )+(1-θ)(E j +τL j -μ j L j )

[0421] =θωL i +E j +τL j -μ j L j (6.11)

[0422] Without information sharing, the expected benefits of data security are:

[0423] S jn =θS j2 +(1-θ)S j4

[0424] =θ(E j +L i )+(1-θ)E j

[0425] =θL i +E j (6.12)

[0426] In one embodiment, it further includes determining the overall expected benefit S of data security for the i-th smart city. i The overall expected benefit S of data security for the j-th smart city j for:

[0427]

[0428]

[0429]

[0430] The replication dynamic equation F(θ) of the i-th smart city and the replication dynamic equation of the j-th smart city will be used to share information. It is expressed as follows:

[0431]

[0432]

[0433] In one embodiment, the system further includes the ability to determine the equilibrium point of the data sharing system or the replica dynamic system when the replica dynamic equations (6.15) and (6.16) are equal to zero. Therefore, based on the replicating dynamic equations (6.15) and (6.16), the following can be determined:

[0434] (1) The following four points are candidate evolutionary equilibrium points: O(0,0), A(1,0), B(0,1), C(1,1);

[0435] (2 points) It is also a candidate evolutionary equilibrium point, among which and

[0436] Based on the stability requirements of the replicating dynamic system and the differential equation, the equilibrium point of the data sharing system or replicating dynamic system must meet the following conditions:

[0437]

[0438] Substituting formulas (6.15) and (6.16) into formula (6.17), we obtain the following result:

[0439]

[0440] In one embodiment, the method further includes determining local stability based on the Jacobian matrix J of the data sharing system or the replicating dynamic system, thereby determining a stability strategy for the data sharing system or the replicating dynamic system.

[0441]

[0442] in,

[0443]

[0444]

[0445]

[0446]

[0447] Determining the equilibrium point and stability of an evolutionary game using the properties of the Jacobian matrix includes: using the values ​​of the determinant det(J) and trace tr(J) to determine the equilibrium point and stability of the evolutionary game. If the determinant tr(J) is greater than zero and the trace tr(J) is less than zero, then the equilibrium point of the data sharing system or the replicating dynamic system is locally stable. This equilibrium point is then chosen as the evolutionary equilibrium point of the data sharing system or the replicating dynamic system, resulting in the following formula:

[0448] Determinant:

[0449] trace:

[0450] In one embodiment, it also includes;

[0451] when or At that time, the evolutionary equilibrium point is C(1,1);

[0452] Both the i-th and j-th smart cities have chosen to share information (information sharing is also known as data resource sharing, data sharing, or resource sharing).

[0453] In one embodiment, it also includes,

[0454] when At that time, the evolutionary equilibrium point is determined to be A(1,0);

[0455] The i-th smart city chooses to share information, while the j-th smart city chooses not to share information.

[0456] In one embodiment, it also includes,

[0457] when At that time, the evolutionary equilibrium points are determined to be O(0,0) and C(1,1);

[0458] When both the i-th and j-th smart cities choose to share information, the external system of the smart city set provides the first amount of data resources to the smart cities in the smart city set.

[0459] When both the i-th and j-th smart cities choose not to share information, the external system of the smart city set provides the smart cities in the smart city set with a second amount of data resources.

[0460] The second quantity is greater than the first quantity.

[0461] In one embodiment, it also includes,

[0462] when or At that time, the evolutionary equilibrium point is determined to be O(0,0);

[0463] Both the i-th and j-th smart cities choose not to share information, and the external systems of the smart city set provide a third number of data resources to the smart cities in the smart city set.

Claims

1. A method for resource allocation in smart cities based on data stability, the method comprising: A set of smart cities is defined as a collection of smart cities that have weakly correlated resources with each other, wherein a weakly correlated resource is defined as a resource in the smart city set where the ratio of the amount of correlated data resources between any two smart cities to the total amount of data resources of the two smart cities is less than or equal to a correlation threshold. When it is determined that each smart city does not share related data resources, the initial data security benefits obtained by each smart city based on information security configuration are as follows. And the resource provision coefficient that determines the external systems of the smart city cluster can provide data resources to the smart cities within the smart city cluster. ; When determining the resource loss cost due to data leakage caused by unauthorized intrusion during resource sharing for each smart city's sharing of related data resources, and loss cost coefficient And determine the data spillover coefficient when each smart city shares related data resources. ; Randomly select a first smart city and a second smart city from the set of smart cities, and combine the first smart city and the second smart city into a data sharing system; and Local stability is determined based on the equilibrium point of the data sharing system, and the data stability of the sharing system is determined based on the local stability. Based on the data stability, the shared resources of the first smart city and the second smart city in the data sharing system are configured. Also includes; when or At that time, the evolutionary equilibrium point is ; Both the i-th and j-th smart cities have chosen to share information. Let be the cost coefficient of data leakage caused by unauthorized intrusion during resource sharing in the i-th smart city. Let be the loss cost coefficient of the j-th smart city due to data leakage caused by illegal intrusion during resource sharing; Let be the resource loss cost of the i-th smart city due to data leakage caused by illegal intrusion during resource sharing; Let $\frac{j}{j}$ be the resource loss cost of the j-th smart city due to data leakage caused by illegal intrusion during resource sharing. The resource provision coefficient for external systems within a smart city cluster that can provide data resources to the smart cities within the cluster; It also includes, when At that time, the evolutionary equilibrium point was determined to be ; The i-th smart city chooses to share information, while the j-th smart city chooses not to share information. It also includes, when At that time, the evolutionary equilibrium point was determined to be and ; When both the i-th and j-th smart cities choose to share information, the external system of the smart city set provides the first amount of data resources to the smart cities in the smart city set. When both the i-th and j-th smart cities choose not to share information, the external system of the smart city set provides the smart cities in the smart city set with a second amount of data resources. The second quantity is greater than the first quantity; It also includes, when or At that time, the evolutionary equilibrium point was determined to be ; Both the i-th and j-th smart cities choose not to share information, and the external systems of the smart city set provide a third number of data resources to the smart cities in the smart city set.

2. The method according to claim 1, wherein the association threshold is 5%, 10%, 15% or 20%.

3. The method according to claim 1, wherein, The first smart city is smart city i, and the second smart city is smart city j; Scenario 1: When a smart city and smart cities When all choose to share information, a smart city is determined. and smart cities Data security benefits : （6.1） （6.2） The second scenario: When a smart city... Choosing to share information, and smart cities When choosing not to share information, determine the smart city. and smart cities Data security benefits for: （6.3） （6.4） The third scenario: When a smart city... Choosing not to share information, while smart cities When choosing to share information, determine the smart city. and smart cities Data security benefits for: （6.5） （6.6） The fourth scenario: When a smart city... and smart cities When all choose not to share information, a smart city is determined. and smart cities Data security : （6.7） （6.8） in, In the first scenario, smart cities Data security benefits; In the first scenario, smart cities Data security benefits; In the second scenario, smart cities Data security benefits; In the second scenario, smart cities Data security benefits; In the third scenario, smart cities Data security benefits; In the third scenario, smart cities Data security benefits; In the fourth scenario, smart cities Data security benefits; In the fourth scenario, smart cities Data security benefits; This represents the initial data security benefits obtained by the i-th smart city based on information security configuration. The initial data security benefits obtained by the j-th smart city based on information security configuration; Let i be the amount of data resources that the i-th smart city can obtain from external systems of the smart city set; Let j be the amount of data resources that the j-th smart city can obtain from external systems of the smart city set; For the city and city If all choose the sharing strategy, then the city Through the city The benefits of data security obtained through sharing For the city and city If all choose the sharing strategy, then the city Through the city The benefits of data security obtained through sharing.

4. The method according to claim 3, when determining It is the data acquisition function for the i-th smart city, and It is the j-th city When using the data retrieval function, then: （1） ； （2） ； (3) Effect coefficient and resource provision coefficient Data retrieval function and All are monotonically increasing, and the loss cost coefficient is also increasing. Data retrieval function and All are monotonically decreasing.

5. According to the method described in claim 1, for the i-th smart city, when acting as a data resource provider, the proportion of information sharing selected is: The proportion of those who choose not to share information is ; For the j-th smart city, when acting as a data resource demander, the proportion of those choosing information sharing is: The proportion of those who choose not to share information is ,but .

6. According to the method of claim 1 or 4, the expected benefit of data security for the i-th smart city, whether it chooses to share information or not, is: Expected benefits of data security when information is shared for: （6.9） Expected benefits of data security without information sharing for: （6.10） The expected data security benefits for the j-th smart city, considering both the option to share information and the option not to share information, are: When information is shared, the expected benefits of data security are: （6.11） Without information sharing, the expected benefits of data security are: （6.12）。 7. The method according to claim 1, further comprising determining the overall expected benefit of data security for the i-th smart city. The overall expected benefit of data security for the j-th smart city for: （6.13） （6.14） The replication dynamic equation of the i-th smart city that shares information The replication dynamic equation of the j-th smart city It is expressed as follows: （6.15） （6.16）。 8. The method according to claim 7 further comprises, when the replication dynamic equation (6.15) and the replication dynamic equation (6.16) are equal to zero, being able to determine the equilibrium point of the data sharing system or the replication dynamic system. Therefore, based on the replicating dynamic equations (6.15) and (6.16), the following can be determined: (1) The following four points are candidate evolutionary equilibrium points: ; (2) point It is also a candidate evolutionary equilibrium point, among which ,and , Based on the stability requirements of the replicating dynamic system and the differential equation, the equilibrium point of the data sharing system or replicating dynamic system must meet the following conditions: （6.17） Substituting formulas (6.15) and (6.16) into formula (6.17), we obtain the following result: （6.18）。 9. The method according to claim 1 or 8, further comprising determining local stability based on the Jacobian matrix J of the data sharing system or the replicating dynamic system, thereby determining a stability strategy for the data sharing system or the replicating dynamic system: （6.19） in, Determining the equilibrium point and stability of evolutionary games using the properties of the Jacobian matrix includes: using the determinant. Heji The value of determines the equilibrium point and stability of the evolutionary game. If the determinant Greater than zero and trace If the value is less than zero, then the equilibrium point of the data sharing system or the replication dynamic system is locally stable. Therefore, choosing this equilibrium point as the evolutionary equilibrium point of the data sharing system or the replication dynamic system yields the following formula: Determinant: (6.20) trace: (6.21).

10. A system for resource allocation in smart cities based on data stability, the system comprising: A first determining device is used to determine a set of smart cities consisting of multiple smart cities that have weakly correlated resources with each other, wherein a weakly correlated resource is defined as the ratio of the amount of correlated data resources between any two smart cities in the set to the total amount of data resources of the two smart cities being less than or equal to a correlation threshold. The second determining device is used to determine the initial data security benefits obtained by each smart city based on information security configuration when each smart city does not share related data resources. And the resource provision coefficient that determines the external systems of the smart city cluster can provide data resources to the smart cities within the smart city cluster. ; The third determining device is used to determine the resource loss cost caused by data leakage due to illegal intrusion during resource sharing when each smart city shares related data resources. and loss cost coefficient And determine the data spillover coefficient when each smart city shares related data resources. ; A constitutive apparatus is used to randomly select a first smart city and a second smart city from the set of smart cities, and to form a data sharing system between the first smart city and the second smart city. A configuration device is used to determine local stability based on the equilibrium point of the data sharing system, and to determine the data stability of the sharing system based on the local stability, and to configure the shared resources of the first smart city and the second smart city within the data sharing system based on the data stability. Also includes; when or At that time, the evolutionary equilibrium point is ; Both the i-th and j-th smart cities have chosen to share information. Let be the cost coefficient of data leakage caused by unauthorized intrusion during resource sharing in the i-th smart city. Let be the loss cost coefficient of the j-th smart city due to data leakage caused by illegal intrusion during resource sharing; Let be the resource loss cost of the i-th smart city due to data leakage caused by illegal intrusion during resource sharing; Let $\frac{j}{j}$ be the resource loss cost of the j-th smart city due to data leakage caused by illegal intrusion during resource sharing. The resource provision coefficient for external systems within a smart city cluster that can provide data resources to the smart cities within the cluster; It also includes, when At that time, the evolutionary equilibrium point was determined to be ; The i-th smart city chooses to share information, while the j-th smart city chooses not to share information. It also includes, when At that time, the evolutionary equilibrium point was determined to be and ; When both the i-th and j-th smart cities choose to share information, the external system of the smart city set provides the first amount of data resources to the smart cities in the smart city set. When both the i-th and j-th smart cities choose not to share information, the external system of the smart city set provides the smart cities in the smart city set with a second amount of data resources. The second quantity is greater than the first quantity; It also includes, when or At that time, the evolutionary equilibrium point was determined to be ; Both the i-th and j-th smart cities choose not to share information, and the external systems of the smart city set provide a third number of data resources to the smart cities in the smart city set.

11. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method according to any one of claims 1-9.

12. An electronic device, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-9.

Images