Security operation and maintenance monitoring method based on local target CIM model analysis

Abstract

The application relates to the technical field of data security, in particular to a security operation and maintenance monitoring method based on local target CIM model analysis, which comprises the following steps: a CIM platform is built, and multiple data in a city are stored as data sources of the city through real-time monitoring, wherein each data unit in the data sources is composed of plaintext data and attribute information; the CIM platform performs data access by initiating an access request, obtains to-be-encrypted data based on attribute information of accessed data in the access request and in combination with a secondary attribute access strategy; the time credibility, the space credibility and the evaluation coefficient of each to-be-encrypted data are calculated, the control parameters of a chaotic mapping algorithm are adjusted, the plaintext data of the to-be-encrypted data is encrypted and transmitted to the CIM platform for analysis and decision-making. While guaranteeing data security, the application optimizes the use of computing resources, improves the security of data protection and the response speed of the platform.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data security, in particular to a security operation and maintenance monitoring method based on local target CIM model analysis. BACKGROUND

[0002] With the deepening of the construction of smart cities, the city information model (CIM) platform has become the core infrastructure of digital governance of the city. Through the integration of geographic information systems (GIS), building information models (BIM), Internet of Things (IoT) and other technologies, it has monitored and aggregated massive, multi-source and heterogeneous data in the fields of urban drainage networks, transportation and environment in real time, so as to monitor and evaluate the operation state of the city in real time and provide operation and maintenance monitoring methods. For example, in the case of extreme rainstorm weather, the CIM platform can be used to monitor and evaluate the waterlogging situation of the city, so as to take appropriate measures in advance.

[0003] The CIM platform realizes digital modeling and management of the urban physical environment and infrastructure by integrating multi-source and heterogeneous data. To ensure data security, traditional methods generally use attribute-based encryption and other encryption technologies for access control. By applying the same strength of encryption to all data that meets the same access policy, the security of the data is ensured. However, due to the differences in the timeliness and spatial distribution characteristics of different data that meet the access policy, the effective value of different data is also different. Using high-strength encryption results in a waste of huge computing resources and increases the response time of the platform. If the same low-strength encryption is used, it is difficult to ensure the security of important data, resulting in an inability to achieve the optimal balance between data security protection and resource efficiency. SUMMARY

[0004] To solve the above technical problems, a security operation and maintenance monitoring method based on local target CIM model analysis is provided to solve the existing problems.

[0005] The technical problem solved by the present application is to provide a security operation and maintenance monitoring method based on local target CIM model analysis, comprising the following steps:

[0006] A CIM platform is built using CIM technology, and real-time monitoring of various data in the city is stored as a data source of the city. Each data unit in the data source is composed of plaintext data and attribute information, and the attribute information includes a main attribute set and a secondary attribute set. The main attribute set at least includes a timestamp attribute and a geographic location attribute.

[0007] The CIM platform initiates an access request for data access. Based on the attribute information of the accessed data in the access request and the secondary attribute set of the data unit in the data source, the secondary attribute access policy is combined to filter the data unit in the data source and obtain the data to be encrypted.

[0008] The time credibility of each data to be encrypted is calculated by analyzing the interval between the time stamp corresponding to each data to be encrypted and the initiation time of the access request.

[0009] The spatial credibility of each data to be encrypted is calculated by analyzing the spatial distribution of the geographic positions of each data to be encrypted and the access data in the access request.

[0010] Based on the time credibility and the spatial credibility, an evaluation coefficient of each data to be encrypted is obtained, the control parameter of the chaotic mapping algorithm is adjusted, the plaintext data of the data to be encrypted is encrypted to obtain the ciphertext data by using the adjusted control parameter and the chaotic mapping algorithm, and the ciphertext data is transmitted to the CIM platform for analysis and decision-making.

[0011] Preferably, the access request includes an access attribute set, and the access attribute set includes a time stamp attribute of the initiation of the access request, a geographic position attribute of the access data, and a secondary attribute set of the access data.

[0012] Preferably, the data to be encrypted is obtained by screening all data units from the data source that satisfy the strategy of the secondary attribute set based on the secondary attribute set of the access data in the access request according to a predefined secondary attribute access strategy.

[0013] Preferably, the time credibility of each data to be encrypted is calculated by calculating the time interval between the time stamp corresponding to each data to be encrypted and the time stamp of the initiation of the access request; if the time interval is greater than a preset first value, the time credibility of each data to be encrypted is assigned a value of 0; otherwise, the time credibility of each data to be encrypted is the result of negative mapping of the time interval.

[0014] Preferably, the spatial credibility of each data to be encrypted is calculated by:

[0015] calculating the spatial distance between the geographic position of each data to be encrypted and the geographic position of the access data in the access request;

[0016] taking the ratio of the spatial distance to a preset second value as a relative ratio, and taking the negative mapping of the relative ratio as the spatial credibility of each data to be encrypted.

[0017] Preferably, the spatial distance is calculated by calculating the Haversine distance between the geographic position of each data to be encrypted and the geographic position of the access data in the access request.

[0018] Preferably, the evaluation coefficient is the result of positive fusion of the time credibility and the spatial credibility.

[0019] Preferably, the specific process of the forward fusion is: weighting and summing the time credibility and the space credibility based on a preset first weight and a preset second weight to obtain an evaluation coefficient of each to-be-encrypted data; wherein the preset first weight is less than the preset second weight, and the sum of the preset first weight and the preset second weight is 1.

[0020] Preferably, the calculation formula of the adjusted control parameter corresponding to the i-th to-be-encrypted data is: , wherein, is a preset initial control parameter, is the normalized evaluation coefficient corresponding to the i-th to-be-encrypted data, is a preset adjustment amplitude.

[0021] Preferably, the encrypting the plaintext data of the to-be-encrypted data to obtain the ciphertext data comprises: generating a chaotic sequence by using a Logistic chaotic mapping algorithm based on the adjusted control coefficient corresponding to each to-be-encrypted data, generating a key by using the chaotic sequence, and obtaining the ciphertext data by performing an exclusive or operation on the key and the plaintext data of each to-be-encrypted data.

[0022] The present application has at least the following beneficial effects:

[0023] ​​​The application has the beneficial effects that the to-be-encrypted data meeting the secondary attribute strategy is quickly screened out from the massive data, fine-grained control of data access is realized, and the efficiency of data access and processing is improved; the time credibility of each to-be-encrypted data is calculated, the beneficial effects are that the timeliness of the to-be-encrypted data is considered to evaluate the effective value of the to-be-encrypted data, to reflect the encryption degree of the to-be-encrypted data, to ensure that the data with high timeliness is protected at a higher level, and the data with low timeliness can appropriately reduce the encryption strength to save computing resources; secondly, the spatial credibility of each to-be-encrypted data is calculated, the beneficial effects are that the spatial distribution characteristics of the to-be-encrypted data are considered to evaluate the association between the to-be-encrypted data and the access data, to further evaluate the effective value of the to-be-encrypted data, to ensure that the data with strong spatial association with the access data is more strictly encrypted; the evaluation coefficient of each to-be-encrypted data is obtained, the beneficial effects are that the importance of the to-be-encrypted data is comprehensively evaluated to reflect the situation that the to-be-encrypted data needs higher level of encryption protection; the control parameters of the chaotic mapping algorithm are adjusted, the plaintext data of the to-be-encrypted data is encrypted to obtain the ciphertext data by using the adjusted control parameters and the chaotic mapping algorithm, and the ciphertext data is transmitted to the CIM platform for analysis and decision, the beneficial effects are that the data is controlled by the secondary attribute, the encryption degree of the data is evaluated by the primary attribute, the evaluation coefficient is calculated, the control parameters of the chaotic mapping algorithm are dynamically adjusted, the chaotic sequence generated by the chaotic mapping algorithm has unpredictability and high complexity to generate keys with different strengths, then the encrypted data is more difficult to crack, and important data is more highly protected, and relatively unimportant data is encrypted at a lower level, so that the use of computing resources is optimized while ensuring data security, and the security and response speed of the CIM platform for data protection are improved as a whole. BRIEF DESCRIPTION OF DRAWINGS

[0024] The security operation and maintenance monitoring method based on the local target CIM model analysis provided by the embodiments of the application will be further described in detail below with reference to the accompanying drawings.

[0025] Figure 1 The step flowchart of the security operation and maintenance monitoring method based on the local target CIM model analysis provided by the embodiments of the application is shown in the following figure.

[0026] Figure 2 The step flowchart of the method for obtaining ciphertext data provided by the embodiments of the application is shown in the following figure. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of the security operation and maintenance monitoring method based on local target CIM model analysis, in conjunction with the accompanying drawings and implementation examples, is provided. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0029] Please see Figure 1 The diagram illustrates a flowchart of a security operation and maintenance monitoring method based on local target CIM model analysis according to an embodiment of this application. The method includes the following steps:

[0030] Step 1: Build a CIM platform using CIM technology and store various data from real-time monitoring of the city as the city's data source. Each data unit in the data source consists of plaintext data and attribute information. The attribute information includes a set of primary attributes and a set of secondary attributes. The primary attribute set includes at least timestamp attributes and geographic location attributes.

[0031] With the acceleration of digitalization and the widespread adoption of IoT technology, the Internet of Things (IoT) has now permeated various fields, forming a model of interconnectedness of everything. CIM (City Information Modeling) is a digital representation of a city's physical space. By integrating multi-source data such as Geographic Information System (GIS), Building Information Modeling (BIM), and the Internet of Things (IoT), a three-dimensional digital model of the city is constructed. This model can monitor the status of basic urban operating equipment, forming a more specific and comprehensive information platform. Based on various information, it can simulate and display various emergencies and analyze the effects of various response methods, providing operation and maintenance monitoring methods.

[0032] In this embodiment, we take the drainage pipe network in the city as an example for analysis. As an important part of urban infrastructure, the municipal drainage pipe network is prone to causing large-scale urban flooding in extreme rainstorms. The failure to monitor the operation of the city's drainage pipe network in real time is a problem that hinders early warning and prediction.

[0033] With the development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent detection technology can achieve real-time monitoring and data analysis of the state of the drainage pipe network through online sensors, pipeline robots, and unmanned aerial vehicles, effectively improving the intelligent and accurate level of drainage pipe network management. Therefore, by building a CIM platform through CIM technology, the geographic information, structural information, and operation data of the pipe network can be integrated to visually display, comprehensively analyze, and intelligently decide the state of the drainage pipe network, significantly improving the efficiency and response speed of pipe network management, enabling timely detection and early warning of potential safety problems, and enhancing urban safety protection.

[0034] Therefore, for the drainage pipe network in the city, to meet the business needs of urban drainage system supervision and display real-time dynamic information of the drainage system, IOT technology is needed to associate dynamic information and static information of physical entities in CIM to provide data support for the platform. Therefore, IOT technology and CIM technology are integrated to realize drainage facility operation supervision, analyze and warn potential problems in the drainage system, and improve the operation and management level of the drainage system.

[0035] By monitoring the facilities of the drainage pipe network, the specific monitoring objects include important drainage facilities such as drainage pipe networks, drainage gates, drainage pump stations, inspection wells, interception wells, overflow wells, rainwater storage tanks, sewage treatment plants, water accumulation points, low-lying underpass passages, and river and lake outlets. By deploying different sensors at key locations, parameters such as water level, water quality, water flow, rainfall, and water flow rate can be measured. Water quality can include water pH, conductivity, and chemical oxygen demand. Each sensor is considered a monitoring point, and the data monitored by the sensor in real time is sent to an edge gateway for preprocessing through an IOT network. Combined with a GIS system, the data type, timestamp, geographic location, facility type, device number, and information such as the street block to which the data belongs are written into a database for storage as a city data source.

[0036] The preprocessing process is to fill in missing values. In this embodiment, linear interpolation is used to fill in missing values. Linear interpolation is a well-known technique and will not be described here. As an alternative, the implementer can use other methods such as mean filling method, etc. This embodiment does not make special restrictions.

[0037] In this embodiment, the data sampling interval is As an alternative, the implementer can set it according to actual conditions.

[0038] Thus, the city data source is obtained.

[0039] Step 2, the CIM platform accesses data by initiating an access request, based on the attribute information of the accessed data in the access request and the secondary attribute set of the data unit in the data source, combined with the secondary attribute access strategy, the data unit in the data source is screened to obtain the data to be encrypted.

[0040] Since the CIM platform can comprehensively analyze and evaluate the integrated multi-source heterogeneous data, when the CIM platform initiates a data access request to the data service layer, i.e. the database, in order to ensure the security of data transmission, the data needs to be encrypted and transmitted to the CIM platform. The traditional attribute-based encryption (ABE) algorithm uses a static attribute set for encryption. The attribute encryption strategy encrypts the data by defining a logical operation combination of a set of static attributes. The data can be accessed if it meets the attribute encryption strategy. For each monitoring parameter in the entire city, there are the same static attributes. As long as each monitoring parameter of all monitoring points meets the attribute encryption strategy, access control can be performed. However, the data in the urban drainage system has strong time effectiveness and spatial correlation. The traditional ABE algorithm uses the same encryption strength for all data based on the attributes of the data, which will cause waste of computing resources and long response time, and it is difficult to meet the balance requirement of data security and transmission efficiency.

[0041] Each data unit in the data source is composed of plaintext data and attribute information, wherein the attribute information includes a primary attribute set and a secondary attribute set;

[0042] It should be noted that the plaintext data is the monitoring value actually collected by the sensor, the data type and the device number, the attribute information is the data type, the timestamp, the geographic location, the facility type, the device number and the street block to which it belongs, therefore, the primary attribute set at least includes the timestamp and the geographic location information, and the secondary attribute set at least includes the data type and the facility type.

[0043] Wherein, the plaintext data is the data content to be encrypted and transmitted, the primary attribute functions as dynamic adjustment of encryption strength, and the secondary attribute functions as data access control by the platform. The data can be transmitted if it meets the secondary attribute access strategy. When the CIM platform initiates a data access request, the data that needs to be accessed is called by the secondary attribute access strategy.

[0044] When the CIM platform accesses data, it will initiate an access request , wherein the access request contains an access attribute set, the access attribute set includes a timestamp attribute of the access request, a geographic location attribute of the accessed data and a secondary attribute set of the accessed data;

[0045] Based on the secondary attribute set of the access data in the access request and the secondary attribute set of the data units in the data source, all data units meeting the secondary attribute strategy are filtered from the data source as the data to be encrypted according to a predefined secondary attribute access strategy.

[0046] It should be noted that the attribute access strategy is a known technology and will not be described here; the predefined secondary attribute access strategy is to filter out data meeting specific conditions by defining a set of logical rules. For ease of understanding, assume that the CIM platform wants to access the rainfall of the A block in the city, and the data type in the secondary attribute is rainfall, then all data with the data type of rainfall will be filtered out as the data to be encrypted; if the CIM platform wants to access the water level at the river and lake outlet, the secondary attribute is the facility type of river and lake outlet and the data type of water level, and the secondary attribute access strategy is defined as: facility type = river and lake outlet AND data type = water level, all water level data at the river and lake outlet will be filtered out as the data to be encrypted; the secondary attribute access strategy is defined as: facility type = river and lake outlet OR data type = water level, then all water level data and all data with the facility type of river and lake outlet in the secondary attribute are filtered out.

[0047] Thus, the data to be encrypted is obtained.

[0048] Step 3, analyze the interval between the time stamp corresponding to each data to be encrypted and the initiation time of the access request, calculate the time credibility of each data to be encrypted; calculate the spatial credibility of each data to be encrypted through the spatial distribution of the geographic location between each data to be encrypted and the access data in the access request; based on the time credibility and the spatial credibility, obtain the evaluation coefficient of each data to be encrypted.

[0049] Further, since there are a large number of data meeting the same secondary attribute as the access request, in order to ensure data security, all data need to be encrypted and transmitted. However, due to the timeliness and spatial correlation of the data, only the data meeting the timeliness and spatial correlation requirements of the primary attribute can provide reliable urban drainage information in the access control of the data. In terms of timeliness, as the data changes, if the time stamp attribute difference of the data is large, the credibility of the data is low; in terms of spatial correlation, other data with the same secondary attribute that are the same as or close to the geographic location attribute of the access data have a higher correlation with the access data, while other data with the same secondary attribute that are far away from the geographic location of the access data have a very low correlation. The traditional attribute-based encryption method applies the same encryption strategy to all data with the same secondary attribute, which leads to a high occupation of computing resources by data with very low correlation, affecting the overall response speed.

[0050] Based on the above analysis, the time reliability is calculated by analyzing the proximity of the timestamps between each piece of data to be encrypted and the access request initiation. Specifically:

[0051] Calculate the time interval between the timestamp corresponding to each piece of data to be encrypted and the timestamp of the access request.

[0052] If the time interval is greater than a preset first value, the time credibility of each piece of data to be encrypted is assigned to 0; otherwise, the time credibility of each piece of data to be encrypted is the result of negative mapping of the time interval.

[0053] In this embodiment, the first preset value is the sampling interval. Three times, or 15 minutes, is another implementation method that the implementer can set according to the actual situation; secondly, the specific process of negative mapping is as follows: negative mapping is performed using an exponential function, assuming the time interval is denoted as ,Will The result is used as the result of the negative mapping, where, It is an exponential function with the natural constant as the base.

[0054] It should be noted that the smaller the time interval, the greater the time reliability, indicating that the time of the data to be encrypted is closer to the time of the access data, the more timely the data to be encrypted is, the higher its reliability, and the more it needs to be protected by a higher level of encryption.

[0055] Secondly, spatial credibility is calculated by analyzing the spatial distribution between the geographical locations of the data to be encrypted and the geographical locations of the data accessed in the access requests. Specifically:

[0056] Calculate the spatial distance between the geographical location of each piece of data to be encrypted and the geographical location of the data accessed in the access request;

[0057] In this embodiment, since geographical location is represented by latitude and longitude, spatial distance is calculated by the Haversine distance between the geographical location of each piece of data to be encrypted and the geographical location of the data to be accessed in the access request. The Haversine distance is the distance between two latitude and longitude points. The calculation of the Haversine distance is a well-known technique and will not be described in detail here.

[0058] The ratio of the spatial distance to the preset second value is recorded as the relative ratio. The relative ratio is negatively mapped to serve as the spatial credibility of each piece of data to be encrypted.

[0059] In this embodiment, the preset range of the second numerical value is: In the embodiment, the preset second value is set to 100 m, and as another embodiment, the implementer can set it according to the actual situation; by setting the preset second value, the spatial distance is scaled; secondly, the specific process of negative mapping is to use an exponential function for negative mapping, assuming that the relative ratio is denoted as The result of is taken as the result of negative mapping, wherein is an exponential function with a natural constant as the base.

[0060] It should be noted that the smaller the spatial distance, the greater the spatial credibility, which indicates that the geographical position of the to-be-encrypted data and the access data is closer, and the spatial correlation of the to-be-encrypted data and the access data is stronger, and therefore the credibility of the to-be-encrypted data is higher.

[0061] Further, based on the time credibility and the spatial credibility, an evaluation coefficient is determined, specifically:

[0062] Based on the preset first weight and the preset second weight, the time credibility and the spatial credibility are weighted and summed to obtain the evaluation coefficient of each to-be-encrypted data; wherein the preset first weight is less than the preset second weight, and the sum of the preset first weight and the preset second weight is 1;

[0063] In the embodiment, the preset first weight is 0.3, and the preset second weight is 0.7, so that the spatial position correlation of the data is more credible than the time correlation, and as another embodiment, the implementer can set it according to the actual situation.

[0064] It should be noted that the greater the evaluation coefficient, the higher the importance of the to-be-encrypted data, and higher-level encryption protection is required, otherwise, lower-level encryption protection can be used to save computing resources.

[0065] At this point, the evaluation coefficient of each to-be-encrypted data is obtained.

[0066] Step 4, based on the evaluation coefficient, the control parameter of the chaotic mapping algorithm is adjusted, and the plaintext data of the to-be-encrypted data is encrypted to obtain the ciphertext data by using the adjusted control parameter and combining the chaotic mapping algorithm, and the ciphertext data is transmitted to the CIM platform for analysis and decision.

[0067] Further, the Logistic chaotic mapping algorithm is a chaotic search method, and the generated chaotic sequence can present higher randomness, has the characteristics of initial value sensitivity, pseudo-randomness and ergodicity, the size of the control parameter directly affects the complexity and unpredictability of the chaotic sequence, thereby affecting the encryption strength, therefore, when the evaluation coefficient of the data to be encrypted is larger, more complex encryption effect is needed, the control parameter of the Logistic chaotic mapping algorithm should be increased to improve the security of the data and avoid tampering by attacks, otherwise, it indicates that the importance of the information of the data to be encrypted is relatively low, the control parameter of the Logistic chaotic mapping algorithm can be reduced to reduce unnecessary occupation of computing resources and improve the response rate of the platform.

[0068] Based on the evaluation coefficient, the control parameter of the chaotic mapping algorithm is adjusted, specifically:

[0069] The calculation formula of the adjusted control parameter corresponding to each data to be encrypted is:

[0070]

[0071] wherein, is the adjusted control parameter corresponding to the i th data to be encrypted, is a preset initial control parameter, is the normalized evaluation coefficient corresponding to the i th data to be encrypted, is a preset adjustment amplitude; In this embodiment, since the time credibility and the space credibility are both negatively mapped by the exponential function, the value range of is 0~1, so the evaluation coefficient does not need to be normalized, as other implementation manners, the implementer can use the sigmoid function for normalization processing, wherein the sigmoid function is a known technology and will not be described here; secondly, the preset initial control parameter

[0072] is set to 3.8, and the preset adjustment amplitude is 0.2, which controls the adjustment amplitude to avoid excessive adjustment of the control parameter, as other implementation manners, the implementer can set it according to the actual situation.

[0073] ​​Based on the adjusted control coefficients corresponding to each piece of data to be encrypted, a chaotic sequence is generated using the Logistic chaotic mapping algorithm. The chaotic sequence is then used to generate a key. An XOR operation is performed on the key and the plaintext data of each piece of data to be encrypted to obtain ciphertext data. The ciphertext data is then transmitted to the CIM platform to ensure data transmission security. The CIM platform decrypts the transmitted data and uses the data to analyze and make decisions about the city's drainage situation, thereby assessing the city's flooding situation and optimizing the city's drainage system.

[0074] It should be noted that the encryption process of the Logistic chaotic mapping algorithm and XOR operation is a well-known technology and will not be elaborated here.

[0075] Furthermore, the flowchart of the method for obtaining encrypted data provided in this application embodiment is as follows: Figure 2 As shown.

[0076] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0077] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A security operation and maintenance monitoring method based on local target CIM model analysis, characterized in that, The method includes the following steps: A CIM platform is built using CIM technology, and various data on the real-time monitoring of the operation of the city's drainage pipe network are stored as the city's data source. Each data unit in the data source consists of plaintext data and attribute information. The attribute information includes a main attribute set and a secondary attribute set. The main attribute set is used to dynamically adjust the encryption strength, and the secondary attribute set is used for data access control. The main attribute set includes at least timestamp attribute and geographic location attribute. The CIM platform accesses data by initiating access requests. Based on the attribute information of the data to be accessed in the access request and the set of secondary attributes of the data units in the data source, combined with the secondary attribute access strategy, the platform filters the data units in the data source and obtains the data to be encrypted. Analyze the interval between the timestamp corresponding to each piece of data to be encrypted and the time of initiation of the access request, and calculate the time interval between the timestamp corresponding to each piece of data to be encrypted and the timestamp of initiation of the access request; if the time interval is greater than a preset first value, the preset first value is 3 times the data sampling interval, and the time credibility of each piece of data to be encrypted is assigned to 0; otherwise, the time credibility of each piece of data to be encrypted is the result of negative mapping of the time interval. Based on the spatial distribution of geographical locations between each piece of data to be encrypted and the data accessed in the access request, the spatial distance between the geographical locations of each piece of data to be encrypted and the geographical locations of the data accessed in the access request is calculated; the ratio of the spatial distance to a preset second value is recorded as the relative ratio, the preset second value being in the range of [50, 500] meters; the relative ratio is negatively mapped as the spatial credibility of each piece of data to be encrypted. Based on time and space reliability, the evaluation coefficients of each piece of data to be encrypted are obtained. The control parameters of the chaotic mapping algorithm are adjusted. Using the adjusted control parameters and the chaotic mapping algorithm, the plaintext data of the data to be encrypted is encrypted to obtain ciphertext data. The ciphertext data is then transmitted to the CIM platform for analysis and decision-making.

2. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 1, characterized in that, The access request contains a set of access attributes, which includes: the timestamp attribute of the access request, the geographic location attribute of the access data, and a set of secondary attributes of the access data.

3. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 2, characterized in that, The process of obtaining the data to be encrypted includes: based on the set of secondary attributes of the data accessed in the access request, and according to a predefined secondary attribute access strategy, selecting all data units whose secondary attributes satisfy the strategy from the data source as the data to be encrypted.

4. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 1, characterized in that, The spatial distance is calculated by measuring the Haversine distance between the geographical location of each piece of data to be encrypted and the geographical location of the data accessed in the access request.

5. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 1, characterized in that, The evaluation coefficients are the result of a positive fusion of temporal and spatial credibility.

6. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 5, characterized in that, The specific process of the forward fusion is as follows: based on the preset first weight and the preset second weight, the time credibility and spatial credibility are weighted and summed to obtain the evaluation coefficient of each data to be encrypted; wherein, the preset first weight is less than the preset second weight, and the sum of the preset first weight and the preset second weight is 1.

7. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 1, characterized in that, No. Each piece of data to be encrypted corresponds to the adjusted control parameters. The calculation formula is: ,in, To preset the initial control parameters, For the first Each piece of data to be encrypted corresponds to a normalized evaluation coefficient. This is the preset adjustment range.

8. The security operation and maintenance monitoring method based on local target CIM model analysis as described in claim 1, characterized in that, The step of encrypting the plaintext data of the data to be encrypted to obtain ciphertext data includes: generating a chaotic sequence using the Logistic chaotic mapping algorithm based on the adjusted control coefficients corresponding to each data to be encrypted; generating a key using the chaotic sequence; and obtaining the ciphertext data by performing an XOR operation on the key and the plaintext data of each data to be encrypted.

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