Transformer area sensor data state detection method and device and electronic equipment

Abstract

The invention discloses a transformer area sensor data state detection method, a transformer area sensor data state detection device and electronic equipment. The method comprises the steps of obtaining a sensor deployment scheme based on transformer area basic information; based on the sensor deployment scheme, detecting whether the circulation state of target sensor data is safe; under the condition that the circulation state is safe, detecting whether the access state of the target sensor data is safe or not; under the condition that the access state is safe, detecting whether the service use state of the target sensor data is safe or not; and under the condition that the service use state is not safe, performing safety processing on the target sensor data. According to the invention, the technical problems of incomplete and unsafe transformer area data detection are solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a method, a device and an electronic device for detecting data status of a substation sensor. Background Art

[0002] In related technologies, sensor data is usually managed after it is stored in the database. However, with the development of smart grid technology, the amount of data in the substation has increased dramatically. Managing the sensor data after it is stored in the database will result in low efficiency, low service quality, and threaten the security of the substation data.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and electronic device for detecting the status of data of a station area sensor, so as to at least solve the technical problems of incomplete and unsafe data detection in the station area.

[0005] According to one aspect of an embodiment of the present invention, a method for detecting the status of sensor data in a substation is provided, comprising: obtaining a sensor deployment plan based on basic information of the substation; detecting whether the flow status of target sensor data is safe based on the sensor deployment plan; if the flow status is safe, detecting whether the access status of the target sensor data is safe; if the access status is safe, detecting whether the business usage status of the target sensor data is safe; if the business usage status is unsafe, securely processing the target sensor data.

[0006] Optionally, obtaining a sensor deployment plan based on basic information of the area includes: obtaining the basic information of the area to obtain a basic information characteristic value of the area; comparing the basic information characteristic value with basic information characteristic values ​​of each area stored in a database to obtain a sensor deployment plan corresponding to the basic information characteristic value.

[0007] Optionally, detecting whether the flow status of target sensor data is safe based on the sensor deployment scheme includes: acquiring a flow status data set of the target sensor data based on the sensor deployment scheme; obtaining a flow status evaluation value of the target sensor data based on the flow status data set; comparing the flow status evaluation value with a flow status boundary evaluation value of the sensor data stored in a database; determining that the flow status of the target sensor data is safe when the flow status evaluation value is greater than or equal to the flow status boundary evaluation value; and / or determining that the flow status of the target sensor data is unsafe when the flow status evaluation value is less than the flow status boundary evaluation value.

[0008] Optionally, when the circulation state is safe, detecting whether the access state of the target sensor data is safe includes: when the circulation state is safe, acquiring an access state data set of the target sensor data; obtaining an access state evaluation value of the target sensor data based on the access state data set; comparing the access state evaluation value with an access state boundary evaluation value of the sensor data stored in a database; determining that the access state of the target sensor data is safe when the access state evaluation value is greater than or equal to the access state boundary evaluation value; and / or determining that the access state of the target sensor data is unsafe when the access state evaluation value is less than the access state boundary evaluation value.

[0009] Optionally, when the access status is safe, detecting whether the business usage status of the target sensor data is safe includes: when the access status is safe, obtaining a business usage status data set of the target sensor data; based on the business usage status data set, obtaining a business usage status evaluation value of the target sensor data; comparing the business usage status evaluation value with a business usage status definition evaluation value of the sensor data stored in a database; when the business usage status evaluation value is greater than or equal to the business usage status definition evaluation value, determining that the business usage status of the target sensor data is safe; and / or, when the business usage status evaluation value is less than the business usage status definition evaluation value, determining that the business usage status of the target sensor data is unsafe, and issuing a risk warning for the business usage status.

[0010] Optionally, when the business usage status is unsafe, securely processing the target sensor data includes: when the business usage status is unsafe, obtaining a business usage status comparison feature value of the target sensor data based on the business usage status evaluation value; comparing the business usage status comparison feature value with each business usage status comparison feature value stored in a database to obtain a data risk solution corresponding to the business usage status comparison feature value; comparing the target sensor data with sensor data with risks in each business usage status stored in a database to obtain a specific responsible person and a leakage date corresponding to the target sensor data; and securely processing the target sensor data based on the data risk solution, the specific responsible person and the leakage date.

[0011] According to another aspect of the present invention, a device for detecting the status of sensor data in a substation is provided, comprising: an acquisition module for acquiring a sensor deployment plan based on basic information of the substation; a first detection module for detecting whether the flow status of target sensor data is safe based on the sensor deployment plan; a second detection module for detecting whether the access status of the target sensor data is safe if the flow status is safe; a third detection module for detecting whether the business usage status of the target sensor data is safe if the access status is safe; and a processing module for securely processing the target sensor data if the business usage status is unsafe.

[0012] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for detecting the data status of a station area sensor.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned methods for detecting data status of a station area sensor when running.

[0014] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the methods for detecting data status of a station area sensor are implemented.

[0015] In an embodiment of the present invention, a method of full life cycle management of substation data is adopted. By detecting the status of sensor data in the substation, whether the status of the sensor data is safe, and issuing a risk warning for unsafe status, the purpose of ensuring data security and improving data flow efficiency is achieved, thereby achieving the technical effect of timely discovering security vulnerabilities, timely identifying security issues, timely responding to and resolving security incidents, and improving the data security protection capabilities of the power system, thereby solving the technical problems of incomplete and unsafe substation data detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a schematic diagram of a method for detecting data status of a station area sensor according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a power safety protection method based on data full life cycle management according to an optional implementation mode of the present invention;

[0019] Figure 3 It is a structural block diagram of a device for detecting data status of a station area sensor according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0023] Substation: In the power system, a substation refers to a specific geographical area or user group that is supplied by a single transformer or a group of connected transformers. In the power grid structure, a substation is one of the basic units of power distribution. It connects the high-voltage grid and the end user and is responsible for converting high-voltage electricity into low-voltage electricity suitable for household, commercial or industrial use.

[0024] Flow status: Flow status refers to the state of data during transmission from the source to the destination, focusing on the security and efficiency of data transmission.

[0025] Access status: Access status focuses on the status of the data access system or network, and evaluates the efficiency and security of the data access process.

[0026] Business usage status: Business usage status focuses on the application of data in specific business scenarios, ensuring that the data processing process meets business requirements and is safe and controllable.

[0027] With the development of smart grid technology, the amount of data in substations has increased dramatically. The large amount of diverse data has brought challenges in storage, management and protection. Especially in ensuring data security, substations are increasingly relying on network communications and data exchange. The advancement of information technology and communication technology has provided new possibilities and tools for data management in substations. By comprehensively managing the generation, storage, and use of power data, substations can be effectively protected from internal and external threats, while improving the operating efficiency and service quality of substations.

[0028] In the relevant technologies, there are still some deficiencies in the research on power safety protection methods based on data life cycle management, which are specifically reflected in the single data collection and processing methods of traditional substations, and the inflexible and inefficient data management methods, which make data integration difficult and make it impossible to achieve seamless flow and comprehensive analysis of data. Due to the incompleteness of data security monitoring methods, the overly single data processing methods, and the inflexibility of data management methods, the rapid response ability of substations to changing conditions is limited, thus affecting the stability and efficiency of power supply. Due to the difficulty of data integration and the inability to achieve seamless flow of data, information islands may be generated, that is, key data cannot be effectively shared and utilized in the system, which not only reduces the quality of decision-making, but also makes the system more vulnerable to external threats. In short, incomplete data security monitoring may lead to failure to detect or respond to security threats in a timely manner. In order to make up for the problems caused by failure to detect or respond to security threats in a timely manner, more resources need to be invested to deal with system problems and data errors, resulting in increased operation and maintenance costs.

[0029] In response to the above problems, method 1 is a full life cycle monitoring system for power grid equipment based on shared services, including a network communication security monitoring module, a hardware equipment security monitoring module, a data security monitoring module, and an operation and maintenance security monitoring module. The network communication security monitoring module is used to monitor the network communication security of power grid equipment, the hardware equipment security monitoring module is used to monitor the hardware security in power grid equipment, the data security monitoring module is used to monitor the data security of power grid equipment, and the operation and maintenance security monitoring module is used to monitor the operation and maintenance security of power grid equipment. This method can monitor the security status of power grid equipment throughout its life cycle from the aspects of hardware and data security, realize unmanned monitoring, improve work efficiency, and actively protect the security of equipment.

[0030] Another method 2 is a full life cycle management system for power transmission and transformation equipment based on Internet of Things technology, the management system consists of electronic tags, on-site monitoring and measuring devices and management system software; the electronic tags are installed on the power transmission and transformation equipment; the on-site monitoring and measuring devices integrate RFID identification, GPS all-weather real-time positioning, wireless communication, full-state quantity measurement, information security, industrial control, etc., to perform real-time identification and tracking of power transmission and transformation equipment, operation data collection, intelligent diagnosis and status alarm, and the on-site monitoring and measuring devices and management system software exchange real-time data through Ethernet and other communication methods; the real-time monitoring of power transmission and transformation equipment is realized through various modules of the management system software. This method realizes the full life cycle management of power transmission and transformation equipment, makes the entire life cycle of power equipment transparent, will improve the efficiency of equipment management, improve the transparency of power asset operation and data integrity, and give full play to the use efficiency of power equipment.

[0031] Therefore, method 1 emphasizes the full life cycle security monitoring of power grid equipment, focusing on the security of hardware, data, operation and maintenance, etc. It is a system-level security monitoring, focusing on the security protection of hardware and network communications; method 2 emphasizes the application of Internet of Things technology in the full life cycle management of power transmission and transformation equipment, focusing on real-time monitoring of equipment status and data collection. However, neither method 1 nor method 2 involves data security or sensor data flow analysis, but tends to be intelligent in equipment operation management and status monitoring. In this regard, an embodiment of the present invention provides a substation sensor data status detection method, which focuses on the full life cycle security management of sensor data, especially the security of data flow, access and use, involves data security traceability management, and focuses on the security and integrity of sensor-generated data.

[0032] Based on this, an embodiment of the present invention provides a method for detecting the status of substation sensor data. By monitoring the full life cycle status of substation sensor data, risk warnings are promptly issued for stages where risks exist. After confirming that the previous stage is safe, status detection for subsequent stages is carried out, and data in an unsafe business usage status is securely processed. Risk solutions are determined, specific responsible persons and leakage dates are traced, and risks are resolved in a timely manner.

[0033] According to an embodiment of the present invention, an embodiment of a method for detecting the data status of a station area sensor is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] Figure 1 is a schematic diagram of a method for detecting data status of a station area sensor according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0035] Step S102: acquiring a sensor deployment plan based on the basic information of the station area.

[0036] As an optional embodiment, the execution subject of the method of this embodiment can be a terminal or server for processing the area sensor data. It should be noted that the above-mentioned terminal can be of various types, for example, it can be a mobile terminal with certain computing capabilities, or it can be a fixed computer device with recognition capabilities, etc. The above-mentioned server can also be of various types, for example, it can be a local server or a virtual cloud server. The server can be a single computer device or a computer cluster in which multiple computer devices are integrated according to computing capabilities.

[0037] As an optional embodiment, when obtaining a sensor deployment plan based on the basic information of the substation, multiple methods can be used. For example, the basic information of the substation can be obtained first to obtain the basic information characteristic value of the substation; then the basic information characteristic value and the basic information characteristic value of each substation stored in the database are compared to obtain the sensor deployment plan corresponding to the basic information characteristic value. Among them, the basic information can be stored in a basic information data set, and the basic information characteristic value can be obtained according to the basic information data set. The basic information data set can include one or more information data, such as the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation. For another example, the sensor deployment plan generation model can be trained based on the historical substation basic information and historical sensor deployment plans, and the basic information of the substation can be input into the sensor deployment plan generation model to obtain the output of the sensor deployment plan. The method of obtaining the sensor deployment plan using the basic information of the substation can be combined with the actual situation and actual needs of the substation, so that the sensor is deployed in a more needed position, which can make the data collected by the sensor more accurate and play a greater role.

[0038] As an optional embodiment, when the basic information of the above-mentioned substation is first obtained to obtain the basic information characteristic value of the above-mentioned substation; and then the basic information characteristic value of the above-mentioned substation is compared with the basic information characteristic value of each substation stored in the database to obtain the sensor deployment scheme corresponding to the basic information characteristic value, there are many ways to obtain the basic information of the above-mentioned substation and obtain the basic information characteristic value of the above-mentioned substation. For example, the basic information of the substation composed of the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation can be used to obtain the basic information characteristic value of the substation. When the basic information of the substation includes the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation, the basic information characteristic value of the substation can also be obtained in a variety of ways. For example, the basic information characteristic value can be calculated by the following formula:

[0039]

[0040] In the formula, is the basic information characteristic value of the substation, bd is the substation voltage level, sb is the total length of the substation power supply line, md is the substation area, ε1 is the compensation factor of the set substation voltage level, ε2 is the compensation factor of the set total length of the substation power supply line, and ε3 is the compensation factor of the set substation area.

[0041] Step S104: Based on the above sensor deployment solution, detect whether the flow status of the target sensor data is safe.

[0042] As an optional embodiment, when detecting whether the flow state of the target sensor data is safe based on the above sensor deployment scheme, multiple methods can be used. For example, firstly based on the above sensor deployment scheme, obtain the flow state data set of the above target sensor data; then based on the above flow state data set, obtain the flow state evaluation value of the above target sensor data; compare the above flow state evaluation value with the flow state definition evaluation value of the sensor data stored in the database; when the above flow state evaluation value is greater than or equal to the above flow state definition evaluation value, determine that the above flow state of the above target sensor data is safe; and / or, when the above flow state evaluation value is less than the above flow state definition evaluation value, determine that the above flow state of the above target sensor data is unsafe, and issue a risk warning for the above flow state. Among them, according to the above sensor deployment scheme, the sensor position can be deployed more optimally, sensor data can be obtained from the deployed sensors, and the above flow state can be detected during the flow of sensor data. The above flow state data set can include one or more information data, for example, the time it takes for the sensor data to flow to the next node, the sensor data flow packet loss rate, the sensor data flow network bandwidth, etc. By detecting whether the flow status of the above-mentioned target sensor data is safe, risks can be promptly prompted when problems occur in the flow, and the operation and maintenance personnel can take measures based on the prompt information to eliminate obstacles in the flow process and ensure the normal flow of sensor data, thereby ensuring the security and efficiency of sensor data in the flow.

[0043] As an optional embodiment, when obtaining the flow state evaluation value of the above-mentioned target sensor data according to the above-mentioned flow state data set, a variety of methods can be used. For example, the flow state evaluation value of the above-mentioned target sensor data can be obtained based on the time it takes for the sensor data to flow to the next node, the sensor data flow packet loss rate, and the sensor data flow network bandwidth in the above-mentioned flow state data set. When the above-mentioned flow state data set includes the time it takes for the sensor data to flow to the next node, the sensor data flow packet loss rate, and the sensor data flow network bandwidth, a variety of methods can also be used to obtain the flow state evaluation value of the above-mentioned target sensor data. For example, the following formula can be used to calculate the above-mentioned flow state evaluation value, and the role of the time it takes for the sensor data to flow to the next node, the sensor data flow packet loss rate, and the sensor data flow network bandwidth in the process of sensor data flow can be comprehensively analyzed to obtain a comprehensive indicator to evaluate the flow state:

[0044]

[0045] Wherein, θ is the evaluation value of the sensor data flow state, sc is the time it takes for the sensor data to flow to the next node, bl is the packet loss rate of the sensor data flow, dk is the network bandwidth of the sensor data flow, μ1 is the compensation factor for the set time it takes for the sensor data to flow to the next node, μ2 is the compensation factor for the set packet loss rate of the sensor data flow, and μ3 is the compensation factor for the set network bandwidth of the sensor data flow.

[0046] Step S106: When the above-mentioned flow status is safe, detecting whether the access status of the above-mentioned target sensor data is safe.

[0047] As an optional embodiment, when the above-mentioned flow state is safe, when detecting whether the access state of the above-mentioned target sensor data is safe, multiple methods can be used. For example, first, when the above-mentioned flow state is safe, obtain the access state data set of the above-mentioned target sensor data; then, based on the above-mentioned access state data set, obtain the access state evaluation value of the above-mentioned target sensor data; compare the above-mentioned access state evaluation value with the access state definition evaluation value of the sensor data stored in the database; when the above-mentioned access state evaluation value is greater than or equal to the above-mentioned access state definition evaluation value, determine that the above-mentioned access state of the above-mentioned target sensor data is safe; and / or, when the above-mentioned access state evaluation value is less than the above-mentioned access state definition evaluation value, determine that the above-mentioned access state of the above-mentioned target sensor data is unsafe, and issue a risk warning for the above-mentioned access state. Among them, the above-mentioned access state data set may include one or more information data, such as the access response time of the sensor data, the repetition ratio of the access inspection data, the access packet loss rate, etc. By detecting the above-mentioned access state and issuing a risk warning for the unsafe access state, abnormal access data flow can be discovered and processed in time, so that the sensor data can perform the access step normally and efficiently, and the security of the access state is guaranteed.

[0048] As an optional embodiment, when obtaining the access status evaluation value of the above-mentioned target sensor data based on the above-mentioned access status data set, a variety of methods can be used. For example, the above-mentioned access status evaluation value can be obtained based on the access response duration of the sensor data included in the above-mentioned access status data set, the access check data repetition ratio, and the access packet loss rate. When the above-mentioned access status data set includes the access response duration of the sensor data, the access check data repetition ratio, and the access packet loss rate, the above-mentioned access status evaluation value can also be obtained in a variety of ways. For example, in order to fully utilize the above-mentioned three types of data and obtain a comprehensive access status evaluation value, the following formula can be used for calculation:

[0049]

[0050] Wherein, γ is the evaluation value of the sensor data access status, xy is the sensor data access response time, cf is the sensor data access inspection data duplication ratio, db is the sensor data access packet loss rate, σ1 is the compensation factor of the set sensor data access response time, σ2 is the compensation factor of the set sensor data access inspection data duplication ratio, σ3 is the compensation factor of the set sensor data access packet loss rate, and e is a natural constant.

[0051] Step S108: When the access status is safe, detecting whether the service usage status of the target sensor data is safe.

[0052] As an optional embodiment, when the above-mentioned access status is safe, a variety of methods can be used to detect whether the business usage status of the above-mentioned target sensor data is safe. For example, first, when the above-mentioned access status is safe, obtain the business usage status data set of the above-mentioned target sensor data; then, based on the above-mentioned business usage status data set, obtain the business usage status evaluation value of the above-mentioned target sensor data; compare the above-mentioned business usage status evaluation value with the business usage status definition evaluation value of the sensor data stored in the database; when the above-mentioned business usage status evaluation value is greater than or equal to the above-mentioned business usage status definition evaluation value, determine that the above-mentioned business usage status of the above-mentioned target sensor data is safe; and / or, when the above-mentioned business usage status evaluation value is less than the above-mentioned business usage status definition evaluation value, determine that the above-mentioned business usage status of the above-mentioned target sensor data is unsafe, and issue a risk warning for the above-mentioned business usage status. By detecting the business usage status and issuing a risk warning, it is possible to issue a warning before the business usage problem develops, so as to facilitate timely adoption of problem solutions to deal with risks and avoid affecting the circulation of sensor data. It can also protect sensor data from being damaged during business use and ensure the accuracy and integrity of the data.

[0053] As an optional embodiment, when obtaining the business usage status evaluation value of the above-mentioned target sensor data based on the above-mentioned business usage status data set, a variety of methods can be used. For example, the above-mentioned business usage status evaluation value can be obtained based on the modification frequency, abnormal access frequency, and number of operation failures of the sensor data included in the above-mentioned business usage status data set during the business usage process. When the above-mentioned business usage status data set includes the modification frequency, abnormal access frequency, and number of operation failures of the sensor data during the business usage process, a variety of methods can also be used to obtain the above-mentioned business usage status evaluation value. For example, the following formula can be used to calculate the above-mentioned business usage status evaluation value:

[0054]

[0055] Wherein, δ is the evaluation value of the sensor data service usage status, xg is the modification frequency of the sensor data service usage process, yc is the abnormal access frequency of the sensor data service usage process, sb is the number of operation failures in the sensor data service usage process, τ1 is the compensation factor of the set modification frequency of the sensor data service usage process, τ2 is the compensation factor of the set abnormal access frequency of the sensor data service usage process, and τ3 is the compensation factor of the set number of operation failures in the sensor data service usage process.

[0056] Step S110: When the service usage status is unsafe, the target sensor data is securely processed.

[0057] As an optional embodiment, when the above-mentioned business usage status is unsafe, a variety of methods can be used to perform security processing on the above-mentioned target sensor data. For example, when the above-mentioned business usage status is unsafe, based on the above-mentioned business usage status evaluation value, first obtain the business usage status comparison feature value of the above-mentioned target sensor data; compare the above-mentioned business usage status comparison feature value with each business usage status comparison feature value stored in the database to obtain the data risk solution corresponding to the above-mentioned business usage status comparison feature value; then compare the above-mentioned target sensor data with the sensor data with risks in each business usage status stored in the database to obtain the specific person responsible and the leakage date corresponding to the above-mentioned target sensor data; based on the above-mentioned data risk solution, the above-mentioned specific person responsible and the above-mentioned leakage date, perform security processing on the above-mentioned target sensor data. The above-mentioned security processing may be to simply cut off the communication with the sensor with risks and no longer receive the data collected by the sensor, or to trace back the nodes where the risks exist and solve the risks, or to combine the above-mentioned two methods or adopt other methods. By securely processing sensor data that is not secure for business use and identifying the specific responsible persons and the date of leakage, we can quickly identify the link where the problem exists, reduce the losses caused by the risk, remedy the risk, and improve the security and accuracy of data use.

[0058] As an optional embodiment, when obtaining the business usage status comparison feature value of the above-mentioned target sensor data based on the above-mentioned business usage status evaluation value, a variety of methods can be used. For example, for sensor data with unsafe business usage status, its corresponding business usage status evaluation value can be imported into the business usage status comparison model, and then the above-mentioned business usage status comparison feature value can be obtained, and then the sensor data can be safely traced and managed according to the above-mentioned business usage status comparison feature value. Among them, the above-mentioned business usage status comparison model can adopt a variety of methods to establish a variety of different models. For example, the business usage status comparison model can be obtained based on experience, or the business usage status comparison model can be obtained based on the way a neural network learns historical data. Through the business usage status comparison model, the business usage status comparison feature value can be obtained, which is convenient for comparison with the historical situation in the database, so as to quickly derive a risk handling plan. For example, the business usage status comparison model can be expressed by the following formula:

[0059]

[0060] Where β is the service usage status comparison feature value, δ is the service usage status evaluation value of the sensor generated data, and e is a natural constant.

[0061] Through the above steps, a better sensor deployment plan can be obtained to ensure that sensors can efficiently collect useful data. It can also detect that sensor data can be safely transmitted and used throughout the entire life cycle, including the circulation process, access process, and business use process. That is, the security of the entire process from data generation and transmission to business application is ensured, thereby optimizing the operating efficiency of the power system and improving the professionalism and reliability of data management.

[0062] In combination with the above embodiment and the optional embodiment, an optional implementation is provided. In this optional implementation, a power safety protection method based on data full life cycle management is provided. Figure 2 is a schematic diagram of a power safety protection method based on data full life cycle management according to an optional implementation mode of the present invention, such as Figure 2 As shown, the method includes the following processing.

[0063] S1, analyze the basic information of the substation area and compare it to obtain the sensor deployment plan.

[0064] Specifically, the basic information of the substation is analyzed and compared to obtain the sensor deployment plan. The specific analysis process is: obtain the substation basic information data set, and based on the obtained substation basic information data set, obtain the substation basic information characteristic value through comprehensive analysis, and use the substation basic information characteristic value as the analysis basis for obtaining the sensor deployment plan by comparison; establish a mapping set of historical substation basic information characteristic values ​​and sensor deployment plans, compare the substation basic information characteristic values ​​with the mapping set of sensor deployment plans corresponding to the basic information characteristic values ​​of each substation stored in the database, and obtain the sensor deployment plan corresponding to the basic information characteristic value of the substation.

[0065] It needs to be explained that the above-mentioned acquisition of the basic information of the substation and its characteristic values ​​can more accurately determine which areas or equipment need to be monitored, ensure that sensors are concentrated at important nodes, make the monitoring results more accurate and reliable, identify the needs and bottlenecks of different substations, and enable the sensor deployment plan to be optimized according to actual needs, thereby effectively allocating resources and avoiding unnecessary investment. The correct sensor deployment plan can timely discover and warn of potential faults or safety hazards, reduce the risk of system failures, and improve the safety and stability of the entire substation. Without an appropriate sensor deployment plan, intelligent applications are difficult to achieve. The analysis of the characteristic values ​​of the substation will help promote the construction of intelligent monitoring and automated scheduling and improve the intelligence level of the substation. The characteristic values ​​of the substation provide an important data basis, which can be used in subsequent decision support systems to help managers make more reasonable decisions based on real monitoring information.

[0066] Furthermore, the basic information data set of the substation includes the substation voltage level, the total length of the substation power supply lines, and the substation area. The substation voltage level refers to the voltage standard or rated value of the transmission or distribution lines in a specific substation, which is obtained based on the transformer. The total length of the substation power supply lines refers to the total length of all power supply lines in the substation, which is obtained based on the total station and laser rangefinder. The substation area refers to the total area of ​​the substation. The Geographic Information System (GIS) technology can be used to perform spatial analysis on the substation and calculate the actual covered land area.

[0067] In a specific embodiment, a conductor with a larger cross-section can reduce line losses, thereby adapting to a longer power supply distance. The higher the voltage level, the longer the power supply line can theoretically be. The length of the power supply line in the substation is related to the area it covers. Substations with larger areas require longer power supply lines to meet the electricity needs of all users. The substation area and the length of the power supply line are positively correlated. Larger substations usually have higher demands for electricity, which may require the use of higher voltage levels to meet power supply needs, especially under heavy loads.

[0068] It should be explained that the above-mentioned basic information characteristic values ​​of the substation area are obtained in the following way:

[0069]

[0070] In the formula, is the basic information characteristic value of the substation, bd is the substation voltage level, sb is the total length of the substation power supply line, md is the substation area, ε1 is the compensation factor of the set substation voltage level, ε2 is the compensation factor of the set total length of the substation power supply line, and ε3 is the compensation factor of the set substation area.

[0071] It should be explained that the above-mentioned characteristic values ​​of basic information of the substation are calculated through the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation. The voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation are normalized. Understanding the area of ​​the substation can evaluate the structure and connectivity of the power network, which is crucial for system expansion and optimization planning. According to the basic information of the substation, equipment resources can be reasonably allocated to reduce redundant investment and improve overall operation efficiency. Through the characteristic values ​​of the substation, potential high-risk areas can be identified, and countermeasures can be formulated in advance to reduce the probability of failure, which provides a basis for subsequent data analysis and can be combined with other data for in-depth mining to support intelligent decision-making. The set compensation factors of the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation are obtained from the database. According to historical data, a mapping set of the historically measured voltage level of the substation, the total length of the power supply line of the substation, the area of ​​the substation and the compensation factors of the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation is established to obtain the compensation factors of the voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation corresponding to the current voltage level of the substation, the total length of the power supply line of the substation, and the area of ​​the substation.

[0072] S2, based on the sensor deployment plan, analyzes the sensor data flow status and determines whether the sensor data flow status is safe.

[0073] Specifically, sensors are used to obtain information on the power usage status of the substation, including current sensors, voltage sensors, power sensors, energy meters, temperature sensors, etc. The sensor data specifically includes line current, load current, number of overload current, line voltage, phase voltage, cumulative energy consumption, number of abnormal energy consumption, etc., which are all parameters related to the data security protection of the substation. The sensor data flow status is analyzed to determine whether the sensor data flow status is safe. The specific analysis process is: obtain a sensor data flow status data set, and based on the obtained sensor data flow status data set, obtain a sensor data flow status evaluation value through comprehensive analysis, and use the sensor data flow status evaluation value as an analysis basis for determining whether the sensor data flow status is safe; compare the sensor data flow status evaluation value with the sensor data flow status definition evaluation value stored in the database; if the sensor data flow status evaluation value is higher than or equal to the sensor data flow status definition evaluation value, then the sensor data flow status corresponding to the sensor data flow status evaluation value is safe; if the sensor data flow status evaluation value is lower than the sensor data flow status definition evaluation value, then the sensor data flow status corresponding to the sensor data flow status evaluation value is at risk, and a prompt is issued for the sensor data flow status at risk.

[0074] It should be explained that timely identification of potential risks can improve the safety of the system and avoid abnormalities in sensor data streams that may lead to further safety incidents. Early identification of risks allows necessary maintenance and repairs to be performed before problems expand, thereby reducing the maintenance cost of the entire system. Monitoring the data stream status of sensors allows measures to be taken before potential abnormal problems occur, thereby enhancing the reliability of the entire system. That is, monitoring the data stream status helps ensure the integrity and accuracy of the data generated by the sensors, thereby ensuring the rationality of subsequent data analysis and decision-making.

[0075] Furthermore, the sensor data flow status data set specifically includes the time it takes for sensor data to flow to the next node, the sensor data flow packet loss rate, and the sensor data flow network bandwidth. The time it takes for sensor data to flow to the next node refers to the time required from the sensor data to the data being sent to the next processing node (for example, a gateway, server, or other sensor). It can help evaluate the delay in data transmission and affect applications with high real-time requirements. It can be obtained based on a network analyzer. The packet loss rate refers to the ratio between the number of lost data packets and the total number of sent data packets during data transmission. A high packet loss rate will result in incomplete data, affecting analysis and decision-making. The traffic and packet loss statistics of the device can be obtained through the Simple Network Management Protocol (SNMP). Network bandwidth refers to the maximum amount of data that can be transmitted by the network in a specific time period, usually expressed in bits per second (bps), reflecting the data transmission capacity between the sensor and the next node. The monitoring interface of the network device or SNMP can be used to obtain the bandwidth utilization of the current network.

[0076] In a specific embodiment, bandwidth is the maximum data transmission rate that the network can support. If the bandwidth is higher, more data can be transmitted in a shorter time in theory. When the bandwidth is sufficient, the transmission time is usually shorter. When the network is overloaded or the bandwidth is insufficient, data packets may be discarded, resulting in an increased packet loss rate. The retransmission of data will increase the overall transmission time. When the bandwidth is too low or the network is congested, the packet loss rate will usually increase. That is, if the bandwidth is sufficient and the network is relatively stable, the packet loss rate will be relatively low.

[0077] It should be explained that by analyzing the sensor data flow status and judging whether the sensor data flow status is safe, the security issues that may be encountered during data transmission, such as unauthorized access, data leakage or tampering, can be identified in a timely manner, which helps enterprises to prevent these threats in advance and take corresponding security measures to protect the core data of the station area from attack. It can also detect whether data is lost or errors occur during transmission or processing, which helps to conduct effective data analysis and correct decision-making, and can discover bottlenecks or efficiency problems in data processing and transmission, so as to optimize the data architecture or upgrade the system in a targeted manner and improve the overall data processing efficiency.

[0078] It should be explained that the above sensor data flow state evaluation value is obtained in the following way:

[0079]

[0080] Wherein, θ is the evaluation value of the sensor data flow state, sc is the time it takes for the sensor data to flow to the next node, bl is the packet loss rate of the sensor data flow, dk is the network bandwidth of the sensor data flow, μ1 is the compensation factor for the set time it takes for the sensor data to flow to the next node, μ2 is the compensation factor for the set packet loss rate of the sensor data flow, and μ3 is the compensation factor for the set network bandwidth of the sensor data flow.

[0081] It should be explained that the above sensor data flow status evaluation value is calculated by the time it takes for the sensor data to flow to the next node, the packet loss rate of the sensor data flow, and the network bandwidth of the sensor data flow, and the time it takes for the sensor data to flow to the next node, the packet loss rate of the sensor data flow, and the network bandwidth of the sensor data flow are normalized. Monitoring the data flow time and packet loss rate can timely discover network bottlenecks or faults, quickly locate problem nodes, and evaluate the performance of data flow. It can identify links with insufficient performance and provide a basis for optimizing network architecture and sensor layout. Monitoring the changing trend of the packet loss rate can take measures to reduce data packet loss, improve the reliability of data transmission, evaluate bandwidth usage, identify underutilized resources, and make reasonable configurations to avoid unnecessary waste of resources. Longer time and higher packet loss rate can indicate the existence of network attacks or malicious behaviors, and potential security threats can be discovered in a timely manner. The set compensation factors for the time it takes for sensor data to flow to the next node, the packet loss rate of sensor data flow, and the network bandwidth of sensor data flow are obtained from a database, and a mapping set of the historically measured time it takes for sensor data to flow to the next node, the packet loss rate of sensor data flow, and the network bandwidth of sensor data flow and the compensation factors for the time it takes for sensor data to flow to the next node, the packet loss rate of sensor data flow, and the network bandwidth of sensor data flow is established based on historical data to obtain the compensation factors for the time it takes for sensor data to flow to the next node, the packet loss rate of sensor data flow, and the network bandwidth of sensor data flow corresponding to the current time it takes for sensor data to flow to the next node, the packet loss rate of sensor data flow, and the network bandwidth of sensor data flow.

[0082] S3, based on the sensor data with secure flow status, analyzing the sensor data access status to determine whether the sensor data access status is secure.

[0083] Specifically, the sensor data access status is analyzed to determine whether the sensor data access status is safe. The specific analysis process is: obtain the sensor data access status data set, which specifically includes the sensor data access response time, the sensor data access check data duplication ratio, and the sensor data access packet loss rate. Among them, the response time refers to the time interval from the sensor sending the access request to the successful access, which is used to evaluate the delay of data access and the efficiency of data processing. It can be obtained using network monitoring tools, analysis software or the sensor's own logging function. The duplication ratio of the check data refers to the ratio of the repeated data packets sent by the sensor to the total number of data packets within a certain period of time. A high duplication ratio may indicate a data transmission problem or a fault in the sensor itself. The repeated data packets in the data stream can be detected by data processing and analysis tools, such as data recorders, edge computing devices or cloud servers. The packet loss rate refers to the ratio of the data packets lost during the data access process to the total number of data packets. The packet loss phenomenon usually leads to incomplete data, which in turn affects subsequent data analysis and decision-making. The data packet access situation can be monitored by a network analyzer, a test instrument or the communication module inside the sensor.

[0084] It should be explained that if the packet loss rate is high, it means that some data packets failed to reach the destination during the data access process, resulting in the need to resend the data packets. Therefore, when packet loss occurs, the response time may increase, because the system needs to wait for the resent data packets and process these repeated requests, which prolongs the overall response time. At the same time, the increase in response time may cause an increase in the data duplication ratio. If the system fails to receive the data packet in time, the sender may repeatedly send the access request, resulting in the generation of duplicate data. If the system is relatively stable and the response time is short, the duplication ratio is usually low. A high duplication ratio may be one of the manifestations of a high packet loss rate. When the system detects that some data packets have not been confirmed to be received, it triggers the retransmission mechanism, resulting in the generation of duplicate data. If the packet loss rate is low, it means that the data access status is stable and the duplication ratio is generally low.

[0085] Based on the acquired sensor data access status data set, a sensor data access status evaluation value is obtained through comprehensive analysis, and the sensor data access status evaluation value is used as an analysis basis for judging whether the sensor data access status is safe; the sensor data access status evaluation value is compared with the sensor data access status definition evaluation value stored in the database; if the sensor data access status evaluation value is higher than or equal to the sensor data access status definition evaluation value, the sensor data access status corresponding to the sensor data access status evaluation value is safe; if the sensor data access status evaluation value is lower than the sensor data access status definition evaluation value, the sensor data access status corresponding to the sensor data access status evaluation value is at risk, and a prompt is issued for the sensor data access status at risk.

[0086] It should be explained that the above-mentioned monitoring access status (including response time, duplicate data ratio, and packet loss rate) can timely detect and handle abnormal data flows, thereby improving the reliability and integrity of sensor data. For sensor data access status with risks, timely prompts can quickly initiate corresponding emergency measures to prevent potential problems from spreading, thereby reducing the probability of data loss or system failure. Analyzing data access status and identifying risky sensors can more effectively configure network and computing resources, ensure that key equipment obtains necessary support, and improve overall system performance. For sensor data access status with risks, timely prompts can help operation and maintenance personnel conduct further security checks, identify possible network attacks or malicious behaviors, and enhance system security. By evaluating the sensor access status, managers can obtain data-driven insights and reports to help them make effective decisions and optimize operating processes, thereby improving overall efficiency.

[0087] Furthermore, the sensor data access status evaluation value is obtained in the following way:

[0088]

[0089] Wherein, γ is the evaluation value of the sensor data access status, xy is the sensor data access response time, cf is the sensor data access inspection data duplication ratio, db is the sensor data access packet loss rate, σ1 is the compensation factor of the set sensor data access response time, σ2 is the compensation factor of the set sensor data access inspection data duplication ratio, σ3 is the compensation factor of the set sensor data access packet loss rate, and e is a natural constant.

[0090] It should be explained that the above sensor data access status evaluation value is calculated through the sensor data access response time, the sensor data access check data duplication ratio, and the sensor data access packet loss rate. It is necessary to normalize the sensor data access response time, the sensor data access check data duplication ratio, and the sensor data access packet loss rate. Monitoring the sensor access response time can identify and optimize the delay in the data access process and improve the overall efficiency of data collection. Monitoring the duplication ratio of check data helps to identify data redundancy, ensure the uniqueness and accuracy of the data set, and improve the overall quality of the data. A comprehensive evaluation of the access status helps to make effective decisions on resource allocation, reasonably configure bandwidth and computing resources, and avoid idle resources. By monitoring the response time and packet loss rate, potential system failures or performance degradation can be discovered in a timely manner, so that timely measures can be taken to repair them. By analyzing the historical data access situation, data support can be provided for future system expansion and resource planning to ensure the sustainable development of the system. The set sensor data access response time, sensor data access inspection data repetition ratio, and sensor data access packet loss rate compensation factors are obtained from a database, and a mapping set of historically measured sensor data access response time, sensor data access inspection data repetition ratio, sensor data access packet loss rate and sensor data access response time, sensor data access inspection data repetition ratio, and sensor data access packet loss rate compensation factors is established based on historical data to obtain the sensor data access response time, sensor data access inspection data repetition ratio, and sensor data access packet loss rate compensation factors corresponding to the current sensor data access response time, sensor data access inspection data repetition ratio, and sensor data access packet loss rate.

[0091] S4, based on the sensor data with secure access status, analyzing the sensor data service usage status to determine whether the sensor data service usage status is secure.

[0092] Specifically, the sensor data service usage status is analyzed to determine whether the sensor data service usage status is safe. The specific analysis process is: obtain the sensor data service usage status data set, which specifically includes the modification frequency of the sensor data service usage process, the abnormal access frequency of the sensor data service usage process, and the number of operation failures in the sensor data service usage process. Among them, the modification frequency refers to the number of times the data generated by the sensor is modified or updated within a certain period of time, which may include the adjustment of configuration parameters, correction or update of data, etc., and can be tracked through a data management system (such as a database management system) or sensor management software. The abnormal access frequency refers to the number of abnormal access events that occur within a certain period of time, such as unauthorized access attempts, malformed data requests, etc., which imply potential security threats or system failures, and can be collected through security monitoring systems (such as firewalls, intrusion detection systems) and application logs (such as application programming interface (API) access logs, operation logs). The number of operation failures refers to the number of times the operation is not successfully executed due to various reasons within a certain period of time, such as device failure, network problems, and incorrect data format, which can be obtained through application logs, operation logs or sensor data recording devices.

[0093] In a specific embodiment, if the modification frequency is high, it means that frequent updates or adjustments are being made, which may lead to a certain degree of instability, because under high-frequency modifications, the system configuration may be wrong, causing operation failure. An increase in the frequency of abnormal access usually means that there are potential security threats or system vulnerabilities, which may cause operation failures. Unauthorized access attempts or malicious requests may fail due to being blocked by system security mechanisms. High-frequency modifications may attract malicious attackers who attempt to obtain unauthorized access rights or affect system stability through abnormal access policies. Especially when it comes to the modification of sensitive data, the security of the system will receive more attention and the frequency of abnormal access may increase.

[0094] Based on the acquired sensor data service usage status data set, a sensor data service usage status evaluation value is obtained through comprehensive analysis, and the sensor data service usage status evaluation value is used as an analysis basis for judging whether the sensor data service usage status is safe; the sensor data service usage status evaluation value is compared with the sensor data service usage status definition evaluation value stored in the database; if the sensor data service usage status evaluation value is higher than or equal to the sensor data service usage status definition evaluation value, then the sensor data service usage status corresponding to the sensor data service usage status evaluation value is safe; if the sensor data service usage status evaluation value is lower than the sensor data service usage status definition evaluation value, then the sensor data service usage status corresponding to the sensor data service usage status evaluation value is at risk, and a prompt is issued for the sensor data service usage status at risk.

[0095] It should be explained that the above-mentioned operation of issuing risk warnings can alert the operation and maintenance team in advance before the problem occurs, prompting them to take timely measures to solve potential problems and prevent greater impact on the normal operation of the system. Early identification of abnormal conditions in sensors can help avoid system failures, thereby reducing the time and maintenance costs of unexpected downtime and improving system availability. Monitoring and prompting potential risks can prevent data loss or damage caused by abnormal access, operation failures, etc., thereby ensuring the integrity and accuracy of the data and laying the foundation for subsequent data analysis. After issuing risk warnings, the operation and maintenance team can focus on solving high-risk areas, optimize resource allocation, improve the efficiency of troubleshooting, and avoid blindly handling all problems. Risk warnings can also provide important information basis for management, helping them consider safety and potential risks when making relevant management decisions and enhance the scientific nature of decision-making.

[0096] Specifically, the sensor data service usage status evaluation value is obtained in the following way:

[0097]

[0098] Wherein, δ is the evaluation value of the sensor data service usage status, xg is the modification frequency of the sensor data service usage process, yc is the abnormal access frequency of the sensor data service usage process, sb is the number of operation failures in the sensor data service usage process, τ1 is the compensation factor of the set modification frequency of the sensor data service usage process, τ2 is the compensation factor of the set abnormal access frequency of the sensor data service usage process, and τ3 is the compensation factor of the set number of operation failures in the sensor data service usage process.

[0099] It should be explained that the above sensor data service usage status evaluation value is calculated by the modification frequency of the sensor data service usage process, the abnormal access frequency of the sensor data service usage process, and the number of operation failures in the sensor data service usage process. It is necessary to normalize the modification frequency of the sensor data service usage process, the abnormal access frequency of the sensor data service usage process, and the number of operation failures in the sensor data service usage process. It helps to identify potential security issues or abnormal access behaviors, take timely measures to protect the system, and ensure the security and reliability of data processing. Monitoring the modification frequency can understand the trend of data changes, thereby discovering opportunities to optimize or adjust the data processing process and improve the efficiency of the business process. Identifying the causes of operation failures, especially frequently failed operations, can provide direction for process optimization, such as improving the user interface or increasing the system response speed. Timely handling of abnormal access can enhance user confidence in operations and provide more stable services. High-frequency abnormal access or failure times may become early warning signals of potential system failures, helping operation and maintenance personnel to promptly discover and solve problems and prevent serious system failures. According to the frequency of data use and abnormal conditions, resource allocation can be dynamically adjusted to ensure sufficient resources for key components, thereby improving overall performance. System performance monitoring data can be used as a continuous improvement process, prompting the team to regularly evaluate and improve data business processes, thereby improving overall system performance and service quality. The compensation factors for the set sensor data business use process modification frequency, sensor data business use process abnormal access frequency, and sensor data business use process operation failure times are obtained from the database. Based on historical data, a mapping set of historically measured sensor data business use process modification frequency, sensor data business use process abnormal access frequency, and sensor data business use process operation failure times and compensation factors for sensor data business use process modification frequency, sensor data business use process abnormal access frequency, and sensor data business use process operation failure times is established to obtain the compensation factors for the sensor data business use process modification frequency, sensor data business use process abnormal access frequency, and sensor data business use process operation failure times corresponding to the current sensor data business use process modification frequency, sensor data business use process abnormal access frequency, and sensor data business use process operation failure times.

[0100] S5, perform data security traceability management on sensor data with unsafe business usage status.

[0101] Specifically, data security traceability management is performed on sensor data whose business usage status is at risk; the business usage status assessment value of the sensor data whose business usage status is at risk is imported into the business usage status comparison model to obtain the business usage status comparison feature value, and the business usage status comparison feature value is used as the analysis basis for data security traceability management of sensor data whose business usage status is at risk.

[0102] The business usage status comparison model is obtained in the following way:

[0103]

[0104] Where β is the service usage status comparison feature value, δ is the service usage status evaluation value of the sensor generated data, and e is a natural constant.

[0105] The business usage status comparison feature value is compared with the data risk solutions corresponding to each business usage status comparison feature value stored in the database to obtain the data risk solution corresponding to the business usage status comparison feature value; the sensor data with risk in the business usage status is compared with the specific responsible persons and leakage dates corresponding to each business usage status risk sensor data stored in the database; the data risk solution, specific responsible person and leakage date corresponding to the business usage status comparison feature value are obtained, and the risk of sensor data with risk in the business usage status is resolved.

[0106] In a specific embodiment, the business usage status comparison model is used to determine the data risk solution, which can immediately trigger the predetermined response measures, improve the efficiency of risk response, reduce human intervention, and improve the accuracy and consistency of the analysis results. Data-based quantitative decision support enables more reasonable response plans and reasonable solutions to risk problems when facing complex data security issues. Identifying specific responsible persons can clarify who should be responsible for data leaks or risk events, which helps to formulate corresponding accountability mechanisms and ensure that relevant personnel are responsible for their actions. Comparing data can identify which sensors or business processes have higher risks, thereby helping management to conduct risk assessment and management. In the event of a data leak, the responsible person and the leak date can be quickly located, which helps to quickly take remedial measures and reduce potential losses. Regular data security traceability management can help organizations establish a mechanism for continuous improvement and improve the overall data security level.

[0107] It needs to be explained that the above-mentioned power safety protection method based on data full life cycle management is used to provide safety protection for data flow, data access, and data service usage process data in low voltage protection.

[0108] It should be explained that the above provides a power safety protection method based on data full life cycle management, and conducts detailed security analysis on each data generation and transmission stage, which can timely discover and repair potential security vulnerabilities and prevent security threats from spreading to other parts of the substation, thereby improving the safety protection level of the entire system. Analyzing the basic information of the substation and comparing the resulting sensor deployment plan can ensure the efficient deployment of sensors in key locations, optimize resource utilization, and improve the coverage and accuracy of data collection. Systematically analyzing the flow, access and use status of data generated by sensors can ensure the integrity and accuracy of data during transmission and storage, and improve the fault response rate.

[0109] It should be explained that by analyzing the sensor data flow status and judging whether the sensor data flow status is safe, the security issues that may be encountered during data transmission, such as unauthorized access, data leakage or tampering, can be identified in a timely manner, which helps enterprises to prevent these threats in advance and take corresponding security measures to protect the core data of the station area from attack. It can also detect whether data is lost or errors occur during transmission or processing, which is helpful for effective data analysis and correct decision-making. It can also find bottlenecks or efficiency problems in data processing and transmission, so as to optimize the data architecture or upgrade the system in a targeted manner and improve the overall data processing efficiency.

[0110] It needs to be explained that the above-mentioned data security traceability management of sensor data in an unsafe business usage status enables organizations to quickly track down the specific persons responsible for data security issues. It is the key to timely responding to and resolving security incidents and helps minimize damage. Traceability management provides the specific person responsible for the problem and the date of the leak, which is crucial for quickly formulating effective response measures and greatly improves the efficiency of handling security incidents. Traceability analysis of data security incidents can identify weak links in the existing security protection system. This continuous improvement and optimization helps to establish a more solid security protection barrier.

[0111] According to an embodiment of the present invention, a device for detecting data status of a station area sensor is provided. Figure 3 is a structural block diagram of a station area sensor data status detection device according to an embodiment of the present invention, such as Figure 3 As shown, the device includes: an acquisition module 202, a first detection module 204, a second detection module 206, a third detection module 208 and a processing module 210. The device is described below.

[0112] The acquisition module 202 is used to acquire the sensor deployment plan based on the basic information of the station area; the first detection module 204 is connected to the above-mentioned acquisition module 202, and is used to detect whether the flow status of the target sensor data is safe based on the above-mentioned sensor deployment plan; the second detection module 206 is connected to the above-mentioned first detection module 204, and is used to detect whether the access status of the above-mentioned target sensor data is safe when the above-mentioned flow status is safe; the third detection module 208 is connected to the above-mentioned second detection module 206, and is used to detect whether the business usage status of the above-mentioned target sensor data is safe when the above-mentioned access status is safe; the processing module 210 is connected to the above-mentioned third detection module 208, and is used to securely process the above-mentioned target sensor data when the above-mentioned business usage status is not safe.

[0113] It should be noted here that the above-mentioned acquisition module 202, first detection module 204, second detection module 206, third detection module 208 and processing module 210 correspond to steps S102 to S110 in the embodiment, and the instances and application scenarios implemented by the multiple modules are the same as the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0114] As an optional embodiment, the acquisition module 202 includes: a first acquisition unit and a second acquisition unit. The first acquisition unit is used to acquire the basic information of the station area and obtain the basic information characteristic value of the station area; the second acquisition unit is connected to the first acquisition unit and is used to compare the basic information characteristic value with the basic information characteristic value of each station area stored in the database to obtain the sensor deployment plan corresponding to the basic information characteristic value.

[0115] As an optional embodiment, the first detection module 204 includes: a first acquisition unit, a third acquisition unit, a first comparison unit and a first determination unit. The first acquisition unit is used to acquire the flow state data set of the target sensor data based on the sensor deployment scheme; the third acquisition unit is connected to the first acquisition unit and is used to acquire the flow state evaluation value of the target sensor data based on the flow state data set; the first comparison unit is connected to the third acquisition unit and is used to compare the flow state evaluation value with the flow state definition evaluation value of the sensor data stored in the database; the first determination unit is connected to the first comparison unit and is used to determine that the flow state of the target sensor data is safe when the flow state evaluation value is greater than or equal to the flow state definition evaluation value; and / or, when the flow state evaluation value is less than the flow state definition evaluation value, it is determined that the flow state of the target sensor data is unsafe and a risk warning is issued for the flow state.

[0116] As an optional embodiment, the second detection module 206 includes: a second acquisition unit, a fourth acquisition unit, a second comparison unit and a second determination unit. The second acquisition unit is used to acquire the access status data set of the target sensor data when the circulation status is safe; the fourth acquisition unit is connected to the second acquisition unit and is used to obtain the access status evaluation value of the target sensor data based on the access status data set; the second comparison unit is connected to the fourth acquisition unit and is used to compare the access status evaluation value with the access status definition evaluation value of the sensor data stored in the database; the second determination unit is connected to the second comparison unit and is used to determine that the access status of the target sensor data is safe when the access status evaluation value is greater than or equal to the access status definition evaluation value; and / or, when the access status evaluation value is less than the access status definition evaluation value, determine that the access status of the target sensor data is unsafe and issue a risk warning for the access status.

[0117] As an optional embodiment, the third detection module 208 includes: a third acquisition unit, a fifth acquisition unit, a third comparison unit and a third determination unit. The third acquisition unit is used to acquire the business usage status data set of the target sensor data when the access status is safe; the fifth acquisition unit is connected to the third acquisition unit and is used to acquire the business usage status evaluation value of the target sensor data based on the business usage status data set; the third comparison unit is connected to the fifth acquisition unit and is used to compare the business usage status evaluation value with the business usage status definition evaluation value of the sensor data stored in the database; the third determination unit is connected to the third comparison unit and is used to determine that the business usage status of the target sensor data is safe when the business usage status evaluation value is greater than or equal to the business usage status definition evaluation value; and / or, when the business usage status evaluation value is less than the business usage status definition evaluation value, determine that the business usage status of the target sensor data is unsafe and issue a risk warning for the business usage status.

[0118] As an optional embodiment, the processing module 210 includes: a sixth obtaining unit, a seventh obtaining unit, an eighth obtaining unit and a processing unit. The sixth obtaining unit is used to obtain the business usage status comparison feature value of the target sensor data based on the business usage status evaluation value when the business usage status is unsafe; the seventh obtaining unit is connected to the sixth obtaining unit and is used to compare the business usage status comparison feature value with each business usage status comparison feature value stored in the database to obtain a data risk solution corresponding to the business usage status comparison feature value; the eighth obtaining unit is connected to the seventh obtaining unit and is used to compare the target sensor data with each sensor data with risk of business usage status stored in the database to obtain the specific person responsible and the leakage date corresponding to the target sensor data; the processing unit is connected to the eighth obtaining unit and is used to perform security processing on the target sensor data based on the data risk solution, the specific person responsible and the leakage date.

[0119] According to an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for detecting the data status of a station area sensor.

[0120] According to an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned methods for detecting data status of a station area sensor when running.

[0121] According to an embodiment of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0122] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0123] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0128] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting data status of a station area sensor, characterized in that: include: Obtain sensor deployment plans based on basic information of the area; Based on the sensor deployment scheme, detecting whether the flow status of the target sensor data is safe; In the case where the flow state is safe, detecting whether the access state of the target sensor data is safe; In the case where the access status is safe, detecting whether the service usage status of the target sensor data is safe; When the service usage status is unsafe, the target sensor data is securely processed.

2. The method according to claim 1, characterized in that The step of obtaining a sensor deployment plan based on the basic information of the station area includes: Obtaining basic information of the station area and obtaining a basic information characteristic value of the station area; The basic information characteristic value is compared with the basic information characteristic value of each station area stored in the database to obtain a sensor deployment plan corresponding to the basic information characteristic value.

3. The method according to claim 1, characterized in that The detecting whether the flow state of the target sensor data is safe based on the sensor deployment scheme includes: Based on the sensor deployment scheme, acquiring a flow status data set of the target sensor data; Based on the flow state data set, obtaining a flow state evaluation value of the target sensor data; comparing the flow state evaluation value with the flow state definition evaluation value of the sensor data stored in a database; When the flow state evaluation value is greater than or equal to the flow state boundary evaluation value, the flow state of the target sensor data is determined to be safe; and / or, when the flow state evaluation value is less than the flow state boundary evaluation value, the flow state of the target sensor data is determined to be unsafe, and a risk warning is issued for the flow state.

4. The method according to claim 1, characterized in that: When the flow state is safe, detecting whether the access state of the target sensor data is safe includes: When the flow state is safe, obtaining an access state data set of the target sensor data; Based on the access status data set, obtaining an access status evaluation value of the target sensor data; comparing the access status evaluation value with the access status delimitation evaluation value of the sensor data stored in the database; When the access status evaluation value is greater than or equal to the access status definition evaluation value, the access status of the target sensor data is determined to be safe; and / or, when the access status evaluation value is less than the access status definition evaluation value, the access status of the target sensor data is determined to be unsafe, and a risk warning is issued for the access status.

5. The method according to claim 1, characterized in that When the access state is secure, detecting whether the service usage state of the target sensor data is secure includes: When the access state is secure, obtaining a service usage state data set of the target sensor data; Based on the service usage status data set, obtaining a service usage status evaluation value of the target sensor data; comparing the service usage status evaluation value with the service usage status definition evaluation value of the sensor data stored in the database; When the business usage status evaluation value is greater than or equal to the business usage status definition evaluation value, the business usage status of the target sensor data is determined to be safe; and / or, when the business usage status evaluation value is less than the business usage status definition evaluation value, the business usage status of the target sensor data is determined to be unsafe, and a risk warning is issued for the business usage status.

6. The method according to claim 1, characterized in that When the service usage status is unsafe, securely processing the target sensor data includes: In the case where the service usage status is unsafe, obtaining a service usage status comparison feature value of the target sensor data based on the service usage status evaluation value; Comparing the service usage status comparison feature value with each service usage status comparison feature value stored in a database, and obtaining a data risk solution corresponding to the service usage status comparison feature value; Compare the target sensor data with sensor data of each business usage status with risks stored in the database to obtain a specific responsible person and a leakage date corresponding to the target sensor data; Based on the data risk solution, the specific responsible person and the leakage date, the target sensor data is securely processed.

7. A station area sensor data status detection device, characterized in that: include: The acquisition module is used to obtain the sensor deployment plan based on the basic information of the station area; A first detection module, configured to detect whether the flow state of target sensor data is safe based on the sensor deployment scheme; A second detection module is used to detect whether the access status of the target sensor data is safe when the flow status is safe; A third detection module, configured to detect whether the service usage status of the target sensor data is safe when the access status is safe; The processing module is used to perform security processing on the target sensor data when the service usage status is unsafe.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the station area sensor data status detection method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program executes the station area sensor data status detection method described in any one of claims 1 to 6 when running.

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

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