Power distribution area sensor data state detection method and device and electronic equipment

Abstract

The application discloses a kind of transformer area sensor data state detection method, device and electronic equipment.Therein, the method includes: based on transformer area basic information, obtain sensor deployment scheme;Based on the sensor deployment scheme, detect whether the flow state of target sensor data is safe;In the case where the flow state is safe, detect whether the access state of the target sensor data is safe;In the case where the access state is safe, detect whether the business use state of the target sensor data is safe;In the case where the business use state is not safe, the target sensor data is safely handled.The application solves the technical problems that transformer area data detection is not comprehensive and unsafe.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric power, in particular to a transformer area sensor data state detection method and device and electronic equipment. BACKGROUND

[0002] In the related art, the sensor data of a transformer area is usually managed after being warehoused. However, with the development of smart grid technology, the amount of data of a transformer area increases dramatically, and the way of managing the sensor data of a transformer area after being warehoused will cause low efficiency, low service quality and threaten the security of transformer area data.

[0003] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0004] The embodiments of the present application provide a transformer area sensor data state detection method and device and electronic equipment to at least solve the technical problems of incomplete transformer area data detection and insecurity.

[0005] According to an aspect of the embodiments of the present application, a transformer area sensor data state detection method is provided, including: obtaining a sensor deployment scheme based on transformer area basic information; detecting whether a flow state of target sensor data is safe based on the sensor deployment scheme; in the case that the flow state is safe, detecting whether an access state of the target sensor data is safe; in the case that the access state is safe, detecting whether a business use state of the target sensor data is safe; and in the case that the business use state is not safe, performing security processing on the target sensor data.

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

[0007] Optionally, the detecting whether the flow state of the target sensor data is safe based on the sensor deployment scheme comprises: obtaining a flow state data set of the target sensor data based on the sensor deployment scheme; obtaining a flow state evaluation value of the target sensor data based on the flow state data set; comparing the flow state evaluation value with a flow state boundary evaluation value of sensor data stored in a database; determining 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 boundary evaluation value; and / or determining that the flow state of the target sensor data is unsafe when the flow state evaluation value is less than the flow state boundary evaluation value, and issuing a risk prompt for the flow state.

[0008] Optionally, the detecting whether the access state of the target sensor data is safe in the case that the flow state is safe comprises: obtaining an access state data set of the target sensor data in the case that the flow state is safe; 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 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, and issuing a risk prompt for the access state.

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

[0010] Optionally, the safe processing of the target sensor data in the case that the business use state is unsafe comprises: obtaining a business use state comparison characteristic value of the target sensor data based on the business use state evaluation value in the case that the business use state is unsafe; comparing the business use state comparison characteristic value with each business use state comparison characteristic value stored in the database to obtain a data risk solution corresponding to the business use state comparison characteristic value; comparing the target sensor data with each business use state risk sensor data stored in the database to obtain a specific person in charge and a leakage date corresponding to the target sensor data; and performing safe processing on the target sensor data based on the data risk solution, the specific person in charge and the leakage date.

[0011] According to another aspect of the present application, there is provided a transformer area sensor data state detection apparatus, comprising: an acquisition module configured to acquire a sensor deployment scheme based on transformer area basic information; a first detection module configured to detect whether a flow state of target sensor data is safe based on the sensor deployment scheme; a second detection module configured to detect whether an access state of the target sensor data is safe in the case that the flow state is safe; a third detection module configured to detect whether a business use state of the target sensor data is safe in the case that the access state is safe; and a processing module configured to perform safe processing on the target sensor data in the case that the business use state is unsafe.

[0012] According to still another aspect of the present application, there is provided a computer readable storage medium comprising a stored executable program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to perform any of the transformer area sensor data state detection methods described above when the executable program is executed.

[0013] According to yet another aspect of the present application, there is provided an electronic device comprising: a memory storing an executable program; and a processor configured to execute the program, wherein the program performs any of the transformer area sensor data state detection methods described above when executed.

[0014] According to still another aspect of the present application, there is provided a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the steps of any of the transformer area sensor data state detection methods.

[0015] In the embodiment of the present application, the full life cycle management of the transformer area data is adopted, the state of the sensor data in the transformer area is detected, whether the state of the sensor data is safe is detected, and the risk prompt is sent in the case of unsafe state, the purpose of guaranteeing the data security and improving the data flow efficiency is achieved, so as to realize the technical effects of discovering the security vulnerability in time, identifying the security problem in time, responding and solving the security event in time, and improving the data security protection capability of the power system, and further solve the technical problems of incomplete transformer area data detection and insecurity. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0017] Figure 1 is a schematic diagram of a transformer area sensor data state detection method according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of a power security protection method based on data full life cycle management according to an optional embodiment of the present application;

[0019] Figure 3 is a structural block diagram of a transformer area sensor data state detection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable the persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the persons skilled in the art without creative labor should belong to the protection scope of the present application.

[0021] It should be noted that the terms “first”, “second”, and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

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

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

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

[0025] Access state: The access state focuses on the condition of data access to the system or network, evaluating the efficiency and security of the data access process.

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

[0027] With the development of smart grid technology, the amount of data in substations has increased dramatically, and a large amount of diverse data has brought challenges in storage, management and protection, especially in ensuring data security. Substations increasingly rely on network communication and data exchange, and advances in information technology and communication technology provide new possibilities and tools for data management in substations. By comprehensively managing the generation, storage and use of power data, substations can effectively protect against internal and external threats while improving operational efficiency and service quality.

[0028] In related technologies, there are still some deficiencies in the research on power security protection methods based on data life cycle management, which are mainly reflected in the following aspects: traditional substation data collection and processing methods are single, data management methods are not flexible and efficient, causing data integration difficulties, and unable to realize seamless data flow and comprehensive analysis. Due to the lack of comprehensive data security monitoring methods and the single data processing method, the data management method is not flexible enough, which limits the rapid response capability of the substation to changing conditions, thereby affecting the stability and efficiency of power supply. Due to the difficulty of data integration, seamless data flow cannot be achieved, which may lead to the formation of information islands, i.e. key data cannot be effectively shared and utilized in the system, not only reducing the quality of decision-making, but also making the system more vulnerable when facing external threats. In summary, incomplete data security monitoring may result in failure to timely detect or respond to security threats, and in order to compensate for the problems caused by the failure to timely detect or respond to security threats, more resources need to be invested to handle system problems and data errors, resulting in an increase in operation and maintenance costs.

[0029] In view of the above problems, method 1 is a power grid equipment whole life cycle monitoring system based on shared services, comprising a network communication security monitoring module, a hardware device security monitoring module, a data security monitoring module and an operation and maintenance security monitoring module, the network communication security monitoring module is used for monitoring the network communication security of the power grid equipment, the hardware device security monitoring module is used for monitoring the hardware security in the power grid equipment, the data security monitoring module is used for monitoring the data security of the power grid equipment, and the operation and maintenance security monitoring module is used for monitoring the operation and maintenance security of the power grid equipment. This method can monitor the security state of the power grid equipment in the whole life cycle from the aspects of hardware and data security, realize unmanned monitoring, improve work efficiency, and actively protect the safety of the equipment.

[0030] Another method 2 is a power transmission and transformation equipment whole life cycle management system based on Internet of Things technology, the management system is composed of an electronic tag, a field monitoring and measuring device and a management system software; the electronic tag is installed on the power transmission and transformation equipment; the field monitoring and measuring device integrates RFID identification, GPS all-weather real-time positioning, wireless communication, full-state quantity measurement, information security and industrial control, and realizes real-time identification tracking, operation data acquisition, intelligent diagnosis and state alarm of the power transmission and transformation equipment; the field monitoring and measuring device and the management system software realize real-time data interaction through Ethernet communication mode; and the real-time monitoring of the power transmission and transformation equipment is realized through various modules of the management system software. This method realizes the whole life cycle management of the power transmission and transformation equipment, makes the whole life cycle of the power equipment transparent, improves the efficiency of equipment management, improves the transparency of power asset operation and the integrity of data, and fully plays the use efficiency of the power equipment.

[0031] Therefore, method 1 emphasizes the whole life cycle safety monitoring of the power grid equipment, focuses on the safety guarantee of hardware, data and operation and maintenance, is a system level safety monitoring, and focuses on the safety protection of hardware and network communication; method 2 emphasizes the application of Internet of Things technology in the whole life cycle management of the power transmission and transformation equipment, and focuses on real-time monitoring and data acquisition of the equipment state. Neither method 1 nor method 2 involves data security or sensor data flow analysis, and is inclined to intelligent equipment operation management and state monitoring. In view of this, the embodiment of the present application provides a transformer area sensor data state detection method, which focuses on the whole life cycle safety management of sensor data, especially the safety of data flow, access and use, involves data security traceability management, and focuses on the safety and integrity of sensor generated data.

[0032] Based on this, the embodiment of the present application provides a kind of area sensor data state detection method, by monitoring the full life cycle state of area sensor data, risk prompt is sent in time for the stage of existence risk, carry out state detection to subsequent stage under the condition of determining that previous stage is safe, and carry out safe handling to the data of unsafe business use state, determine risk solution, trace specific person in charge and leak date, risk is solved in time.

[0033] According to the embodiment of the present application, an embodiment of a method for detecting the state of a sensor data in a transformer area 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0034] Figure 1 is a schematic diagram of a method for detecting the state of a sensor data in a transformer area according to an embodiment of the present application, as shown in Figure 1 The method comprises the following steps:

[0035] In step S102, based on the basic information of the transformer area, the sensor deployment scheme is obtained.

[0036] As an optional embodiment, the execution subject of the method of the present embodiment can be a terminal or a server for processing the sensor data in the transformer area.It should be noted that the type of the terminal can be various, for example, it can be a mobile terminal with certain computing power, or a fixed computer device with recognition capability, etc.The type of the server can also be various, for example, it can be a local server, or a virtual cloud server.The server can be a single computer device according to the computing power, or a computer cluster integrated by multiple computer devices.

[0037] As an optional embodiment, various methods can be used to obtain sensor deployment schemes based on basic information of the transformer substation. For example, the basic information of the transformer substation can be obtained first to acquire its basic information feature values; then, these feature values ​​can be compared with the basic information feature values ​​of each transformer substation stored in the database to obtain the sensor deployment scheme corresponding to the basic information feature values. The basic information can be stored in a basic information dataset, and the feature values ​​can be obtained based on this dataset. The basic information dataset can include one or more types of information data, such as the voltage level of the transformer substation, the total length of the power supply lines to the substation, and the area of ​​the transformer substation. Alternatively, a sensor deployment scheme generation model can be trained based on historical basic information of transformer substations and historical sensor deployment schemes. Inputting the basic information of the transformer substation into the model will yield the output of the sensor deployment scheme. This method of obtaining sensor deployment schemes using basic information of transformer substations allows for the combination of the actual situation and needs of the transformer substations, enabling sensors to be deployed in more needed locations, resulting in more accurate data collection and greater effectiveness.

[0038] As an optional embodiment, when the method involves first obtaining the basic information of the aforementioned transformer substation, then obtaining the basic information feature values ​​of the aforementioned transformer substation, and finally comparing the aforementioned basic information feature values ​​with the basic information feature values ​​of each transformer substation stored in the database to obtain the sensor deployment scheme corresponding to the aforementioned basic information feature values, there can be multiple ways to obtain the basic information of the aforementioned transformer substation and obtain the basic information feature values ​​of the aforementioned transformer substation. For example, the basic information feature values ​​of the transformer substation can be obtained based on the transformer substation voltage level, the total length of the transformer substation power supply line, and the transformer substation area. When the basic information of the transformer substation includes the transformer substation voltage level, the total length of the transformer substation power supply line, and the transformer substation area, various methods can also be used to obtain the basic information feature values ​​of the transformer substation. For example, the aforementioned basic information feature values ​​can be calculated using the following formula:

[0039]

[0040] In the formula, Here are the basic information characteristics of the transformer substation: bd is the voltage level of the substation, sb is the total length of the power supply line to the substation, md is the area of ​​the substation, ε1 is the compensation factor for the set voltage level of the substation, ε2 is the compensation factor for the set total length of the power supply line to the substation, and ε3 is the compensation factor for the set area of ​​the substation.

[0041] Step S104: Based on the above sensor deployment scheme, 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, a plurality of methods can be used. For example, first, based on the above sensor deployment scheme, the flow state data set of the target sensor data is obtained; then, based on the flow state data set, the flow state evaluation value of the target sensor data is obtained; the flow state evaluation value is compared with the flow state boundary evaluation value of the sensor data stored in the database; when the flow state evaluation value is greater than or equal to the flow state boundary evaluation value, it is determined that the flow state of the target sensor data is safe; and / or, when the flow state evaluation value is less than the flow state boundary evaluation value, it is determined that the flow state of the target sensor data is not safe, and a risk prompt is issued for the flow state. According to the above sensor deployment scheme, the sensor position can be more optimally deployed, the sensor data can be obtained from the deployed sensor, and the flow state can be detected in the process of sensor data flow. The flow state data set can include one or more information data, such as the time length of sensor data flowing to the next node, the packet loss rate of sensor data flowing, the network bandwidth of sensor data flowing, etc. Through the detection of whether the flow state of the target sensor data is safe, the risk can be prompted in time when the flow appears, and the operation and maintenance personnel can take measures according to the prompt information to eliminate the obstacles in the flow process, ensure the normal flow of sensor data, and further ensure the safety and flow efficiency of sensor data in the flow.

[0043] As an optional embodiment, when the flow state evaluation value of the target sensor data is obtained based on the above flow state data set, a plurality of methods can be used. For example, the time length of sensor data flowing to the next node, the packet loss rate of sensor data flowing, and the network bandwidth of sensor data flowing in the flow state data set can be used to obtain the flow state evaluation value of the target sensor data. When the flow state data set includes the time length of sensor data flowing to the next node, the packet loss rate of sensor data flowing, and the network bandwidth of sensor data flowing, a plurality of methods can also be used to obtain the flow state evaluation value of the target sensor data, for example, the following formula can be used to calculate the flow state evaluation value, which can comprehensively analyze the role of the time length of sensor data flowing to the next node, the packet loss rate of sensor data flowing, and the network bandwidth of sensor data flowing in the process of sensor data flowing to obtain a comprehensive index to evaluate the flow state:

[0044]

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

[0046] In step S106, in the case of the above flow transfer state safety, it is detected whether the access state of the target sensor data is safe.

[0047] As an optional embodiment, in the case of the above flow transfer state safety, when detecting whether the access state of the target sensor data is safe, a plurality of methods can be used. For example, first, in the case of the above flow transfer state safety, the access state data set of the target sensor data is obtained; then, based on the access state data set, the access state evaluation value of the target sensor data is obtained; the access state evaluation value is compared with the access state boundary evaluation value of the sensor data stored in the database; when the access state evaluation value is greater than or equal to the access state boundary evaluation value, it is determined that the access state of the target sensor data is safe; and / or, when the access state evaluation value is less than the access state boundary evaluation value, it is determined that the access state of the target sensor data is not safe, and a risk prompt is issued for the access state. The access state data set can include one or more information data, such as the access response time length of the sensor data, the access check data repetition ratio, the access packet loss rate, etc. By detecting the access state and issuing a risk prompt for the unsafe access state, abnormal access data flow can be discovered and processed in time, so that the sensor data can normally and efficiently perform the access step, and the safety of the access state is ensured.

[0048] As an optional embodiment, when the access state evaluation value of the target sensor data is obtained based on the access state data set, a plurality of methods can be used. For example, the access state evaluation value can be obtained based on the access response time length of the sensor data, the access check data repetition ratio, and the access packet loss rate included in the access state data set. When the access state data set includes the access response time length of the sensor data, the access check data repetition ratio, and the access packet loss rate, a plurality of methods can also be used to obtain the access state evaluation value. For example, in order to completely utilize the above three kinds of data to obtain a comprehensive access state evaluation value, the following formula can be used for calculation:

[0049]

[0050] In the formula, γ is a sensor data access state evaluation value, xy is a sensor data access response time length, cf is a sensor data access check data repetition ratio, db is a sensor data access packet loss rate, σ1 is a set compensation factor of the sensor data access response time length, σ2 is a set compensation factor of the sensor data access check data repetition ratio, σ3 is a set compensation factor of the sensor data access packet loss rate, and e is a natural constant.

[0051] In step S108, in the case of the access state being safe, it is detected whether the service use state of the target sensor data is safe.

[0052] As an optional embodiment, in the case of the access state being safe, when it is detected whether the service use state of the target sensor data is safe, a plurality of manners can be adopted. For example, first, in the case of the access state being safe, the service use state data set of the target sensor data is obtained; then, based on the service use state data set, the service use state evaluation value of the target sensor data is obtained; the service use state evaluation value is compared with the service use state boundary evaluation value of the sensor data stored in the database; when the service use state evaluation value is greater than or equal to the service use state boundary evaluation value, it is determined that the service use state of the target sensor data is safe; and / or, when the service use state evaluation value is less than the service use state boundary evaluation value, it is determined that the service use state of the target sensor data is not safe, and a risk prompt is issued for the service use state. By detecting the service use state and issuing the risk prompt, the warning can be given before the service use problem develops, so that the problem solving scheme can be taken in time to deal with the risk, avoid affecting the circulation of the sensor data, and also protect the sensor data from being damaged in the service use process, so as to ensure the accuracy and integrity of the data.

[0053] As an optional embodiment, when the service use state evaluation value of the target sensor data is obtained based on the service use state data set, a plurality of manners can be adopted. For example, the service use state evaluation value can be obtained based on the modification frequency, abnormal access frequency and operation failure times of the sensor data in the service use process included in the service use state data set. When the service use state data set includes the modification frequency, abnormal access frequency and operation failure times of the sensor data in the service use process, a plurality of manners can also be adopted to obtain the service use state evaluation value. For example, the service use state evaluation value can be calculated by using the following formula:

[0054]

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

[0056] In step S110, the target sensor data is processed safely in the case of an unsafe service usage state.

[0057] As an optional embodiment, in the case of an unsafe service usage state, the target sensor data can be processed safely in various ways. For example, in the case of an unsafe service usage state, the service usage state comparison characteristic value of the target sensor data is obtained based on the service usage state evaluation value; the data risk solution corresponding to the service usage state comparison characteristic value is obtained by comparing the service usage state comparison characteristic value with each service usage state comparison characteristic value stored in the database; the specific person responsible and the leak date corresponding to the target sensor data are obtained by comparing the target sensor data with each sensor data with a risky service usage state stored in the database; and the target sensor data is processed safely based on the data risk solution, the specific person responsible, and the leak date. The safe processing of the target sensor data can be cutting off the communication with the risky sensor and no longer receiving the data collected by the sensor, or tracing the node where the risk exists and solving the risk, or a combination of the two or other ways. By processing the sensor data with an unsafe service usage state safely and identifying the specific person responsible and the leak date, the problem can be quickly locked to the link where the problem exists, the loss caused by the risk can be reduced, the risk can be remedied, and the safety and accuracy of data usage can be improved.

[0058] As an optional embodiment, when the service usage state comparison feature value of the target sensor data is obtained based on the service usage state evaluation value, a plurality of ways can be adopted. For example, for sensor data with unsafe service usage state, the corresponding service usage state evaluation value can be introduced into the service usage state comparison model to obtain the service usage state comparison feature value, and then the sensor data is managed according to the service usage state comparison feature value. The service usage state comparison model can be established in a plurality of ways to obtain a plurality of different models. For example, the service usage state comparison model can be obtained according to experience or neural network learning historical data. Through the service usage state comparison model, the service usage state comparison feature value can be obtained, which is convenient for comparison with historical data in the database, so as to quickly obtain a risk processing scheme. For example, the service usage state comparison model can be represented by the following formula:

[0059]

[0060] In the formula, β is the service usage state comparison feature value, δ is the sensor generated data service usage state evaluation value, and e is a natural constant.

[0061] Through the above steps, a more optimal sensor deployment scheme can be obtained, which ensures that the sensor can efficiently collect useful data, and can also detect that the sensor data can be safely transmitted and used in the whole life cycle, including the flow process, the access process and the service usage process, that is, the safety of the whole process from data generation, transmission to service application is ensured, thereby optimizing the operation efficiency of the power system and improving the professionalism and reliability of data management.

[0062] In combination with the above embodiments and optional embodiments, an optional implementation is provided. In the optional implementation, a power safety protection method based on data whole life cycle management is provided, Figure 2 is a schematic diagram of a power safety protection method based on data whole life cycle management according to an optional embodiment of the present application, as Figure 2 shown, the method comprises the following processing.

[0063] S1, analyzing the basic information of the transformer area, and comparing to obtain a sensor deployment scheme.

[0064] Specifically, the basic information of the transformer area is analyzed, and the sensor deployment scheme is obtained by comparison. The specific analysis process is: obtaining the basic information dataset of the transformer area, based on the obtained basic information dataset of the transformer area, comprehensive analysis is carried out to obtain the basic information characteristic value of the transformer area, and the basic information characteristic value of the transformer area is used as the analysis basis for obtaining the sensor deployment scheme by comparison; a mapping set of historical basic information characteristic values of transformer area and sensor deployment scheme is established, and the mapping set of the basic information characteristic value of the transformer area and the corresponding sensor deployment scheme of each basic information characteristic value of the transformer area stored in the database is compared to obtain the sensor deployment scheme corresponding to the basic information characteristic value of the transformer area.

[0065] It needs to be explained that the above obtaining the basic information of the transformer area and its characteristic value can more accurately determine which areas or devices need to be monitored, can ensure that the sensors are concentrated in important nodes, so that the monitoring result is more accurate and reliable, can identify the needs and bottlenecks of different transformer areas, so that the sensor deployment scheme can be optimized according to the actual needs, thereby effectively configuring resources and avoiding unnecessary investment. The correct sensor deployment scheme can timely discover and warn potential faults or safety hazards, reduce the risk of system failure, and improve the safety and stability of the entire transformer area. Without a proper sensor deployment scheme, intelligent application is difficult to achieve. The analysis of the transformer area characteristic value helps to promote the construction of intelligent monitoring and automatic scheduling, and improves the intelligent level of the transformer area. The transformer area characteristic value provides an important data basis, which can be used for subsequent decision support system to help managers make more reasonable decisions based on real monitoring information.

[0066] Further, the basic information dataset of the transformer area specifically includes the transformer area voltage grade, the total length of the transformer area power supply line, and the transformer area area. The transformer area voltage grade refers to the voltage standard or rated value of the power transmission or distribution line in a certain transformer area. It is obtained based on the transformer. The total length of the transformer area power supply line refers to the total length of all power supply lines in the transformer area, which is obtained based on the total station and laser range finder. The transformer area area refers to the total area of the transformer area. The geographic information system (GIS) technology can be used to analyze the space of the transformer area and calculate the actual land area covered.

[0067] In a specific embodiment, a larger cross-section wire can reduce line loss, thereby adapting to a longer power supply distance. The higher the voltage grade, the longer the power supply line can theoretically bear. The length of the power supply line of the transformer area is related to the area it covers. A larger transformer area needs a longer power supply line to meet the electricity demand of all users. The transformer area area and the power supply line length are positively correlated. A larger transformer area usually has a higher demand for electricity, which may require the use of a higher voltage grade to meet the power supply demand, especially in heavy load conditions.

[0068] It should be explained that the above basic information feature values ​​of the transformer substations were obtained in the following way:

[0069]

[0070] In the formula, Here are the basic information characteristics of the transformer substation: bd is the voltage level of the substation, sb is the total length of the power supply line to the substation, md is the area of ​​the substation, ε1 is the compensation factor for the set voltage level of the substation, ε2 is the compensation factor for the set total length of the power supply line to the substation, and ε3 is the compensation factor for the set area of ​​the substation.

[0071] It should be explained that the aforementioned basic information feature values ​​of the transformer substations are calculated using the substation voltage level, the total length of the power supply lines, and the substation area. Normalization is applied to these parameters. Understanding the substation area allows for the assessment of the power network's structure and connectivity, which is crucial for system expansion and optimization planning. Based on the basic substation information, equipment resources can be rationally allocated, redundant investment reduced, and overall operational efficiency improved. The substation feature values ​​can identify potential high-risk areas, allowing for proactive countermeasures and reducing the probability of faults. This provides a foundation for subsequent data analysis, enabling in-depth data mining in conjunction with other data to support intelligent decision-making. The compensation factors for the substation voltage level, the total length of the power supply lines, and the substation area are obtained from a database. A mapping set is established based on historical data, showing the historical measurements of substation voltage level, the total length of the power supply lines, and the substation area against the compensation factors for these parameters. This yields the compensation factors corresponding to the current substation voltage level, the total length of the power supply lines, and the substation area.

[0072] S2, based on the sensor deployment scheme, analyzes the sensor data flow status to determine whether the sensor data flow status is safe.

[0073] Specifically, the sensor is used to obtain the power usage state information of the transformer area, specifically including current sensor, voltage sensor, power sensor, energy meter, temperature sensor, etc., and the sensor data specifically includes line current, load current, overload current times, line voltage, phase voltage, cumulative energy consumption, energy consumption anomaly times, etc., which are all parameters related to the data security protection of the transformer area. Analyze the sensor data flow state, determine whether the sensor data flow state is safe, the specific analysis process is: obtain the sensor data flow state data set, based on the obtained sensor data flow state data set, comprehensive analysis obtains the sensor data flow state evaluation value, the sensor data flow state evaluation value is used as the analysis basis for judging whether the sensor data flow state is safe; compare the sensor data flow state evaluation value with the sensor data flow state boundary evaluation value stored in the database; if the sensor data flow state evaluation value is higher than or equal to the sensor data flow state boundary evaluation value, then the sensor data flow state evaluation value corresponding to the sensor data flow state is safe; if the sensor data flow state evaluation value is lower than the sensor data flow state boundary evaluation value, then the sensor data flow state evaluation value corresponding to the sensor data flow state is at risk, and a prompt is sent for the sensor data flow state at risk.

[0074] It needs to be explained that timely identification of potential risks can improve the security of the system, avoid abnormal sensor data flow, and cause further safety accidents. Identifying risks in advance can perform necessary maintenance and repair before the problem expands, thereby reducing the maintenance cost of the whole system. Monitoring the data flow state of the sensor can take measures before potential abnormal problems occur, thereby enhancing the reliability of the whole system, that is, monitoring the data flow state helps to ensure the integrity and accuracy of the data generated by the sensor, thereby ensuring the rationality of subsequent data analysis and decision-making.

[0075] Further, the sensor data flow state data set specifically includes sensor data flow time to next node, sensor data flow packet loss rate, and sensor data flow network bandwidth. The sensor data flow time to next node refers to the time required from sensor data to the data being sent to the next processing node (e.g., gateway, server, or other sensor), which can help evaluate the delay of data transmission and affect real-time requirements of applications. The sensor data flow time to next node 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. High packet loss rate can cause incomplete data, affecting analysis and decision-making. The packet loss rate can be obtained by Simple Network Management Protocol (SNMP) to obtain the traffic and packet loss statistics of the device. The network bandwidth refers to the maximum amount of data that the network can transmit within a certain period of time, usually expressed in bits per second (bps), reflecting the data transmission capacity between the sensor and the next node. The network bandwidth utilization of the current network can be obtained using the monitoring interface of the network device or SNMP.

[0076] In a specific embodiment, the bandwidth is the maximum data transmission rate that the network can support. If the bandwidth is high, theoretically more data can be transmitted in a shorter time. In the case of sufficient bandwidth, the transmission time is usually short. When the network load is too heavy or the bandwidth is insufficient, data packets may be discarded, causing the packet loss rate to increase. Re-transmission of data will increase the overall transmission time. In the case of low bandwidth or network congestion, the packet loss rate will usually increase, i.e., if the bandwidth is sufficient and the network is stable, the packet loss rate will be relatively low.

[0077] It should be explained that by analyzing the sensor data flow state, it can be determined whether the sensor data flow state is safe, which can timely identify possible security problems encountered by data during transmission, such as unauthorized access, data leakage or tampering, helping enterprises to take corresponding security measures in advance to protect the core data of the transformer area from attacks. It can also detect whether data is lost or errors occur during transmission or processing, which can help effective data analysis and correct decision-making, and can 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.

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

[0079]

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

[0081] It should be explained that the above-mentioned sensor data flow transfer state evaluation value is calculated by the time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer, and the time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer are normalized. Monitoring the data flow transfer time length and the packet loss rate can timely discover network bottlenecks or faults, quickly locate problem nodes, evaluate the performance of data flow transfer, and identify performance-deficient links to provide a basis for optimizing network architecture and sensor layout. Monitoring the change trend of the packet loss rate can take measures to reduce data packet loss phenomenon, improve the reliability of data transmission, evaluate bandwidth usage, identify underutilized resources, make reasonable configuration, and avoid unnecessary resource waste. Long time length and high packet loss rate can indicate the existence of network attacks or malicious behavior, and potential security threats can be discovered in time. The set compensation factors of the time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer are obtained from the database, and the mapping set of the historical measured time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer and the compensation factors of the time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer is established according to historical data, to obtain the compensation factors of the time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer corresponding to the current time length of sensor data flow transfer to the next node, the packet loss rate of sensor data flow transfer, and the network bandwidth of sensor data flow transfer.

[0082] S3, based on the flow transfer state security of sensor data, analyzes the sensor data access state and judges whether the sensor data access state is safe.

[0083] Specifically, the sensor data access state is analyzed to determine whether the sensor data access state is safe. The specific analysis process is as follows: obtaining a sensor data access state data set, which specifically includes sensor data access response time, sensor data access check data repetition ratio, and sensor data access packet loss rate. The response time refers to the time interval from the sensor sending a request to access to 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 log recording function. The check data repetition ratio refers to the ratio of repeated data packets sent by the sensor to the total number of data packets within a certain time. A high repetition ratio may indicate data transmission problems or sensor faults. Repeated data packets in the data stream can be detected using data processing and analysis tools, such as data recorders, edge computing devices, or cloud servers. The packet loss rate refers to the proportion of lost data packets to the total number of data packets during data access. Packet loss often leads to incomplete data, which in turn affects subsequent data analysis and decision-making. Network analyzers, test instruments, or sensor internal communication modules can be used to monitor data packet access.

[0084] It should be noted that if the packet loss rate is high, it means that some data packets fail to reach the destination during data access, 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 retransmitted data packets and process these repeated requests, resulting in an extended overall response time. An increase in response time may also lead to an increase in data repetition ratio. In the case where the system fails to receive data packets in a timely manner, the sender may repeatedly send access requests, resulting in the generation of repeated data. If the system is relatively stable and the response time is short, the repetition ratio will generally be low. A high repetition 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 will trigger a retransmission mechanism, resulting in the generation of repeated data. If the packet loss rate is low, it means that the data access state is stable, and the repetition ratio will generally be low.

[0085] Based on the obtained sensor data access state data set, comprehensive analysis is performed to obtain a sensor data access state evaluation value, which serves as an analysis basis for judging whether the sensor data access state is safe. The sensor data access state evaluation value is compared with a sensor data access state boundary evaluation value stored in a database. If the sensor data access state evaluation value is higher than or equal to the sensor data access state boundary evaluation value, the sensor data access state corresponding to the sensor data access state evaluation value is safe. If the sensor data access state evaluation value is lower than the sensor data access state boundary evaluation value, the sensor data access state corresponding to the sensor data access state evaluation value is at risk, and a prompt is issued for the sensor data access state at risk.

[0086] It should be explained that the above-mentioned monitoring of the access state (including the response time, the repeated data proportion, and the packet loss rate) can timely discover and handle abnormal data flow, thereby improving the reliability and integrity of the sensor data. For the sensor data access state at risk, timely prompting can quickly start the corresponding emergency measures to prevent the spread of potential problems, thereby reducing the probability of data loss or system failure. Analyzing the data access state and identifying the risk sensor can more effectively configure network and computing resources, ensure that critical equipment obtains necessary support, and improve the overall system performance. For the sensor data access state at risk, timely prompting can help the operation and maintenance personnel to further check the security, identify possible network attacks or malicious behaviors, and enhance the security of the system. The evaluation of the sensor access state can provide data-driven insights and reports for the management personnel, helping them to effectively make decisions and optimize operation processes, thereby improving the overall efficiency.

[0087] Further, the sensor data access state evaluation value is obtained in the following manner:

[0088]

[0089] In the formula, γ is the sensor data access state evaluation value, xy is the sensor data access response time, cf is the repeated proportion of the sensor data access inspection data, db is the packet loss rate of the sensor data access, σ1 is a set compensation factor for the sensor data access response time, σ2 is a set compensation factor for the repeated proportion of the sensor data access inspection data, σ3 is a set compensation factor for the packet loss rate of the sensor data access, and e is a natural constant.

[0090] It needs to be explained that the above-mentioned sensor data access state evaluation value is calculated by the sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate. The sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate need to be normalized. Monitoring the length of the sensor access response can identify and optimize the delay in the data access process, improve the overall efficiency of data collection, monitor the repetition ratio of the check data, which helps to identify data redundancy and ensure the uniqueness and accuracy of the data set, and improve the overall quality of the data. Comprehensive evaluation of the access state helps to make effective decisions on resource allocation, reasonably allocate bandwidth and computing resources, and avoid resource idling. By monitoring the response time and packet loss rate, potential system failures or performance degradation can be detected in time, so that measures can be taken to repair in time. 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 compensation factor of the set sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate is obtained from the database, and the mapping set of the historical measured sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate and the compensation factor of the sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate is established according to the historical data, and the compensation factor of the sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate corresponding to the current sensor data access response time, the sensor data access check data repetition ratio and the sensor data access packet loss rate is obtained.

[0091] S4, based on the access state security of the sensor data, analyzing the sensor data service usage state to determine whether the sensor data service usage state is safe.

[0092] Specifically, the sensor data service usage state is analyzed to determine whether the sensor data service usage state is safe. The specific analysis process is as follows: obtaining a sensor data service usage state data set, which specifically includes sensor data service usage process modification frequency, sensor data service usage process abnormal access frequency, and sensor data service usage process operation failure number. The modification frequency refers to the number of times of modifying or updating the data generated by the sensor within a certain period of time, which may include adjusting configuration parameters, correcting or updating data, etc. It can be tracked through a data management system (such as a database management system) or a sensor management software. The abnormal access frequency refers to the number of abnormal access events occurring within a certain period of time, such as unauthorized access attempts, format error data requests, etc., which implies potential security threats or system failures, which 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 operation failure number refers to the number of times of operation failure due to various reasons within a certain period of time, such as device failure, network problem, incorrect data format, which can be obtained through application logs, operation logs or sensor data recording equipment.

[0093] In a specific embodiment, if the modification frequency is high, it means that frequent updates or adjustments are made, which may cause a certain degree of instability, because under high frequency modification, system configuration may be wrong, causing operation failure. An increase in abnormal access frequency usually means that there is a potential security threat or system vulnerability, which may lead to operation failure, unauthorized access attempts or malicious requests may fail due to system security mechanisms. High frequency of modification may attract malicious attackers to attempt to obtain unauthorized access or affect system stability through abnormal access strategies, especially when sensitive data is involved in the modification, the security of the system will be more concerned, and the frequency of abnormal access may increase.

[0094] Based on the obtained sensor data service usage state data set, a comprehensive analysis is performed to obtain a sensor data service usage state evaluation value, which serves as an analysis basis for determining whether the sensor data service usage state is safe. The sensor data service usage state evaluation value is compared with a sensor data service usage state boundary evaluation value stored in a database. If the sensor data service usage state evaluation value is higher than or equal to the sensor data service usage state boundary evaluation value, the corresponding sensor data service usage state is safe. If the sensor data service usage state evaluation value is lower than the sensor data service usage state boundary evaluation value, the corresponding sensor data service usage state is at risk, and a prompt is issued for the sensor data service usage state at risk.

[0095] It should be noted that the above operation of issuing a risk prompt 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 of sensors can help avoid system failures, thereby reducing unexpected downtime and maintenance costs and improving system availability. Monitoring and prompting potential risks can prevent data loss or damage due to abnormal access, operation failure, etc., thereby ensuring data integrity and accuracy and laying a foundation for subsequent data analysis. After issuing a risk prompt, the operation and maintenance team can focus on solving high-risk areas, optimize resource allocation, and improve the efficiency of fault handling to avoid blind processing of all problems. Risk prompts can also provide important information for management, helping them consider safety and potential risks when making relevant management decisions, and enhancing the scientific nature of decisions.

[0096] Specifically, the sensor data service usage state evaluation value is obtained as follows:

[0097]

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

[0099] It needs to be explained that the above-mentioned sensor data service usage state evaluation value is calculated by the sensor data service usage process modification frequency, the sensor data service usage process abnormal access frequency, and the sensor data service usage process operation failure number. The sensor data service usage process modification frequency, the sensor data service usage process abnormal access frequency, and the sensor data service usage process operation failure number need to be normalized. It helps to identify potential security problems or abnormal access behavior, and take timely measures to protect the system, ensure the safety and reliability of data processing. Monitoring the modification frequency can understand the change trend of data, so as to find the opportunity to optimize or adjust the data processing flow, and improve the efficiency of business process. Identifying the reason for operation failure, especially frequent failure operation, can provide direction for process optimization, such as improving user interface or increasing system response speed. Timely processing of abnormal access can improve user operation trust and provide more stable service. High-frequency abnormal access or failure number may become a warning signal of potential system failure, helping operation and maintenance personnel to find and solve problems in time and prevent serious system failure. According to the data usage frequency and abnormal situation, 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 to encourage the team to regularly evaluate and improve data business processes, thereby improving overall system performance and service quality. The compensation factor of the set sensor data service usage process modification frequency, sensor data service usage process abnormal access frequency, and sensor data service usage process operation failure number is obtained from the database, and the mapping set of historical measured sensor data service usage process modification frequency, sensor data service usage process abnormal access frequency, and sensor data service usage process operation failure number and compensation factor of sensor data service usage process modification frequency, sensor data service usage process abnormal access frequency, and sensor data service usage process operation failure number is established according to historical data, to obtain the compensation factor of the current sensor data service usage process modification frequency, sensor data service usage process abnormal access frequency, and sensor data service usage process operation failure number corresponding to the sensor data service usage process modification frequency, sensor data service usage process abnormal access frequency, and sensor data service usage process operation failure number.

[0100] S5, data security trace management is performed on the sensor data with unsafe business usage state.

[0101] Specifically, the sensor data at risk of business use state is subjected to data security traceability management; the business use state evaluation value of the sensor data at risk of business use state is introduced into a business use state comparison model to obtain a business use state comparison characteristic value, which serves as an analysis basis for the data security traceability management of the sensor data at risk of business use state.

[0102] The business use state comparison model is obtained in the following manner:

[0103]

[0104] In the formula, β is the business use state comparison characteristic value, δ is the sensor-generated data business use state evaluation value, and e is a natural constant.

[0105] The business use state comparison characteristic value is compared with the data risk solution corresponding to each business use state comparison characteristic value stored in the database to obtain the data risk solution corresponding to the business use state comparison characteristic value; the sensor data at risk of business use state is compared with the specific person in charge and the leakage date corresponding to each sensor data at risk of business use state stored in the database; the data risk solution, the specific person in charge, and the leakage date corresponding to the business use state comparison characteristic value are obtained, and the business use state at risk of sensor data is solved.

[0106] In one specific embodiment, the business use state comparison model is used to determine a data risk solution, which can immediately trigger a predetermined response measure, provide efficiency in risk response, reduce human intervention, and improve the accuracy and consistency of analysis results. Data-based quantitative decision support enables more reasonable response plans and reasonable risk resolution when facing complex data security problems. Identifying specific persons in charge can clarify who should be held responsible in data leakage or risk events, helping to develop appropriate accountability mechanisms to 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 assess and manage risks. In the event of a data leakage event, the responsible person and the leakage 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 continuous improvement mechanism and improve overall data security levels.

[0107] It should be explained that the above-mentioned power safety protection method based on data full life cycle management is used for the safety protection of data flow, data access, and data business use process data in low-voltage protection.

[0108] It needs to be explained that the above-mentioned power safety protection method based on data full life cycle management can analyze the security of each data generation and transmission stage in detail, discover and repair potential security vulnerabilities in time, prevent security threats from spreading to other parts of the transformer area, and improve the security protection level of the whole system. By analyzing the basic information of the transformer area and comparing the obtained sensor deployment scheme, the efficient deployment of sensors in key positions can be ensured, the resource use is optimized, and the coverage and accuracy of data collection are improved. Systematic analysis of the data flow, access and use state of the sensor can ensure the integrity and accuracy of the data in the transmission and storage process, and improve the fault response rate.

[0109] It needs to be explained that by analyzing the sensor data flow state, it is determined whether the sensor data flow state is safe, which can identify potential security problems that may occur during data transmission, such as unauthorized access, data leakage or tampering, which helps enterprises to prevent these threats in advance and take appropriate security measures to protect the core data of the transformer area from attacks. It can also detect whether data is lost or incorrect during transmission or processing, which helps to perform effective data analysis and correct decision-making, and can also find bottlenecks or efficiency problems in data processing and transmission, so as to optimize the data architecture or upgrade the system, and improve the overall data processing efficiency.

[0110] It needs to be explained that the above-mentioned data security trace management of the business use state unsafe sensor data enables the organization to quickly track the specific person responsible for the data security problem, which is the key to timely response and solution of security incidents, helps to minimize damage, and the trace management provides the specific person responsible for the problem and the leakage date, which is crucial for quickly developing effective response measures, greatly improving the processing efficiency of security incidents. The trace analysis of data security incidents can identify weaknesses in the existing security protection system, and this continuous improvement and optimization helps to establish a more solid security protection barrier.

[0111] According to the embodiment of the present application, a transformer area sensor data state detection device is provided, Figure 3 is a structural block diagram of a transformer area sensor data state detection device according to an embodiment of the present application, as Figure 3 shown, the device comprises: an acquisition module 202, a first detection module 204, a second detection module 206, a third detection module 208 and a processing module 210, which will be described below.

[0112] The acquisition module 202 is configured to acquire a sensor deployment scheme based on the basic information of the transformer area; the first detection module 204 is connected to the acquisition module 202 and is configured to detect whether a flow state of target sensor data is safe based on the sensor deployment scheme; the second detection module 206 is connected to the first detection module 204 and is configured to detect whether an access state of the target sensor data is safe in the case that the flow state is safe; the third detection module 208 is connected to the second detection module 206 and is configured to detect whether a service use state of the target sensor data is safe in the case that the access state is safe; and the processing module 210 is connected to the third detection module 208 and is configured to perform security processing on the target sensor data in the case that the service use state is not safe.

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

[0114] As an optional embodiment, the acquisition module 202 includes a first obtaining unit and a second obtaining unit. The first obtaining unit is configured to acquire the basic information of the transformer area to obtain a basic information characteristic value of the transformer area. The second obtaining unit is connected to the first obtaining unit and is configured to compare the basic information characteristic value with basic information characteristic values of transformer areas stored in a database to obtain a sensor deployment scheme corresponding to the basic information characteristic value.

[0115] As an optional embodiment, the first detection module 204 includes a first acquisition unit, a third obtaining unit, a first comparison unit and a first determination unit. The first acquisition unit is configured to acquire a flow state data set of the target sensor data based on the sensor deployment scheme. The third obtaining unit is connected to the first acquisition unit and is configured to obtain a 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 obtaining unit and is configured to compare the flow state evaluation value with a flow state boundary evaluation value of sensor data stored in a database. The first determination unit is connected to the first comparison unit and is configured 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 boundary evaluation value, and / or determine that the flow state of the target sensor data is not safe when the flow state evaluation value is less than the flow state boundary evaluation value, and issue a risk prompt for the flow state.

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

[0117] As an optional embodiment, the third detection module 208 includes a third acquisition unit, a fifth obtaining unit, a third comparison unit and a third determination unit. The third acquisition unit is configured to acquire a service usage state data set of the target sensor data in the case that the access state is safe. The fifth obtaining unit is connected to the third acquisition unit and configured to obtain a service usage state evaluation value of the target sensor data based on the service usage state data set. The third comparison unit is connected to the fifth obtaining unit and configured to compare the service usage state evaluation value with a defined service usage state evaluation value of the sensor data stored in the database. The third determination unit is connected to the third comparison unit and configured to determine that the service usage state of the target sensor data is safe when the service usage state evaluation value is greater than or equal to the defined service usage state evaluation value, and / or determine that the service usage state of the target sensor data is unsafe when the service usage state evaluation value is less than the defined service usage state evaluation value, and issue a risk prompt for the service usage state.

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

[0119] According to the embodiment of the present application, a computer readable storage medium is provided, which comprises a stored executable program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to perform the transformer area sensor data state detection method according to any one of the above embodiments when the executable program is executed.

[0120] According to the embodiment of the present application, an electronic device is provided, which comprises a memory storing an executable program and a processor configured to execute the program, wherein the program performs the transformer area sensor data state detection method according to any one of the above embodiments when the program is executed.

[0121] According to the embodiment of the present application, a computer program product is provided, which comprises a computer program, wherein the computer program is executed by a processor to implement the steps of the method according to any one of the above embodiments.

[0122] The above embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0123] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0124] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0125] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0126] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0127] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various program code storage media.

[0128] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for detecting the state of a sensor data in a transformer area, characterized by, The method comprises the following steps: obtaining a sensor deployment scheme based on basic information of a transformer area; detecting whether a flow state of target sensor data is safe based on the sensor deployment scheme; detecting whether an access state of the target sensor data is safe in the case that the flow state is safe; detecting whether a service usage state of the target sensor data is safe in the case that the access state is safe; performing safe processing on the target sensor data in the case that the service usage state is not safe; wherein, based on the sensor deployment scheme, obtaining a flow state 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 a flow state boundary evaluation value of sensor data stored in a database; when the flow state evaluation value is greater than or equal to the flow state boundary evaluation value, it is determined that the flow state of the target sensor data is safe; and / or, when the flow state evaluation value is less than the flow state boundary evaluation value, it is determined that the flow state of the target sensor data is not safe, and a risk prompt is issued for the flow state; wherein, in the case that the flow state is safe, obtaining an access state data set of the target sensor data; based on the access state data set, obtaining an access state evaluation value of the target sensor data; comparing the access state evaluation value with an access state boundary evaluation value of sensor data stored in a database; when the access state evaluation value is greater than or equal to the access state boundary evaluation value, it is determined that the access state of the target sensor data is safe; and / or, when the access state evaluation value is less than the access state boundary evaluation value, it is determined that the access state of the target sensor data is not safe, and a risk prompt is issued for the access state; wherein, in the case that the access state is safe, obtaining a service usage state data set of the target sensor data; based on the service usage state data set, obtaining a service usage state evaluation value of the target sensor data; comparing the service usage state evaluation value with a service usage state boundary evaluation value of sensor data stored in a database; when the service usage state evaluation value is greater than or equal to the service usage state boundary evaluation value, it is determined that the service usage state of the target sensor data is safe; and / or, when the service usage state evaluation value is less than the service usage state boundary evaluation value, it is determined that the service usage state of the target sensor data is not safe, and a risk prompt is issued for the service usage state.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining the basic information of the transformer area to obtain basic information characteristic values of the transformer area; The basic information characteristic value is compared with each basic information characteristic value of a district stored in a database to obtain a sensor deployment scheme corresponding to the basic information characteristic value.

3. The method of claim 1, wherein, The target sensor data is safely processed in the case that the business use state is unsafe, including: In the case that the business use state is unsafe, a business use state comparison characteristic value of the target sensor data is obtained based on the business use state evaluation value; The business use state comparison characteristic value is compared with each business use state comparison characteristic value stored in a database to obtain a data risk solution scheme corresponding to the business use state comparison characteristic value; The target sensor data is compared with each sensor data of a business use state at risk stored in a database to obtain a specific person in charge and a leak date corresponding to the target sensor data; The target sensor data is safely processed based on the data risk solution scheme, the specific person in charge and the leak date.

4. A device for detecting the state of a sensor data of a district, characterized by It includes: An acquisition module is configured to acquire a sensor deployment scheme based on basic information of a district; A first detection module is configured to detect whether a flow state of target sensor data is safe based on the sensor deployment scheme; A second detection module is configured to detect whether an access state of the target sensor data is safe in the case that the flow state is safe; A third detection module is configured to detect whether a business use state of the target sensor data is safe in the case that the access state is safe; A processing module is configured to safely process the target sensor data in the case that the business use state is unsafe; The first detection module is further configured to acquire a flow state data set of the target sensor data based on the sensor deployment scheme, obtain a flow state evaluation value of the target sensor data based on the flow state data set, compare the flow state evaluation value with a flow state boundary evaluation value of sensor data stored in a database, 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 boundary evaluation value, and / or determine that the flow state of the target sensor data is unsafe when the flow state evaluation value is less than the flow state boundary evaluation value, and issue a risk prompt for the flow state; The second detection module is further configured to acquire an access state data set of the target sensor data in the case that the flow state is safe, obtain an access state evaluation value of the target sensor data based on the access state data set, compare the access state evaluation value with an access state boundary evaluation value of sensor data stored in a database, determine 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 determine 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, and issue a risk prompt for the access state; The third detection module is further configured to, when the access state is safe, acquire service usage state data set of the target sensor data; obtain a service usage state evaluation value of the target sensor data based on the service usage state data set; compare the service usage state evaluation value with a service usage state boundary evaluation value of the sensor data stored in a database; when the service usage state evaluation value is greater than or equal to the service usage state boundary evaluation value, determine that the service usage state of the target sensor data is safe; and / or when the service usage state evaluation value is less than the service usage state boundary evaluation value, determine that the service usage state of the target sensor data is unsafe, and issue a risk prompt for the service usage state.

5. A computer readable storage medium, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the transformer area sensor data state detection method of any one of claims 1 to 3.

6. An electronic device, comprising: The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the transformer area sensor data state detection method of any one of claims 1 to 3. The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the computer readable storage medium is located to perform the transformer area sensor data state detection method of any one of claims 1 to 3. The computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that, ​

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