Security protection method and device for trading data of multiple market entities

Abstract

The present invention discloses a security protection method and device for multi-market entity transaction data. Among them, the method includes: obtaining the release time data, channel entry time data, and data arrival time data of the multi-market entity transaction data; determining the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data; obtaining the information benefit value of the multi-market entity transaction data; determining the information loss value of the multi-market entity transaction data; determining the trust degree of the multi-market entity transaction data in the network system according to the information benefit value and the information loss value; determining the data security protection strategy of the multi-market entity transaction data in the power Internet of Things based on the trust degree; and performing data security protection on the multi-market entity transaction data according to the data security protection strategy. The present invention solves the technical problem that the protection measures for multi-market entity transaction data are relatively simple, resulting in relatively large data security risks.

Description

Technical Field

[0001] The present invention relates to the technical field of data security, and more particularly, to a method and device for securing transaction data of multiple market players. Background Art

[0002] Objects are connected to the network through the Internet of Things (IoT), and relevant information of physical objects is detected at any time using technologies such as sensors, so as to track, locate, and manage the objects. With the wide application of technologies such as cloud computing and big data, in production and life, it has become increasingly important to quickly, real-time, and effectively search for information about objects in the real world and efficiently organize and manage this information. For example, searching for the location information of a current express delivery, searching for the best route from school to the gym, etc. Therefore, the IoT search technology has emerged as the times require. The DYAER search engine developed by the Swiss Federal Institute of Technology in Zurich, the University of Lübeck in Germany, and the Comoco Communication Laboratory in Germany supports the search for static and dynamic object information; the Shodan search engine provides online devices, and relevant devices connected to the Internet can be searched by entering keywords. The IoT search technology has penetrated into all aspects of people's lives, such as warehousing and logistics, health care, environmental monitoring, etc. While bringing convenience, the IoT search technology also has serious data security and privacy problems. IoT devices usually collect information about objects in daily life and hand it over to intelligent objects for storage, analysis, and management in order to provide various search services for users and return information that meets the search requests. However, if the IoT devices are maliciously attacked and exploited by attackers, it is very likely to lead to the leakage of private data.

[0003] In order to achieve data security protection during the search process, encryption algorithms are widely used in the IoT search technology. The attribute-based encryption scheme was first proposed by Sahai and Waters. Existing ABE schemes include the ciphertext-policy attribute encryption scheme (CP-ABE) in which the ciphertext is associated with the access structure and the key is associated with the user attributes, and the key-policy attribute encryption scheme (KP-ABE) in which the key is associated with the access structure and the ciphertext is associated with the user attributes. Due to the characteristics of CP-ABE itself, that is, CP-ABE does not require a completely trustworthy data storage system, it is more suitable for application in the IoT search technology.

[0004] Boneh et al. first proposed a basic public key encryption scheme that supports keyword search, but the algorithm efficiency and security performance of this scheme are both low. There is a literature that proposed a retrievable encryption scheme that supports attribute revocation, but the update of the key ciphertext in this scheme is jointly completed by the data owner, the attribute authorization agency, and the system, increasing the communication cost; a keyword search encryption scheme that supports multi-keyword search was proposed, improving the search accuracy, but the data owner needs to upload the encrypted keywords to the system, reducing the encryption efficiency.

[0005] After years of operation and construction, the application scenarios of data transmission solutions in the power industry can be generally divided into three categories: detection, control and power business information transmission. At this stage, the use of different data transmission solutions meets the current temporary needs and can basically ensure the safe and reliable operation of various power businesses. Data transmission solutions can be divided into wired and wireless forms to achieve remote data transmission or IoT data transmission.

[0006] Wired transmission solutions mainly include technologies such as optical fiber, power line carrier, Ethernet and bus. In the early days, bus or Ethernet technology was mainly used to meet the data transmission needs from detection or control. For the transmission of business information, power line carrier technology and industrial Ethernet technology are mainly used. In the detection scenario, Professor Cheng Yonghong's team at Xi'an Jiaotong University developed the first domestic comprehensive online monitoring system for power equipment based on fieldbus technology. The system realized multi-parameter online monitoring of a single transformer through PXI bus integration technology, which played a good demonstration role; Wang Xun and others from Hunan University designed a partial discharge monitoring system for GIS equipment through Ethernet technology. In the control information transmission scenario, Zhao Jianguo and others from Shandong University studied relay protection systems based on bus technology; Yang Qixun and others built a process bus communication experimental platform for transformer differential protection based on bus communication.

[0007] As optical fiber technology begins to be applied in the power industry, it gradually replaces the transmission media based on coaxial cables and twisted pairs in Ethernet and bus technologies with its advantages in transmission rate, bandwidth, reliability and real-time performance, and has derived xPON optical fiber technology to meet the stringent requirements of data transmission reliability and real-time performance in many business occasions such as detection, control, and business information flow in the power grid. According to statistics, as of 2019, 35kV and above power plants and self-owned property offices / business offices have achieved full optical fiber coverage.

[0008] Various wired transmission methods have been used so far. Although they can basically meet the data transmission needs of the power grid, they still have problems such as cumbersome wiring and line modification and limited expansion and upgrading of communication networks. In addition, during the transmission process, line noise, easy aging and damage of lines, etc. will greatly increase industrial costs and reduce work efficiency. Therefore, the wired method has restricted the flexibility of power grid development to a certain extent.

[0009] The current wireless transmission solutions in the power industry mainly include various solutions such as 230MHz wireless power private network, 3 / 4G cellular technology, satellite communication technology, WiFi, ZigBee, Bluetooth, and Low-Power Wide-Area Network (LPWAN) technology. In the initial stage of the investment in wireless transmission technology, it mainly replaced the wired detection services in the Internet of Things communication network, and technologies such as WiFi, ZigBee, and Bluetooth were selected as wireless solutions. These wireless transmission solutions have a short transmission distance and limited transmission rate, and are only suitable for transmitting some basic types of data, unable to meet the needs of high-bandwidth data transmission such as images and videos.

[0010] With the rapid development of cellular technology, satellite technology, and Low-Power Wide-Area Network (LPWAN) technology, as well as the strong construction of the power wireless private network in the power industry, the wireless transmission solution has been greatly improved in terms of coverage area, device power consumption, reliability, etc.

[0011] Among them, the LPWAN technology developed with the development of the Internet of Things technology has become a focus of attention due to its outstanding advantages of low power consumption and wide coverage. LPWAN technology can be divided into Narrow Band IoT (NB-IoT) technology and enhanced machine-type communication (eMTC) based on cellular technology and working under the authorized spectrum of operators, as well as Long Range (LoRa) technology and Sigfox technology working in the unlicensed spectrum. NB-IoT technology and eMTC technology, relying on their binding relationship with operators and performance characteristics of long transmission distance, large capacity, and strong anti-interference ability, are often combined with 3 / 4G cellular technology and 230MHz / 4G power wireless private network to complete data detection work, as well as control or power service information transmission services with a lower confidentiality level. For occasions with signal blind spots such as remote areas and long-distance transmission lines, satellite communication is often combined with LoRa or Sigfox technology to complete the data backhaul of detection services.

[0012] To sum up, the wireless transmission solution can basically replace the wired solution in detection services for more convenient distributed detection, which helps to promote the digitalization process in the new era. However, compared with the wired method, since the wireless method will inevitably face physical isolation and security issues such as interference and malicious attacks during transmission, the transmission of control information and power service information with a high confidentiality level in the power industry still needs to rely on technologies such as fiber optic private networks to achieve.

[0013] In response to the problems in the above-mentioned related technologies, no effective solution has been proposed yet. Summary of the Invention

[0014] An embodiment of the present invention provides a method and device for securing multi-market entity transaction data, at least to solve the technical problem that the protection measures for multi-market entity transaction data are relatively simple, resulting in relatively large data security risks.

[0015] According to one aspect of the embodiments of the present invention, a method for securing multi-market entity transaction data is provided, including: obtaining the release time data of the multi-market entity transaction data, the channel entry time data of the multi-market entity transaction data, and the data arrival time data of the multi-market entity transaction data; determining the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data; obtaining the information benefit value of the multi-market entity transaction data; determining the information loss value of the multi-market entity transaction data according to the error data one, the error data two, and the error data three; determining the trust level of the multi-market entity transaction data in the network system according to the information benefit value and the information loss value; determining the data security protection strategy of the multi-market entity transaction data in the power Internet of Things based on the trust level; and performing data security protection on the multi-market entity transaction data according to the data security protection strategy.

[0016] Optionally, obtaining the release time data of the multi-market entity transaction data, the channel entry time data of the multi-market entity transaction data, and the data arrival time data of the multi-market entity transaction data includes: using the Internet of Things perception system to obtain the release time data, the channel entry time data, and the data arrival time data of the multi-market entity transaction data from the data monitoring center, where the release time data, the channel entry time data, and the data arrival time data are all time data in the order of year, month, day, hour, minute, and second.

[0017] Optionally, determining the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data includes: respectively using a statistical analysis method for the release time data, the channel entry time data, and the data arrival time data to determine the error data one, the error data two, and the error data three.

[0018] Optionally, statistical analysis methods are respectively used for the release time data, the channel entry time data, and the data arrival time data to determine the first error data, the second error data, and the third error data, including: respectively determining the information detection errors of the release time data, the channel entry time data, and the data arrival time data on different time scales; determining the first error data, the second error data, and the third error data according to the information detection errors on the different time scales.

[0019] Optionally, obtaining the information benefit value of the multi-market entity transaction data includes: determining the information benefit value of the multi-market entity transaction data through a first formula, where the first formula is: R represents the information benefit value, is the first information benefit value formed by providing data transmission at 9 different rates to users, k Dvi is the first unit benefit value brought by the power Internet of Things providing data transmission at the i-th rate, is the second information benefit value formed by providing data storage sharing at 9 different scales to users, k DSi is the second unit benefit value brought by the power Internet of Things providing data storage sharing at the i-th rate, k Mi M H is the third information benefit value formed by the power Internet of Things providing data detection for the hydro-generator set in the perception layer, k Mi M T is the fourth information benefit value formed by the power Internet of Things providing data detection for the thermal power unit in the perception layer, k Mi M N is the fifth information benefit value formed by the power Internet of Things providing data detection for the nuclear power unit in the perception layer, k Mi M G is the sixth information benefit value formed by the power Internet of Things providing data detection for the gas turbine unit in the perception layer, k Mi is the third unit benefit value brought by the power Internet of Things providing data detection for the unit in the perception layer.

[0020] Optionally, determining the information loss value of the multi-market entity transaction data according to the first error data, the second error data, and the third error data includes: determining the information loss value of the multi-market entity transaction data through a second formula and the first error data, the second error data, and the third error data, where the second formula is: L represents the information loss value, represents the influence coefficient of the first error data on the power data release, is the unit power information loss value caused by the error data one, is the influence coefficient of the error data two on the power data publication, is the unit power information loss value caused by the error data two, is the influence coefficient of the error data three on the power data publication, is the unit power information loss value caused by the error data three, represents the error data one, represents the error data two, represents the error data three.

[0021] Optionally, determining the trust level of the multi-market entity transaction data in the network system according to the information gain value and the information loss value includes: determining the trust level through the third formula and the information gain value and the information loss value, where the third formula is: B represents the trust level, R represents the information gain value, L represents the information loss value, and the trust level is the average value of the trust levels in the network system.

[0022] Optionally, determining the data security protection strategy of the multi-market entity transaction data in the power Internet of Things based on the trust level includes: determining the data security protection strategy corresponding to the trust level through a security protection optimization model, where the security protection optimization model is represented by the fourth formula, and the fourth formula is: maxB, B represents the trust level, represents the error data one, represents the error data two, represents the error data three.

[0023] According to another aspect of the embodiments of the present invention, there is also provided a security protection device for multi-market entity transaction data, including: a first acquisition unit for acquiring the release time data of the multi-market entity transaction data, the channel entry time data of the multi-market entity transaction data, and the data arrival time data of the multi-market entity transaction data; a first determination unit for determining the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data; a second acquisition unit for acquiring the information benefit value of the multi-market entity transaction data; a second determination unit for determining the information loss value of the multi-market entity transaction data according to the error data one, the error data two, and the error data three; a third determination unit for determining the trust level of the multi-market entity transaction data in the network system according to the information benefit value and the information loss value; a fourth determination unit for determining the data security protection strategy of the multi-market entity transaction data in the power Internet of Things based on the trust level; and a protection unit for performing data security protection on the multi-market entity transaction data according to the data security protection strategy.

[0024] Optionally, the first acquisition unit includes: a first acquisition subunit for acquiring the release time data, the channel entry time data, and the data arrival time data of the multi-market entity transaction data from a data monitoring center by using an Internet of Things perception system, wherein the release time data, the channel entry time data, and the data arrival time data are all time data in the order of year, month, day, hour, minute, and second.

[0025] Optionally, the first determination unit includes: a first determination subunit for respectively determining the error data one, the error data two, and the error data three of the release time data, the channel entry time data, and the data arrival time data by using a statistical analysis method.

[0026] Optionally, the first determination subunit includes: a first determination module for respectively determining the information detection errors of the release time data, the channel entry time data, and the data arrival time data on different time scales; and a second determination module for determining the error data one, the error data two, and the error data three according to the information detection errors on the different time scales.

[0027] Optionally, the second acquisition unit includes: a second determination subunit for determining the information benefit value of the multi-market entity transaction data through a first formula, wherein the first formula is: R represents the information benefit value, is the first information benefit value formed by providing data transmission to users at 9 different rates, kDvi is the first unit revenue value brought by the power Internet of Things providing data transmission at the i-th rate, is the second information revenue value formed by providing data storage sharing for users in 9 different scales, k DSi is the second unit revenue value brought by the power Internet of Things providing data storage sharing at the i-th rate, k Mi M H is the third information revenue value formed by the power Internet of Things providing data detection for the hydro-generating unit in the perception layer, k Mi M T is the fourth information revenue value formed by the power Internet of Things providing data detection for the thermal power unit in the perception layer, k Mi M N is the fifth information revenue value formed by the power Internet of Things providing data detection for the nuclear power unit in the perception layer, k Mi M G is the sixth information revenue value formed by the power Internet of Things providing data detection for the gas turbine unit in the perception layer, k Mi is the third unit revenue value brought by the power Internet of Things providing data detection for the unit in the perception layer.

[0028] Optionally, the second determination unit includes: a second determination subunit, configured to determine the information loss value of the multi-market entity transaction data through the second formula and the error data one, the error data two, and the error data three, where the second formula is: L represents the information loss value, represents the influence coefficient of the error data one on the power data publication, is the unit power information loss value brought by the error data one, is the influence coefficient of the error data two on the power data publication, is the unit power information loss value brought by the error data two, is the influence coefficient of the error data three on the power data publication, is the unit power information loss value brought by the error data three, represents the error data one, represents the error data two, represents the error data three.

[0029] Optionally, the third determination unit includes: a third determination subunit, configured to determine the trust degree through the third formula and the information revenue value and the information loss value, where the third formula is: B represents the trust degree, R represents the information benefit value, L represents the information loss value, and the trust degree is the average value of the trust degrees in the network system.

[0030] Optionally, the fourth determination unit includes: a fourth determination subunit, configured to determine the data security protection policy corresponding to the trust degree through a security protection optimization model, where the security protection optimization model is represented by a fourth formula, and the fourth formula is: maxB, B represents the trust degree, represents the first error data, represents the second error data, represents the third error data.

[0031] According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored program, where, when the program runs, it controls the device where the non-volatile storage medium is located to execute any one of the above-mentioned security protection methods for multi-market entity transaction data.

[0032] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including: a memory and a processor, where the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, and when the computer program runs, it causes the processor to execute any one of the above-mentioned security protection methods for multi-market entity transaction data.

[0033] According to another aspect of the embodiments of the present invention, a computer program product is further provided, including a computer program, and when the computer program is executed by a processor, it implements any one of the above-mentioned security protection methods for multi-market entity transaction data.

[0034] In an embodiment of the present invention, the release time data of multi-market entity transaction data, the channel entry time data of multi-market entity transaction data, and the data arrival time data of multi-market entity transaction data are obtained; the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data are determined; the information benefit value of the multi-market entity transaction data is obtained; the information loss value of the multi-market entity transaction data is determined according to the error data one, the error data two, and the error data three; the trust level of the multi-market entity transaction data in the network system is determined according to the information benefit value and the information loss value; the data security protection strategy of the multi-market entity transaction data in the power Internet of Things is determined based on the trust level; and the data security protection of the multi-market entity transaction data is performed according to the data security protection strategy. Through the technical solution provided by the present invention, the purpose of data security protection for the power Internet of Things is achieved by means of statistical analysis, based on the time data information of the release, channel entry, and data arrival of the multi-market entity transaction data to detect errors, as well as the information benefit value of the multi-market entity transaction data and the information loss value of the error detection data, and the technical effect of improving the security of the data in the power Internet of Things is achieved, thereby solving the technical problem that the protection measures for the multi-market entity transaction data are relatively simple, resulting in relatively large data security risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation to the present invention. In the drawings:

[0036] Figure 1 is a hardware structure block diagram of a mobile terminal for a security protection method of multi-market entity transaction data according to an embodiment of the present invention;

[0037] Figure 2 is a flowchart of a security protection method of multi-market entity transaction data according to an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of a security protection device for multi-market entity transaction data according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0041] The further construction and development of the power Internet of Things is continuing. With the deployment of more intelligent sensing devices in various links of the power source, grid, load and energy storage, and the deepening of the requirements for precise control and two-way interaction, revolutionary changes will occur in the detection, control and business information transfer services of the data transmission scheme.

[0042] 1) In the detection application scenarios, three aspects will be deepened.

[0043] ① The detection scope is broadened: from the detection of primary power equipment information to the detection of secondary power equipment and information data in various environmental control, multimedia scenarios, user sides, etc., in order to obtain more comprehensive digital perception, strengthen the management of power assets, and deepen the understanding of the energy flow of the power Internet of Things and the energy Internet. ② The detection content is diversified: on the basis of the detection of basic data, images and voices, the transmission of high-definition videos is added to meet the requirements of large-scale video applications in the power grid such as inspection, monitoring, and emergency site self-organizing network comprehensive applications. ③ The detection frequency is made real-time: for the development of applications such as meeting the future demand-side management of electricity load and real-time user pricing, the detection frequency is expected to be increased from the current detection in days and hours to the quasi-real-time level in minutes.

[0044] 2) In the control application scenarios, with the development of applications such as distributed energy regulation and precise load control, the requirement for latency will reach the ms level.

[0045] 3) In the business information transfer scenarios, two-way interaction is strengthened on the premise of ensuring accurate, real-time, secure and confidential information transfer, so as to achieve the purpose of strengthening the management and coordination of various power services.

[0046] In summary, in the further construction of the power Internet of Things, the data transmission demand will show an explosive growth, and there are higher requirements for the rate, connection density, bandwidth, and latency of wireless transmission solutions. Although wireless transmission technologies have developed new technologies such as v5.2 low-power Bluetooth and Beidou Generation 4 to meet the continuously improving business requirements, they still seem powerless in coping with the digital transformation of the power Internet of Things and are difficult to support the development of the power Internet of Things in the digital era.

[0047] As the core technology in the construction of the power Internet of Things, data transmission faces huge challenges and urgently needs to introduce a "ubiquitous, full-coverage, and high-efficiency" wireless transmission solution that can achieve secure and reliable, flexible access, and two-way real-time interaction for support. Fortunately, with the gradual maturity of the application of 5G technology in other industry fields, the power Internet of Things will be one of the largest scenarios for 5G applications.

[0048] 5G refers to the fifth-generation technical standard of cellular networks. 5G has developed rapidly and has advantages far beyond the corresponding indicators of the existing 4G in terms of performance indicators such as bandwidth, latency, and transmission rate.

[0049] The International Telecommunication Union (ITU) summarizes the basic characteristics of 5G as: high rate, high capacity, high reliability, low latency, and low power consumption. Such characteristics are called "three highs and two lows".

[0050] 1) The peak data transmission speed of 5G (theoretical maximum speed) can reach 10 Gbit / s for uplink and 20 Gbit / s for downlink, about 20 times that of 4G technology. For the massive and diversified data detection services in the power system, the high rate can provide strong support for them.

[0051] 2) 5G has a spectrum width of hundreds of megahertz or even gigahertz, can support high-density connections of 1 million devices per square kilometer, and support large-capacity data transmission of 10 Mbit / s per square meter. This performance indicator is hundreds of times that of 4G technology. This will be able to provide better solutions for advanced metering services, grid large video applications, etc. that need to access a large number of terminal devices in various fields of the power Internet of Things (especially in the wireless access challenge of the "last mile" of the distribution communication network).

[0052] 3) 5G supports its high reliability through multi-connection technology. Its theoretical index is a packet loss rate of 0.001%, which can be comparable to fiber optic communication and is expected to provide high-reliability wireless data connections for the power system.

[0053] 4) In terms of low latency, the latency of 4G often exceeds 50 ms, and such performance is not applicable to the above scenarios. However, the expected performance index of the end-to-end delay of 5G is 1 ms, which can provide flexible and timely responses for many collaborative control scenarios.

[0054] 5) 5G features low power consumption. By optimizing the sleep / activity ratio, setting the sleep mode when there is no data transmission, and using network slicing technology, devices can operate in a low-power mode, maintaining low energy consumption of intelligent terminal devices in the power IoT, thus keeping maintenance costs and device costs low and ensuring the device lifespan (usually at least 10 years for industrial applications).

[0055] ITU has defined three major 5G scenarios: Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low Latency Communications (uRLLC), and Massive Machine Type Communications (mMTC).

[0056] Typical applications defined for eMBB include ultra-high-definition video, virtual reality, augmented reality, etc. Typical applications for uRLLC include wireless control of autonomous driving, industrial control, remote medical surgery, smart grid, intelligent transportation, public protection and disaster relief, etc. These scenarios focus on services that are extremely sensitive to latency and reliability. Typical applications for mMTC include smart grid, smart home, and smart city, etc. These scenarios require a high connection density and exhibit diverse industry heterogeneity and differentiation.

[0057] The eMBB scenario mainly meets the requirements of some high-bandwidth services and strengthens data detection application scenarios and service information transmission scenarios. Currently, the applications of the power IoT in this regard are mainly large grid videos, including substation robot inspections, online monitoring of transmission line drones, distribution room video surveillance, mobile on-site construction operation management, and emergency site self-organizing network comprehensive applications, etc. Many researchers have tried to conduct experiments using 5G technology in certain scenarios and achieved certain results.

[0058] The uRLLC scenario strengthens control application scenarios and service information transmission scenarios, mainly including wireless control in the power IoT and service information transmission such as power system dispatching. Different services in the production control area of the power system have different requirements for latency and reliability. Specific services include distributed distribution automation, distributed energy regulation, differential current protection of the distribution network, and demand-side response of electricity loads.

[0059] Existing data transmission solutions such as power optical fiber and private wireless network have various problems such as high cost, poor stability, and high latency. 5G technology is expected to support these services that require low latency and high reliability. Relevant literature has made attempts based on the combination of 5G data transmission technology and differential current protection systems. In engineering demonstrations, the action delay of differential protection is about 67 - 71 ms, and the stability is good.

[0060] To meet the latency target of ms-level precise load control services, a new Internet of Things - Grid (IoT - G) data transmission solution is proposed. This technology is a transition before 5G technology is fully mature, inherits the low-latency design concept of 5G systems, and supports spectrum aggregation technology. Field test results show that the IoT - G data transmission solution meets the requirements for grid services in terms of latency, data rate, capacity, and coexistence.

[0061] The key use of the mMTC scenario is to connect a large number of deployed sensing terminal devices to meet the business requirements of massive connections, which is a comprehensive improvement of detection services. Currently in the power grid, on the one hand, due to the limitations of data transmission technology, many sensing terminals only collect and upload partial information; on the other hand, only very sparse sensing terminals are equipped in local systems. This "sparse digitization" leaves many blind spots in the operation monitoring of equipment and systems, and a lot of data such as physical, chemical, meteorological states and electricity consumption information that are worthy of monitoring are missed.

[0062] In the power Internet of Things, by using more sensing devices, a more in-depth understanding of the operating state of power equipment and the energy flow in the power grid can be achieved, and it helps to realize two-way interactive services such as information detection, energy efficiency management, and smart appliances in the power consumption link (distributed power sources, charging piles, residential users, etc.).

[0063] Some scholars have introduced how the smart grid benefits from advanced distributed state estimation methods in a 5G environment and outlined emerging distributed state estimation solutions. These scholars believe that the emergence of 5G will greatly promote the provision of distributed information acquisition and processing services required by wide-area measurement systems, thus providing an ideal stage for the development of future distributed smart grid services.

[0064] Currently, various data transmission solutions are constantly evolving. v5.2 low-power Bluetooth, NB-IoT, xPON fiber, Beidou-4 satellite, etc. have all provided more efficient data information transmission solutions for the power grid. However, to meet the construction requirements of the power Internet of Things under digital transformation and energy revolution and build a data transmission network with unified standards, high response capabilities, high robustness, and strong scalability, there are still many problems to be solved.

[0065] The emerging 5G data transmission technology gradually replaces some of the existing wireless and wired transmission solutions with its outstanding performance advantages, the convenience of leveraging existing communication infrastructure, and the support for network slicing technology. While meeting current demands, it can also proactively guide comprehensive business development. Its application in the power Internet of Things is imperative. However, in the initial stage of application, some challenges can be foreseeably expected, mainly including:

[0066] For the selection of data transmission solutions in the power Internet of Things, issues such as the confidentiality level of transmitted data, service characteristics, and communication physical environment need to be comprehensively considered. 5G cannot completely replace the current data transmission solutions. For example, in the transmission of data such as control signals and dispatching voices that require strict security and reliability guarantees, the current 5G technology cannot replace the role of power optical fiber dedicated lines because it cannot rule out the possibility of being interfered with and attacked. The heterogeneous integration of 5G with existing diverse data transmission solutions will be a challenge, and their coexistence and mutual cooperation will be the norm.

[0067] As an essential supporting part of the communication bearer network, time synchronization technology plays a very crucial role. By solving problems such as how to build a high-precision space-ground integrated time synchronization network by combining satellite technology; how to optimize existing synchronization transmission technologies (such as 1588v2) to improve the time processing accuracy of individual terminal nodes, etc., it is very important to provide a unified time reference, ensure the validity of transmitted data, and meet the service requirements of synchronous phasor measurement, digital differential protection, fault location, etc.

[0068] The main power-consuming links of the 5G wireless transmission system are a large number of communication terminal devices and communication base stations. With the advent of the power Internet of Things era, deploying a super-dense communication terminal devices and base stations, a huge energy consumption will be foreseeable. Therefore, improving the energy efficiency of 5G data transmission is very important for the power Internet of Things. For 5G terminal devices, in addition to directly starting with hardware and developing low-power-consuming devices, considering how to obtain energy using environmental parameters such as radio frequency and temperature difference, and researching integrated low-power passive design will be the direction for researchers to think about. For communication base stations, optimizing the base station settings with the maximization of energy efficiency as the operation idea, interacting with the power distribution network supply and demand

[58] , and using renewable energy will be feasible solutions.

[0069] As a new generation of wireless cellular technology, the communication network used in the power Internet of Things (IoT) can be private or public. While strengthening the construction of the private power 5G network, in the era of the sharing economy, it will be a trend to complete the transmission of some business data of the power IoT through the public network formed by existing communication infrastructure. Both the wireless transmission method and the use of the public network will bring new types of security risks to the power grid. Ensuring the security of 5G network access, 5G terminal security, slice security, and edge computing security, supporting unified identity management and authentication, supporting the construction of diversified trust relationships, exploring privacy protection strategies, and establishing and analyzing corresponding threat models will all contribute to establishing the security usage standards of 5G wireless transmission technology in the power IoT.

[0070] For the data transmission scheme in the power IoT, its development trend is mainly reflected in the following two aspects:

[0071] 1) Conform to the digital development and transformation, and face diversified massive information;

[0072] At present, the data transmission requirements in the power grid mainly come from the operation monitoring and control of various power equipment, the detection of power consumption information, and the dispatching of the power system, etc. However, with the improvement of requirements in the construction goal of the power IoT for more comprehensive and in-depth distributed detection services, precise linkage between control services and the main grid, as well as the proposed new requirements at multiple levels and in multiple aspects such as cooperating with artificial intelligence and big data technologies to achieve comprehensive perception and analysis, strengthening two-way interaction on the user side, and interconnecting and coordinating multiple energies, the data flow information in the power IoT has more distinct characteristics such as diversity, complexity, and massiveness.

[0073] In the trend of richer communication content, it is necessary to consider the differentiation of business communication requirements specifically, realize data transmission services with mutual security isolation and customizable functions, and make adaptations and improvements in corresponding aspects such as bandwidth, rate, and latency, so as to support the multi-source fusion big data analysis in the power IoT.

[0074] Facing future diversified business forms and massive information interaction, it is necessary to establish the communication architecture construction concept of "comprehensive access, integrated bearing, and business penetration". Building an integrated communication network is an inevitable trend. A unified platform provides a secure and reliable data transmission network with strong bearing capacity for information interaction of basic units within the perception layer, unified data access upward to the platform layer, or feedback and interaction of internal and external information based on application layer information to achieve business penetration. The integrated communication network, with its wide coverage and flexible resource allocation, will surely promote the further in-depth development of the informatization, specialization, and scientific level of the power IoT. This will lay a foundation for data sharing and energy interaction in the power grid and assist the business development of the future energy Internet.

[0075] In terms of trustworthiness calculation, there are many research results. For example, the Eigen Trust algorithm based on reputation obtains the unique reputation value of each user within the global scope through trust iteration on the user trust chain within the global scope, and uses this unique reputation value to represent the trust value of the user within this global scope. This algorithm only gives a method for iteratively calculating the trust value, without considering its convergence speed in calculating the trust value, and at the same time does not consider the possible security threats; the trust model based on Bayesian network calculates the trustworthiness of the current node by using the method of Bayesian probability based on the previous transaction feedback evaluation. The contribution of this scheme is to introduce the Bayesian network into the trustworthiness calculation, but this scheme also does not consider the security threats posed by malicious nodes. At the same time, the trustworthiness based on the Bayesian network needs to process the evaluation sample space before calculation to make it conform to a certain probability distribution, increasing the complexity of the algorithm; the PeerTrust algorithm based on trust. The advantage of the Peer Trust algorithm is that it considers five factors affecting the accuracy of trustworthiness calculation and the anti-attack ability of the algorithm when calculating the trustworthiness, namely the feedback transaction evaluation, the recommended trust value of the evaluation node, the time factor of the transaction, and the incentive mechanism of the transaction. Therefore, this algorithm better combats the behavior of dishonest users and has better anti-attack ability; the dynamic trust model based on weight studies the accuracy and anti-attack ability of the algorithm, focuses on the contribution of the time decay factor and the penalty incentive factor to the accuracy of trustworthiness calculation and the anti-attack ability, proposes a dynamic trust model based on weight, and experimentally gives the suppression effect of the time decay factor and the penalty incentive factor under different attack models, and at the same time proves that it has a good inhibitory effect on strategic attack behaviors. Currently, there are many methods for calculating trustworthiness. For example, the reputation value of a node within the global scope is calculated through multiple iterations of the trust chain, the method of probability theory is introduced to solve the trust problem, and the performance of a trust model algorithm based on Bayesian network is given. The time decay and penalty factors of the algorithm are considered when calculating the trust, improving the accuracy of the algorithm and the anti-strategy attack ability.

[0076] As introduced in the background art, the protection measures for the transaction data of multiple market entities in the related technology are relatively simple, resulting in relatively large data security risks. In view of the above defects, in the embodiments of the present invention, a security protection method and device for the transaction data of multiple market entities are provided.

[0077] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0078] The method embodiments provided in the embodiments of the present invention can be executed on a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1It is a hardware block diagram of a mobile terminal for a method of securing multi-market entity transaction data according to an embodiment of the present invention. As Figure 1 shown, the mobile terminal may include one or more ( Figure 1 only one is shown in Figure 1 ) processors 102 (the processor 102 may include, but is not limited to, processing devices such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Among them, the above mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown in Figure 1 is only illustrative and does not limit the structure of the above mobile terminal. For example, the mobile terminal may further include more or fewer components than

[0079] shown in

[0080] or have a different configuration from that shown in

[0081] The memory 104 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the method of securing multi-market entity transaction data according to an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories may be connected to the mobile terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include the wireless network provided by the communication provider of the mobile terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (Radio Frequency, abbreviated as RF) module, which is used to communicate with the Internet wirelessly. According to an embodiment of the present invention, a method embodiment of a method for securing multi-market entity transaction data 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 from that here.

[0081] Figure 2 is a flowchart of a security protection method for multi-market entity transaction data according to an embodiment of the present invention. As Figure 2 shown, the security protection method for multi-market entity transaction data includes the following steps:

[0082] Step S202, obtain the release time data of the multi-market entity transaction data, the channel entry time data of the multi-market entity transaction data, and the data arrival time data of the multi-market entity transaction data.

[0083] In this embodiment, an Internet of Things perception system can be used to obtain relevant information of the multi-market entity transaction data from the monitoring data center. For example, the release time data, the channel entry time data, and the data arrival time data.

[0084] Step S204, determine the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data.

[0085] In this embodiment, the error data of the above-mentioned each data can be obtained respectively, so as to be used for subsequent data security protection processing.

[0086] Step S206, obtain the information benefit value of the multi-market entity transaction data.

[0087] Step S208, determine the information loss value of the multi-market entity transaction data according to the error data one, the error data two, and the error data three.

[0088] Step S210, determine the trust level of the multi-market entity transaction data in the network system according to the information benefit value and the information loss value.

[0089] Step S212, determine the data security protection strategy of the multi-market entity transaction data in the power Internet of Things based on the trust level.

[0090] Step S214, perform data security protection on the multi-market entity transaction data according to the data security protection strategy.

[0091] As described above, in the embodiments of the present invention, the release time data of multi-market entity transaction data, the channel entry time data of multi-market entity transaction data, and the data arrival time data of multi-market entity transaction data can be obtained; the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data are determined; the information benefit value of the multi-market entity transaction data is obtained; the information loss value of the multi-market entity transaction data is determined according to the error data one, the error data two, and the error data three; the trust degree of the multi-market entity transaction data in the network system is determined according to the information benefit value and the information loss value; the data security protection strategy of the multi-market entity transaction data in the power Internet of Things is determined based on the trust degree; and the data security protection of the multi-market entity transaction data is carried out according to the data security protection strategy, achieving the purpose of data security protection for the power Internet of Things by means of statistical analysis, based on the time data information of the release, channel entry, and data arrival of the multi-market entity transaction data to detect errors, as well as the information benefit value of the multi-market entity transaction data and the information loss value of the error detection data, and achieving the technical effect of improving the security of the data in the power Internet of Things.

[0092] Therefore, through the technical solution provided by the above embodiments of the present invention, the technical problem that the protection measures for multi-market entity transaction data are relatively simple, resulting in relatively large data security risks, is solved.

[0093] According to the above embodiments of the present invention, obtaining the release time data of multi-market entity transaction data, the channel entry time data of multi-market entity transaction data, and the data arrival time data of multi-market entity transaction data may include: using the Internet of Things perception system to obtain the release time data, the channel entry time data, and the data arrival time data of multi-market entity transaction data from the data monitoring center, where the release time data, the channel entry time data, and the data arrival time data are all time data in the order of year, month, day, hour, minute, and second.

[0094] In this embodiment, the Internet of Things perception system can be used to obtain the time data information of the year, month, day, hour, minute, and second of the release of multi-market entity transaction data from the monitoring data center, respectively represented as Using the Internet of Things perception system, obtain the time data information of the year, month, day, hour, minute, and second when the multi-market entity transaction data enters the channel from the monitoring data center, respectively represented as And use the Internet of Things perception system to obtain the time data information of the year, month, day, hour, minute, and second when the multi-market entity transaction data arrives from the monitoring data center, respectively represented as

[0095] According to the above embodiments of the present invention, determining the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data includes: respectively using a statistical analysis method for the release time data, the channel entry time data, and the data arrival time data to determine the error data one, the error data two, and the error data three.

[0096] In this embodiment, a statistical analysis method can be used to determine the error data of the release time data, the channel entry time data, and the data arrival time data.

[0097] According to the above embodiments of the present invention, respectively using a statistical analysis method for the release time data, the channel entry time data, and the data arrival time data to determine the error data one, the error data two, and the error data three includes: respectively determining the information detection errors of the release time data, the channel entry time data, and the data arrival time data on different time scales; determining the error data one, the error data two, and the error data three according to the information detection errors on different time scales.

[0098] In this embodiment, a statistical analysis method is used to calculate and determine the information detection errors of the time data of the multi-market entity transaction data for year, month, day, hour, minute, and second of release, which are respectively represented as The information detection error of the data release timestamp is: At the same time, a statistical analysis method can be used to calculate and determine the information detection errors of the time data of the multi-market entity transaction data for year, month, day, hour, minute, and second of channel entry, which are respectively represented as The information detection error of the channel entry timestamp is: And a statistical analysis method can be used to calculate and determine the information detection errors of the time data of the multi-market entity transaction data for year, month, day, hour, minute, and second of arrival, which are respectively represented as The information detection error of the data arrival timestamp is:

[0099] According to the above embodiments of the present invention, obtaining the information benefit value of the multi-market entity transaction data includes: determining the information benefit value of the multi-market entity transaction data through a first formula, where the first formula is: R represents the information benefit value, is the first information benefit value formed by providing data transmission at 9 different rates to users, k Dvi is the first unit benefit value brought by the power Internet of Things providing data transmission at the i-th rate, is the second information benefit value formed by providing data storage sharing at 9 different scales to users, k DSiis the second unit revenue value brought by the data storage and sharing provided by the power Internet of Things at the i-th rate, k Mi M H is the third information revenue value formed by the power Internet of Things providing data detection for hydropower units at the perception layer, k Mi M T is the fourth information revenue value formed by the power Internet of Things providing data detection for thermal power units at the perception layer, k Mi M N is the fifth information revenue value formed by the power Internet of Things providing data detection for nuclear power units at the perception layer, k Mi M G is the sixth information revenue value formed by the power Internet of Things providing data detection for gas turbine units at the perception layer, k Mi is the third unit revenue value brought by the power Internet of Things providing data detection for units at the perception layer.

[0100] In this embodiment, at the perception layer, data information such as power market electricity prices, power electricity demand and its quotations, output power and quotations of various generator sets are obtained through the perception system. At the platform layer, the sharing and interaction of power system generation data are realized through the big data system. At the network layer, data transmission is realized through the network system, enabling power generators to obtain sufficient information through the Internet of Things and gain benefits. The information revenue value R of the trading data of multiple market players is calculated according to the above first formula.

[0101] In the above first formula, R is the information revenue value formed by the power Internet of Things providing data detection, transmission, storage and sharing to users in market transactions, which can be the information revenue value formed by providing data transmission to users at 9 fuzzy uncertainty rates of extremely low, very low, low, relatively low, medium, relatively high, high, very high, extremely high; is the information revenue value formed by providing data storage and sharing to users at 9 fuzzy uncertainty scales of extremely low, very low, low, relatively low, medium, relatively high, high, very high, extremely high. E[] is the expected value of [], represents the union of 9 fuzzy sets.

[0102] M H 、M T 、M N 、M G can be represented by two-dimensional trapezoidal fuzzy sets, and the specific formula is as follows: M H =(M H1 ,M H2 ,M H3 ,M H4 ;k H ), M T =(M T1 ,MT2 , M T3 , M T4 ; k T ), M N = (M N1 , M N2 , M N3 , M H4 ; k N ), M G = (M G1 , M G2 , M G3 , M G4 ; k G ).

[0103] In the above formula, M H is a two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for hydropower units at the perception layer, and M H1 , M H2 , M H3 , M H4 , and k H are respectively the fuzzy set and its membership degree coefficient of the two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for hydropower units at the perception layer; M T is a two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for thermal power units at the perception layer, and M T1 , M T2 , M T3 , M T4 , and k T are respectively the fuzzy set and its membership degree coefficient of the two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for thermal power units at the perception layer; M N is a two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for nuclear power units at the perception layer, and M N1 , M N2 , M N3 , M N4 , and k N are respectively the fuzzy set and its membership degree coefficient of the two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for nuclear power units at the perception layer; M G is a two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for gas power units at the perception layer, and M G1 , M G2 , M G3 , M G4 , and k G are respectively the fuzzy set and its membership degree coefficient of the two-dimensional trapezoidal fuzzy set for the data detection scale provided by the Internet of Things for gas power units at the perception layer.

[0104] According to the above embodiments of the present invention, determining the information loss value of multi-market entity transaction data based on error data one, error data two, and error data three may include: determining the information loss value of multi-market entity transaction data through the second formula and error data one, error data two, and error data three, where the second formula is: L represents the information loss value, represents the influence coefficient of error data one on power data publication, is the unit power information loss value brought by error data one, is the influence coefficient of error data two on power data publication, is the unit power information loss value brought by error data two, is the influence coefficient of error data three on power data publication, is the unit power information loss value brought by error data three, represents error data one, represents error data two, represents error data three.

[0105] In this embodiment, the information detection errors of data publication, channel entry, and data arrival timestamp formed in the power Internet of Things will cause information loss in market transactions, and its value is calculated according to the above second formula; L is the information loss value of market transactions caused by the information detection errors of data publication, channel entry, and data arrival timestamp formed in the power Internet of Things; is the influence coefficient or weight coefficient of the information detection error of the data publication timestamp on power data publication, is the unit power information loss value brought by the information detection error of the data publication timestamp; is the influence coefficient or weight coefficient of the information detection error of the channel entry timestamp on power data publication, is the unit power information loss value brought by the information detection error of the channel entry timestamp; is the influence coefficient or weight coefficient of the information detection error of the data arrival timestamp on power data publication, is the unit power information loss value brought by the information detection error of the data arrival timestamp.

[0106] According to the above embodiments of the present invention, determining the trust degree of multi-market entity transaction data in the network system based on the information gain value and the information loss value includes: determining the trust degree through the third formula and the information gain value and the information loss value, where the third formula is: B represents the trust degree, R represents the information gain value, L represents the information loss value, and the trust degree is the average value of the trust degrees in the network system.

[0107] In this embodiment, the trustworthiness of multi-market entity transaction data in the network system can be calculated through the above fourth formula. It should be noted that the trustworthiness here is the average value of the trustworthiness of multi-market entity transaction data in the network system.

[0108] According to the above embodiment of the present invention, based on the trustworthiness, a data security protection strategy for multi-market entity transaction data in the power Internet of Things is determined, including: determining a data security protection strategy corresponding to the trustworthiness through a security protection optimization model, where the security protection optimization model is represented by the fourth formula, and the fourth formula is: maxB, B represents the trustworthiness, represents error data one, represents error data two, represents error data three.

[0109] In this embodiment, in the multi-market entity transaction data information security protection optimization model, the goal of security access control is to achieve: 1) maximizing the average value of the trustworthiness of multi-market entity transaction data in the network system; 2) minimizing the information detection error of power data publication, channel entry, and data arrival timestamp in the power Internet of Things. Taking the maximization of the average value of the trustworthiness of multi-market entity transaction data in the network system and the minimization of the information detection error of power data publication, channel entry, and data arrival timestamp as the objective function, and taking the amount of information transmitted by multi-market entity transaction data as the decision variable, a multi-market entity transaction data information security protection optimization model is constructed, and its multi-objective function is represented by the above fourth formula.

[0110] In addition, the constraint conditions for constructing the multi-market entity transaction data information security protection optimization model include the following:

[0111] (1). Constraint condition of user signal traffic: The signal traffic of the i-th user should meet the requirement of being greater than its allowed maximum value and less than its allowed minimum value: In the formula, V Vi , V Vi are the actual value, allowed maximum value, and minimum value of the signal traffic of the i-th user respectively.

[0112] (2). Constraint condition of user signal transmission speed: The signal transmission speed of the i-th user should meet the requirement of being greater than its allowed minimum value: v Vi ≤v Vi , in the formula, v Vi , v Vi are the actual value and allowed minimum value of the signal transmission speed of the i-th user respectively.

[0113] (3). Constraint conditions for channel traffic: The traffic of the $i$-th channel should meet the requirements of being greater than its allowed maximum value and less than its allowed minimum value: where $V$ Xi , V Xi are the actual value, the allowed maximum value, and the allowed minimum value of the traffic of the $i$-th channel, respectively.

[0114] (4). Constraint conditions for channel transmission speed: The transmission speed of the $i$-th channel should meet the requirements of being greater than its allowed maximum value and less than its allowed minimum value: where $v$ Xi , v Xi are the actual value, the allowed maximum value, and the allowed minimum value of the transmission speed of the $i$-th channel, respectively.

[0115] (5). Constraint conditions for network service quality: The network service quality should meet the requirements of being greater than its allowed maximum value and less than its allowed minimum value: where $k$ QoS , k QoS are the actual value, the allowed maximum value, and the allowed minimum value of the network service quality, respectively.

[0116] (6). Constraint conditions for signal transmission delay: The signal transmission delay of the $i$-th channel should meet the requirements of being greater than its allowed maximum value and less than its allowed minimum value: where t Xi , t Xi are the actual value, the allowed maximum value, and the allowed minimum value of the signal transmission delay of the $i$-th channel, respectively.

[0117] (7). Constraint conditions for signal power: The signal power of the $i$-th user should meet the requirements of being greater than its allowed maximum value and less than its allowed minimum value: where $P$ Ui , P Ui are the actual value, the allowed maximum value, and the allowed minimum value of the signal power of the $i$-th user, respectively.

[0118] (8). Constraint conditions for node blocking: The blocking of the $i$-th node should meet the requirements of being greater than its allowed maximum value and less than its allowed minimum value: where $R$ i , R iThey are respectively the actual value, the maximum allowable value, and the minimum value of the blockage of the i-th node.

[0119] In addition, in the embodiments of the present invention, an optimization problem solving method based on the bat algorithm to optimize the support vector machine is also proposed. The specific steps are as follows:

[0120] 1). Collect the characteristic data index data samples of transformer oil aging and define the training set and the test set;

[0121] 2). Parameter setting. Set the number of bat populations N, the maximum number of iterations T during the optimization process, the dimension d of the foraging space, where the foraging space is 2, the maximum frequency f of the pulse max and the minimum frequency f of the pulse min , the maximum loudness A max , the initial pulse frequency f0, the reduction coefficient α of the pulse loudness, and the constant γ;

[0122] 3). Initialization of the population. For the position x of bat i i and the speed v i respectively take a random value. Among them, the position parameter x of bat i i =(C, σ);

[0123] 4). Calculate the fitness corresponding to each bat, and find the bat position x corresponding to the minimum fitness at this moment p . Train the LS-SVM classifier with the parameters corresponding to the bat position, and then optimize the parameters with the training set data to obtain the fitness value of each bat. By comparison, the position of the bat with the minimum fitness is the corresponding optimal parameter value. The fitness function is the average classification error rate during k-fold cross-validation: In the formula: n t is the number of correctly classified samples, and n is the total number of samples for verification;

[0124] 5). Update the position and speed of the bat according to the following formula; f i =f min +(f max -f min )β,

[0125] 6). Randomly generate a number η1 within the range of [0, 1]. If η1 > r i , at this time, apply a random perturbation to the current optimal solution according to the following formula to generate a new solution: x new =x old +ηAt;

[0126] 7). Generate a random number η2. If η2 < A i and at the same time f(x new) <f(x i ), receive the new solution generated in step 6), and update the pulse loudness A according to the following formula i and the frequency r i : r i t+1 = r i 0 (1 - e -γt ),

[0127] 8). Calculate the fitness. Calculate the fitness function values of each bat respectively, and then select the optimal bat position x p .

[0128] 9). Verify the end condition. Judge whether the maximum number of iterations has been reached. If not, go to step 4); if the number of iterations reaches T, the iteration process ends.

[0129] 10). Obtain the optimal parameters. Output the parameters (C, σ) corresponding to the optimal fitness, and the LS - SVM classifier model constructed by the optimal parameters.

[0130] Through the above technical solutions provided by the embodiments of the present invention, using statistical analysis methods, a calculation method for detecting errors in time data information such as the release of multi - market - entity transaction data, channel entry, and the year, month, day, hour, minute, and second of data arrival is proposed. Considering obtaining power data information such as power generation, transmission, transformation, distribution, power consumption, and the market through a sensing system at the sensing layer, realizing the sharing and interaction of power generation data in the power system through a big data system at the storage layer, realizing data transmission through a network system at the network layer, and realizing the fusion and application of independent data at the application layer, a calculation method for the information income value and information loss value of multi - market - entity transaction data in the network system is proposed. A calculation method for the trust degree of multi - market - entity transaction data in the network system is proposed. Taking the maximization of the average trust degree of multi - market - entity transaction data in the network system, the minimum information detection error of power data release, channel entry, and data arrival timestamps as the objective function, and the amount of information transmitted by multi - market - entity transaction data as the decision variable, an optimization model for information security protection of multi - market - entity transaction data is constructed. And an optimization method based on the bat algorithm to optimize the support vector machine is used to calculate the trust degree of multi - market - entity transaction data, and the data security protection of multi - market - entity transaction data in the power Internet of Things is regulated based on the trust degree.

[0131] That is, in the embodiments of the present invention, in view of the influence of information detection errors on data publication, channel entry, and data arrival timestamps in the network system, it reflects the influence of Internet of Things data information on the traffic security management of accessing users. A multi-market entity transaction data information security protection optimization model is constructed; an optimization method based on the bat algorithm to optimize the support vector machine is adopted to calculate the trust degree of multi-market entity transaction data, and the data security protection of multi-market entity transactions in the power Internet of Things is regulated based on the trust degree, providing theoretical guidance for ensuring the scheduling and operation of the network system and providing necessary technical support for the safe and stable operation of the network system.

[0132] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of this application.

[0134] According to an embodiment of the present invention, there is also provided a multi-market entity transaction data security protection device for implementing the above multi-market entity transaction data security protection method. Figure 3 It is a schematic diagram of the multi-market entity transaction data security protection device according to an embodiment of the present invention, as Figure 3 shown. The multi-market entity transaction data security protection device includes: a first acquisition unit 301, a first determination unit 303, a second acquisition unit 305, a second determination unit 307, a third determination unit 309, a fourth determination unit 311, and a protection unit 313. The multi-market entity transaction data security protection device will be described below.

[0135] The first acquisition unit 301 is used to acquire the publication time data of multi-market entity transaction data, the channel entry time data of multi-market entity transaction data, and the data arrival time data of multi-market entity transaction data.

[0136] The first determination unit 303 is configured to determine error data one of the release time data, error data two of the channel entry time data, and error data three of the data arrival time data.

[0137] The second acquisition unit 305 is configured to acquire the information benefit value of the multi-market entity transaction data.

[0138] The second determination unit 307 is configured to determine the information loss value of the multi-market entity transaction data according to error data one, error data two, and error data three.

[0139] The third determination unit 309 is configured to determine the trust level of the multi-market entity transaction data in the network system according to the information benefit value and the information loss value.

[0140] The fourth determination unit 311 is configured to determine the data security protection strategy of the multi-market entity transaction data in the power Internet of Things based on the trust level.

[0141] The protection unit 313 is configured to perform data security protection on the multi-market entity transaction data according to the data security protection strategy.

[0142] It should be noted here that the above first acquisition unit 301, first determination unit 303, second acquisition unit 305, second determination unit 307, third determination unit 309, fourth determination unit 311, and protection unit 313 correspond to steps S202 to step S214 in the above embodiment. The instances and application scenarios implemented by the six units and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiment.

[0143] As can be seen from the above, in the solution described in the above embodiments of the present invention, the first acquisition unit can be used to acquire the release time data of multi-market entity transaction data, the channel entry time data of multi-market entity transaction data, and the data arrival time data of multi-market entity transaction data; the first determination unit is used to determine the error data one of the release time data, the error data two of the channel entry time data, and the error data three of the data arrival time data; the second acquisition unit is used to acquire the information benefit value of multi-market entity transaction data; the second determination unit is used to determine the information loss value of multi-market entity transaction data according to the error data one, the error data two, and the error data three; the third determination unit is used to determine the trust degree of multi-market entity transaction data in the network system according to the information benefit value and the information loss value; the fourth determination unit is used to determine the data security protection strategy of multi-market entity transaction data in the power Internet of Things based on the trust degree; the protection unit is used to perform data security protection on multi-market entity transaction data according to the data security protection strategy, achieving the purpose of data security protection for the power Internet of Things through statistical analysis, based on the time data information detection errors of the release, channel entry, and data arrival of multi-market entity transaction data, as well as the information benefit value of multi-market entity transaction data and the information loss value of error detection data, and achieving the technical effect of improving the security of power Internet of Things data.

[0144] Therefore, through the technical solution provided by the above embodiments of the present invention, the technical problem that the protection measures for multi-market entity transaction data are relatively simple, resulting in relatively large data security risks, is solved.

[0145] Optionally, the first acquisition unit includes: a first acquisition subunit, configured to use the Internet of Things perception system to acquire the release time data, the channel entry time data, and the data arrival time data of multi-market entity transaction data from the data monitoring center, where the release time data, the channel entry time data, and the data arrival time data are all time data in the order of year, month, day, hour, minute, and second.

[0146] Optionally, the first determination unit includes: a first determination subunit, configured to respectively determine the error data one, the error data two, and the error data three of the release time data, the channel entry time data, and the data arrival time data by using a statistical analysis method.

[0147] Optionally, the first determination subunit includes: a first determination module, configured to respectively determine the information detection errors of the release time data, the channel entry time data, and the data arrival time data on different time scales; a second determination module, configured to determine the error data one, the error data two, and the error data three according to the information detection errors on different time scales.

[0148] Optionally, the second acquisition unit includes: a second determination subunit that determines the information gain value of multi-market entity transaction data through a first formula, where the first formula is: R represents the information gain value, is the first information gain value formed by providing data transmission at 9 different rates to the user, k Dvi is the first unit gain value brought by the data transmission of the i-th rate provided by the power Internet of Things, is the second information gain value formed by providing data storage sharing at 9 different scales to the user, k DSi is the second unit gain value brought by the data storage sharing of the i-th rate provided by the power Internet of Things, k Mi M H is the third information gain value formed by the power Internet of Things providing data detection for the hydro-generator set in the perception layer, k Mi M T is the fourth information gain value formed by the power Internet of Things providing data detection for the thermal power unit in the perception layer, k Mi M N is the fifth information gain value formed by the power Internet of Things providing data detection for the nuclear power unit in the perception layer, k Mi M G is the sixth information gain value formed by the power Internet of Things providing data detection for the gas turbine unit in the perception layer, k Mi is the third unit gain value brought by the power Internet of Things providing data detection for the unit in the perception layer.

[0149] Optionally, the second determination unit includes: a second determination subunit that determines the information loss value of multi-market entity transaction data through a second formula, error data one, error data two, and error data three, where the second formula is: L represents the information loss value, represents the influence coefficient of the error data one on the power data publication, is the unit power information loss value brought by the error data one, is the influence coefficient of the error data two on the power data publication, is the unit power information loss value brought by the error data two, is the influence coefficient of the error data three on the power data publication, is the unit power information loss value brought by the error data three, represents the error data one, represents the error data two, represents the error data three.

[0150] Optionally, the third determination unit includes: a third determination subunit, configured to determine a trust level through a third formula, an information gain value, and an information loss value, where the third formula is: B represents the trust level, R represents the information gain value, L represents the information loss value, and the trust level is the average value of the trust levels in the network system.

[0151] Optionally, the fourth determination unit includes: a fourth determination subunit, configured to determine a data security protection policy corresponding to the trust level through a security protection optimization model, where the security protection optimization model is represented by a fourth formula: maxB, B represents the trust level, represents the first error data, represents the second error data, represents the third error data.

[0152] According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored program, where when the program runs, it controls the device where the non-volatile storage medium is located to execute the security protection method for multi-market entity transaction data in any one of the above.

[0153] Optionally, in this embodiment, the above computer-readable storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the communication devices in the communication device group.

[0154] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: obtaining the release time data of the multi-market entity transaction data, the channel entry time data of the multi-market entity transaction data, and the data arrival time data of the multi-market entity transaction data; determining the first error data of the release time data, the second error data of the channel entry time data, and the third error data of the data arrival time data; obtaining the information gain value of the multi-market entity transaction data; determining the information loss value of the multi-market entity transaction data according to the first error data, the second error data, and the third error data; determining the trust level of the multi-market entity transaction data in the network system according to the information gain value and the information loss value; determining the data security protection policy of the multi-market entity transaction data in the power Internet of Things based on the trust level; and performing data security protection on the multi-market entity transaction data according to the data security protection policy.

[0155] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining multi-market entity transaction data release time data, channel entry time data, and data arrival time data from a data monitoring center by using an Internet of Things perception system, where the release time data, channel entry time data, and data arrival time data are all time data in the order of year, month, day, hour, minute, and second.

[0156] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: respectively determining error data one, error data two, and error data three for the release time data, channel entry time data, and data arrival time data by using a statistical analysis method.

[0157] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: respectively determining information detection errors of the release time data, channel entry time data, and data arrival time data on different time scales; and determining error data one, error data two, and error data three according to the information detection errors on different time scales.

[0158] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining an information benefit value of multi-market entity transaction data through a first formula, where the first formula is: R represents the information benefit value, is the first information benefit value formed by providing data transmission at 9 different rates to a user, k Dvi is the first unit benefit value brought by the power Internet of Things providing data transmission at the i-th rate, is the second information benefit value formed by providing data storage sharing at 9 different scales to a user, k DSi is the second unit benefit value brought by the power Internet of Things providing data storage sharing at the i-th rate, k Mi M H is the third information benefit value formed by the power Internet of Things providing data detection for a hydroelectric unit at the perception layer, k Mi M T is the fourth information benefit value formed by the power Internet of Things providing data detection for a thermal power unit at the perception layer, k Mi M N is the fifth information benefit value formed by the power Internet of Things providing data detection for a nuclear power unit at the perception layer, k Mi M G is the sixth information benefit value formed by the power Internet of Things providing data detection for a gas turbine unit at the perception layer, k MiThe third unit revenue value brought by the power Internet of Things for data detection of the unit at the perception layer.

[0159] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining the information loss value of multi-market entity transaction data through the second formula and error data one, error data two, and error data three, where the second formula is: L represents the information loss value, represents the influence coefficient of the error data one on the power data publication, is the unit power information loss value brought by the error data one, is the influence coefficient of the error data two on the power data publication, is the unit power information loss value brought by the error data two, is the influence coefficient of the error data three on the power data publication, is the unit power information loss value brought by the error data three, represents the error data one, represents the error data two, represents the error data three.

[0160] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining the trust degree through the third formula and the information revenue value and the information loss value, where the third formula is: B represents the trust degree, R represents the information revenue value, L represents the information loss value, and the trust degree is the average value of the trust degrees in the network system.

[0161] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining the data security protection strategy corresponding to the trust degree through the security protection optimization model, where the security protection optimization model is represented by the fourth formula: maxB, B represents the trust degree, represents the error data one, represents the error data two, represents the error data three.

[0162] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including: a memory and a processor, the memory stores a computer program; the processor is configured to execute the computer program stored in the memory, and when the computer program runs, the processor executes the security protection method for multi-market entity transaction data in any one of the above.

[0163] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, which when executed by a processor, implements the security protection method for multi-market entity transaction data in any one of the above.

[0164] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0165] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections with each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0167] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0169] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0170] The foregoing are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for protecting the transaction data of multiple market entities, characterized in that: include: Using the Internet of Things sensing system to obtain from the data monitoring center the release time data of the transaction data of multiple market entities, the channel entry time data of the transaction data of the multiple market entities, and the data arrival time data of the transaction data of the multiple market entities, wherein the release time data, the channel entry time data, and the data arrival time data are all time data in terms of year, month, day, hour, minute, and second; Determine error data one of the release time data, error data two of the channel entry time data, and error data three of the data arrival time data; The information benefit value of the multi-market subject transaction data is determined by a first formula, wherein the first formula is: R represents the information benefit value, is the first information benefit value formed by providing users with data transmission at 9 different rates, k Dvi is the first unit benefit value brought by the data transmission of the ith rate provided by the power Internet of Things. It is the second information benefit value formed by providing users with data storage sharing of 9 different scales, k DSi is the second unit benefit value brought by the data storage sharing of the ith rate provided by the power Internet of Things, k Mi M H is the third information benefit value formed by the power Internet of Things providing data detection for the hydropower unit at the perception layer, k Mi M T is the fourth information benefit value formed by the power Internet of Things providing data detection for the thermal power unit at the perception layer, k Mi M N is the fifth information benefit value formed by the power Internet of Things providing data detection for the nuclear power unit at the perception layer, k Mi M G is the sixth information benefit value formed by the electric power Internet of Things providing data detection for the gas-fired generator set at the perception layer, k Mi The third unit income value brought by the power Internet of Things providing data detection for the unit at the perception layer; The information loss value of the multi-market subject transaction data is determined by a second formula and the error data 1, the error data 2 and the error data 3, wherein the second formula is: L represents the information loss value, Indicates the influence coefficient of the error data on the power data release, is the unit power information loss value caused by the error data 1, is the influence coefficient of the error data 2 on the power data release, is the unit power information loss value caused by the error data 2, is the influence coefficient of the error data 3 on the power data release, is the unit power information loss value caused by the error data 3, Represents the error data one, Represents the error data 2, Represents the error data three; The trust degree is determined by a third formula and the information gain value and the information loss value, wherein the third formula is: B represents the trust degree, R represents the information gain value, L represents the information loss value, and the trust degree is the average value of the trust degrees in the network system; The data security protection strategy corresponding to the trust level is determined by a security protection optimization model, wherein the security protection optimization model is represented by a fourth formula, which is: maxB, B represents the degree of trust, represents the error data one, Represents the error data 2, Represents the error data three; Data security protection is performed on the transaction data of the multiple market entities in accordance with the data security protection strategy.

2. The method for protecting the transaction data of multiple market entities according to claim 1, characterized in that: Determining error data 1 of the release time data, error data 2 of the channel entry time data, and error data 3 of the data arrival time data, including: The error data one, the error data two and the error data three are determined respectively by using a statistical analysis method for the release time data, the channel entry time data and the data arrival time data.

3. The method for protecting the transaction data of multiple market entities according to claim 2, characterized in that: The error data 1, the error data 2 and the error data 3 are determined respectively by using a statistical analysis method for the release time data, the channel entry time data and the data arrival time data, including: Respectively determining information detection errors of the release time data, the channel entry time data, and the data arrival time data on different time scales; The error data one, the error data two and the error data three are determined according to the information detection errors on the different time scales.

4. A security protection device for transaction data of multiple market entities, characterized in that: include: A first acquisition unit is used to acquire, from a data monitoring center, release time data of multi-market subject transaction data, channel entry time data of the multi-market subject transaction data, and data arrival time data of the multi-market subject transaction data by using an Internet of Things perception system, wherein the release time data, the channel entry time data, and the data arrival time data are all time data in terms of year, month, day, hour, minute, and second; A first determining unit, configured to determine error data 1 of the release time data, error data 2 of the channel entry time data, and error data 3 of the data arrival time data; The second acquisition unit is used to determine the information benefit value of the multi-market subject transaction data by using a first formula, wherein the first formula is: R represents the information benefit value, is the first information benefit value formed by providing users with data transmission at 9 different rates, k Dvi is the first unit benefit value brought by the data transmission of the ith rate provided by the power Internet of Things. It is the second information benefit value formed by providing users with data storage sharing of 9 different scales, k DSi is the second unit benefit value brought by the data storage sharing of the ith rate provided by the power Internet of Things, k Mi M H is the third information benefit value formed by the power Internet of Things providing data detection for the hydropower unit at the perception layer, k Mi M T is the fourth information benefit value formed by the power Internet of Things providing data detection for the thermal power unit at the perception layer, k Mi M N is the fifth information benefit value formed by the power Internet of Things providing data detection for the nuclear power unit at the perception layer, k Mi M G is the sixth information benefit value formed by the electric power Internet of Things providing data detection for the gas-fired generator set at the perception layer, k Mi The third unit income value brought by the power Internet of Things providing data detection for the unit at the perception layer; The second determining unit is used to determine the information loss value of the multi-market subject transaction data by using a second formula and the error data 1, the error data 2, and the error data 3, wherein the second formula is: L represents the information loss value, Indicates the influence coefficient of the error data on the power data release, is the unit power information loss value caused by the error data 1, is the influence coefficient of the error data 2 on the power data release, is the unit power information loss value caused by the error data 2, is the influence coefficient of the error data 3 on the power data release, is the unit power information loss value caused by the error data 3, represents the error data one, Represents the error data 2, Represents the error data three; A third determining unit is used to determine the trust level by using a third formula and the information gain value and the information loss value, wherein the third formula is: B represents the trust degree, R represents the information gain value, L represents the information loss value, and the trust degree is the average value of the trust degrees in the network system; The fourth determining unit is used to determine the data security protection strategy corresponding to the trust level through a security protection optimization model, wherein the security protection optimization model is represented by a fourth formula, and the fourth formula is: maxB, B represents the degree of trust, Represents the error data one, Represents the error data 2, Represents the error data three; A protection unit is used to perform data security protection on the transaction data of the multiple market entities according to the data security protection strategy.

5. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for protecting the security of transaction data of multiple market entities as described in any one of claims 1 to 3 is implemented.

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