Internet of Things trust analysis method and system for frequency regulation transaction management of thermal power units

Abstract

The present application discloses an Internet of Things trust analysis method and system for thermal power unit frequency regulation transaction management, which considers the data information formed by data transmission rate and data storage sharing, and the data errors and missing formed by data collection, and uses the information sufficiency and missing amount to calculate the trust of the power Internet of Things, and evaluates the impact of trust on the frequency regulation transaction of thermal power units. This Internet of Things trust calculation method applied to the frequency regulation transaction management of thermal power units reflects the impact of the perception system, data transmission rate, and data storage sharing at the same time, provides theoretical guidance for the trust evaluation of the power Internet of Things applied to the frequency regulation transaction management of thermal power units, and provides necessary technical support for frequency regulation transactions based on the Internet of Things.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to an Internet of Things trust analysis method and system for frequency regulation transaction management of thermal power units. Background Art

[0002] The three major application scenarios of 5G are a further deepening of the three major application scenarios of data transmission solutions in the power Internet of Things. Relevant experts and scholars at home and abroad have also divided the power Internet of Things business according to the three major application scenarios of 5G and carried out relevant research work.

[0003] The eMBB scenario mainly meets the needs of some high-bandwidth services, which is an enhancement of data collection application scenarios and business information transmission scenarios. At present, the application of the power Internet of Things in this regard is mainly the big video of the power grid, including substation robot inspection, transmission line drone online monitoring, distribution room video monitoring, mobile on-site construction operation control and emergency on-site self-organizing network comprehensive application. Many researchers have tried to apply 5G technology to certain scenarios and have achieved certain results.

[0004] In terms of trust calculation, there are many research results, such as: calculating the reputation value of nodes in the global scope through multiple iterations of the trust chain, introducing probability theory to solve the trust problem, and giving a trust model algorithm performance based on Bayesian network. The algorithm's time decay and penalty factor are considered when calculating trust, which improves the accuracy of the algorithm and its ability to resist strategic attacks. Trust is dynamically established and developed, and its development trend has certain rules. At the same time, there is a certain correlation between two adjacent transactions. The existing algorithms do not well reflect the time correlation, context correlation and development trend of trust. In order to better describe these characteristics of trust, on the basis of the existing Markov chain model, the steady-state probability is introduced to describe the development trend of trust. After each transaction is completed, the transfer matrix is ​​dynamically updated to more accurately describe the context correlation of trust. At the same time, the time decay factor and penalty incentive factor are added to improve the algorithm's ability to resist attacks. However, there is still little research on the existing technology of the Internet of Things trust calculation method for the frequency regulation transaction management of thermal power units. Therefore, there is an urgent need for an Internet of Things trust calculation method applied to the frequency regulation transaction management of thermal power units to calculate the trust of the power Internet of Things and evaluate the impact of trust on the frequency regulation transaction of thermal power units. Summary of the invention

[0005] The present application provides an Internet of Things trust analysis method for frequency regulation transaction management of thermal power units, which is used to solve the problem of calculating the trust of the power Internet of Things and evaluate the impact of trust on frequency regulation transactions of thermal power units.

[0006] In view of this, the first aspect of the present application provides an Internet of Things trust analysis method for frequency regulation transaction management of thermal power units, the method comprising:

[0007] According to the frequency regulation price data, frequency regulation power demand data, frequency regulation power data of thermal power units involved in frequency regulation and quotation data of the power system, the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price, frequency regulation power demand, frequency regulation power and quotation of the power system in the determined period is calculated by using the statistical analysis method;

[0008] According to the data transmission rate data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine the data transmission rate and a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale;

[0009] The frequency regulation power collection error of the thermal power units participating in frequency regulation is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power, and the frequency regulation quotation collection error of the thermal power units participating in frequency regulation is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation price and quotation collection error;

[0010] According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate and data storage sharing scale, the information benefit value of power Internet of Things in frequency regulation trading is calculated;

[0011] Determine the probability of information loss of the power Internet of Things in the frequency regulation transaction, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error;

[0012] According to the information loss value and the information gain value, the average value of the trust of all users in the power Internet of Things for frequency regulation transactions of thermal power units is calculated.

[0013] Optionally, the method further includes calculating, according to the information loss value and the information gain value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for frequency regulation transactions of thermal power units, and then further including:

[0014] The trust level of the power Internet of Things is evaluated based on the average value of the trust of all users of the power Internet of Things.

[0015] Optionally, according to the frequency regulation power data of the thermal power units participating in the frequency regulation of the power system, a three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the power system in a certain period is calculated by using a statistical analysis method, specifically including:

[0016] Using the IoT sensing system, the power market monitoring data center is used to obtain data information on the power demand for frequency modulation in the power system;

[0017] According to the data information of frequency regulation power demand, the three-dimensional trapezoidal fuzzy set of frequency regulation power demand acquisition error of power system in period t is calculated by using statistical analysis method. Three-dimensional trapezoidal fuzzy set for power demand acquisition error of power system frequency regulation where t=1,2,...,N RP ;

[0018]

[0019]

[0020]

[0021] In the formula, and are the three-dimensional trapezoidal fuzzy set of the power demand for frequency regulation of the power system during period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, respectively. j = 1, 2, 3, 4, and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand acquisition error of the power system in time period t.

[0022] Optionally, according to the data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale, specifically including:

[0023] At the platform level of the power Internet of Things, the data information of the data storage sharing scale is obtained through the Internet of Things monitoring data center, and the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set S of nine fuzzy uncertainties of extremely low, very low, low, relatively low, medium, relatively high, high, very high and extremely high data storage sharing scale. Di , i=1,2,...,9:

[0024] S Di =(S DiL ,S DiM ,S DiU )=[(S DiL1 ,S DiL2 ,S DiL3 ,S DiL4 ;k DSiL ),

[0025] (S DiM1 ,S DiM2 ,SDiM3 ,S DiM4 ;k DSiM ),

[0026] (S DiU1 ,S DiU2 ,S DiU3 ,S DiU4 ;k DSiU )];

[0027] In the formula, S Di is the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiL , S DiM , S DiU and k DSiL , k DSiM , k DSiU They are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiLj , S DiMj , S DiUj , j = 1, 2, 3, 4, which are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data storage sharing scale respectively.

[0028] Optionally, the three-dimensional trapezoidal fuzzy set calculation of the frequency regulation power acquisition error of the thermal power units participating in the frequency regulation based on the frequency regulation power demand and the frequency regulation power specifically includes:

[0029] Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power into the frequency regulation power acquisition error calculation formula to calculate the frequency regulation power acquisition error of the thermal power units involved in frequency regulation.

[0030] Among them, the calculation formula for frequency modulation power acquisition error is:

[0031]

[0032] In the formula, is the three-dimensional trapezoidal fuzzy set of frequency modulation power demand, is the three-dimensional trapezoidal fuzzy set of FM power quantity, Represents the union of fuzzy sets.

[0033] Optionally, the information benefit value of the power Internet of Things in frequency modulation trading is calculated based on the data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data storage sharing scale, specifically including:

[0034] Substitute the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale into the information benefit value calculation formula, and calculate the information benefit value of the power Internet of Things in frequency regulation trading;

[0035] The information benefit value calculation formula is:

[0036]

[0037] In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M T is the information benefit value formed by the Internet of Things providing data collection for thermal power units at the perception layer; E[] is the expected value of []. represents the union of 9 fuzzy sets;

[0038] M T It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows:

[0039] M T =(M TL ,M TM ,M TU )=[(M TL1 ,M TL2 ,M TL3 ,M TL4 ;k HL ),

[0040] (M TM1 ,M TM2 ,M TM3 ,M TM4 ;k TM ),

[0041] (M TU1 ,M TU2 ,M TU3 ,M TU4 ;k TU )];

[0042] Where M T The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer, M TL 、M TM 、M TU and k TL , k TM , k TU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the thermal power unit in the perception layer. TLj 、M TMj 、M TUj (j=1,2,3,4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer.

[0043] Optionally, the determining of the information loss probability of the power Internet of Things in the frequency regulation transaction, and calculating the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error, specifically includes:

[0044] Substitute the probability of information loss, the frequency regulation power of thermal power units participating in frequency regulation, and the frequency regulation quotation collection error into the information loss value calculation formula, and calculate the information loss value of frequency regulation transactions caused by the frequency regulation power of thermal power units participating in frequency regulation and the quotation collection error formed in the power Internet of Things;

[0045] The information loss value calculation formula is:

[0046]

[0047] Where L RP The frequency regulation power and quotation collection errors of the thermal power units involved in frequency regulation in the power Internet of Things will cause the information loss value of frequency regulation transactions; RP The probability of information loss of power Internet of Things in frequency regulation trading; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the thermal power units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the thermal power units involved in frequency regulation in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the thermal power units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the error in collecting frequency quotation of thermal power units participating in frequency regulation in the power Internet of Things.

[0048] Optionally, the calculating, according to the information loss value and the information gain value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for frequency regulation transactions of thermal power units, specifically includes:

[0049] Substituting the information loss value and the information gain value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for frequency regulation transactions of thermal power units is calculated;

[0050] The average value calculation formula is:

[0051]

[0052] In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RP is the information loss value.

[0053] A second aspect of the present application provides an Internet of Things trust analysis system for frequency regulation transaction management of thermal power units, the system comprising:

[0054] The first calculation unit is used to calculate the frequency regulation price, frequency regulation power demand, frequency regulation power and quotation collection error of the power system in a certain period of time by using a statistical analysis method according to the frequency regulation price data, frequency regulation power demand data, frequency regulation power data of the thermal power units participating in the frequency regulation and quotation data of the power system;

[0055] The second calculation unit is used to calculate and determine the data transmission rate and the three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of the data storage sharing scale by using a statistical analysis method according to the data transmission rate data and the data storage sharing scale data of the Internet of Things monitoring data center;

[0056] The third calculation unit is used to calculate the frequency regulation power acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the frequency regulation power, and calculate the frequency regulation quotation acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and quotation;

[0057] A fourth calculation unit is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale;

[0058] A fifth calculation unit is used to determine the information loss probability of the power Internet of Things in the frequency regulation transaction, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error;

[0059] The sixth calculation unit is used to calculate the average value of the trust of all users in the power Internet of Things for frequency regulation transactions of thermal power units based on the information loss value and the information gain value.

[0060] Optionally, it also includes: an evaluation unit, used to evaluate the trust level of the power Internet of Things based on the average value of the trust of the power Internet of Things in all users.

[0061] It can be seen from the above technical solutions that this application has the following advantages:

[0062] The present application provides an Internet of Things trust analysis method for the management of thermal power unit frequency regulation transactions, which considers the data information formed by data transmission rate and data storage sharing, and the data errors and omissions formed by data collection, and uses the information sufficiency and omission to calculate the trust of the power Internet of Things, and evaluates the impact of trust on the frequency regulation transactions of thermal power units. This Internet of Things trust calculation method applied to the management of thermal power unit frequency regulation transactions simultaneously reflects the impact of the perception system, data transmission rate, and data storage sharing, provides theoretical guidance for the trust evaluation of the power Internet of Things applied to the management of thermal power unit frequency regulation transactions, and provides necessary technical support for frequency regulation transactions based on the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of an Internet of Things trust analysis method for frequency regulation transaction management of thermal power units provided in an embodiment of the present application;

[0064] Figure 2 This is a structural diagram of an Internet of Things trust analysis system for frequency regulation transaction management of thermal power units provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] See also Figure 1, an Internet of Things trust analysis method for frequency regulation transaction management of thermal power units provided in an embodiment of the present application includes:

[0067] Step 101, according to the frequency regulation price data, frequency regulation power demand data, frequency regulation power data of thermal power units participating in frequency regulation and quotation data of the power system, a statistical analysis method is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the frequency regulation price, frequency regulation power demand, frequency regulation power and quotation acquisition error of the power system in a certain period;

[0068] It should be noted that step 101 is a process and method for error assessment of frequency regulation transaction data collection of thermal power units in the Internet of Things. At the perception layer of the power Internet of Things, relevant data information on the frequency regulation price of the power system with fuzzy uncertainty, the power demand of the power system frequency regulation, the frequency regulation power of the thermal power units participating in the frequency regulation and their quotations, the frequency regulation power of the thermal power units participating in the frequency regulation and their quotations, the frequency regulation power of the nuclear power units participating in the frequency regulation and their quotations, the frequency regulation power of the gas power units participating in the frequency regulation and their quotations, the frequency regulation power of the energy storage units participating in the frequency regulation and their quotations, and the frequency regulation power of the west-to-east power transmission units participating in the frequency regulation and their quotations are obtained.

[0069] Specifically include:

[0070] 1011. Use the IoT sensing system to obtain relevant data information on the power system frequency regulation price from the power market monitoring data center. According to the data on the power system frequency regulation price obtained from the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N e , N e The three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system (time period number)

[0071]

[0072]

[0073]

[0074] In the formula, and are the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. (j=1,2,3,4) and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error of the power system in time period t.

[0075] 1012. Use the IoT sensing system to obtain relevant data information on the power demand for power system frequency regulation from the power market monitoring data center. According to the data on the power demand for power system frequency regulation obtained from the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N RP 3D trapezoidal fuzzy set for power system frequency regulation power demand acquisition error

[0076]

[0077]

[0078]

[0079] In the formula, and are the three-dimensional trapezoidal fuzzy set of the power demand for frequency regulation of the power system during period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, respectively. (j=1,2,3,4) and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand acquisition error of the power system in time period t.

[0080] 1013. Use the IoT sensing system to obtain relevant data information about the frequency modulation power of thermal power units participating in frequency modulation from the power market monitoring data center. According to the frequency modulation power data of thermal power units participating in frequency modulation in the power market, use statistical analysis methods to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of frequency regulation power quantity acquisition error of thermal power units involved in frequency regulation

[0081]

[0082]

[0083]

[0084] In the formula, and are the frequency regulation power acquisition error three-dimensional trapezoidal fuzzy set or the fuzzy number and membership coefficient corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set for the thermal power units participating in frequency regulation in time period t, (j=1,2,3,4) and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the thermal power units participating in frequency regulation in time period t.

[0085] 1014. Use the IoT sensing system to obtain relevant data information on the quotations of thermal power units participating in frequency regulation from the power market monitoring data center. According to the quotations of thermal power units participating in frequency regulation in the power market, a statistical analysis method is used to calculate and determine the time period t (t = 1, 2, ..., N RP )Three-dimensional trapezoidal fuzzy set of the bid collection error of thermal power units participating in frequency regulation

[0086]

[0087]

[0088]

[0089] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the thermal power units participating in the frequency regulation in time period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoid, respectively. (j=1,2,3,4) and They are the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set of the quotation collection error of the thermal power units participating in frequency regulation during time period t.

[0090] Step 102: According to the data transmission rate data and the data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale;

[0091] It should be noted that step 102 is a process and method for monitoring the data transmission rate of the power Internet of Things for power market frequency regulation transactions; and a process and method for evaluating the data storage sharing scale of the power Internet of Things for power market frequency regulation transactions.

[0092] 1021. The process and method of monitoring the data transmission rate of the power Internet of Things for power market frequency regulation transactions, specifically including:

[0093] In the network layer of the power Internet of Things, the relevant data information of the data transmission rate is obtained from the Internet of Things monitoring data center, and the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set v of nine fuzzy uncertainties of data transmission rate: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di (i=1,2,...,9):

[0094] v Di =(v DiL ,v DiM,v DiU )=[(v DiL1 ,v DiL2 ,v DiL3 ,v DiL4 ;k DviL ),

[0095] (v DiM1 ,v DiM2 ,v DiM3 ,v DiM4 ;k DviM ),

[0096] (v DiU1 ,v DiU2 ,v DiU3 ,v DiU4 ;k DviU )];

[0097] In the formula, v Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, v DiL 、v DiM 、v DiU and k DviL , k DviM , k DviU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate, respectively. DiLj 、v DiMj 、v DiUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of data transmission rate.

[0098] 1022. The process and method for evaluating the scale of power Internet of Things data storage and sharing for power market frequency regulation transactions, specifically including:

[0099] At the platform layer of the power Internet of Things, relevant data information on the scale of data storage sharing is obtained from the Internet of Things monitoring data center, and a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set S of nine fuzzy uncertainties: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di (i=1,2,...,9):

[0100] S Di =(S DiL ,S DiM ,S DiU )=[(S DiL1 ,S DiL2 ,S DiL3 ,S DiL4 ;k DSiL ),

[0101] (SDiM1 ,S DiM2 ,S DiM3 ,S DiM4 ;k DSiM ),

[0102] (S DiU1 ,S DiU2 ,S DiU3 ,S DiU4 ;k DSiU )];

[0103] In the formula, S Di is the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiL , S DiM , S DiU and k DSiL , k DSiM , k DSiU They are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiLj , S DiMj , S DiUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data storage sharing scale respectively.

[0104] Step 103, calculating the frequency regulation power acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the frequency regulation power, and calculating the frequency regulation quotation acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and quotation;

[0105] It should be noted that step 103 is the process and method for calculating the error in frequency regulation power collection of thermal power units involved in frequency regulation formed in the power Internet of Things; and the process and method for calculating the error in frequency regulation quotation collection of thermal power units involved in frequency regulation formed in the power Internet of Things.

[0106] 1031. The process and method for calculating the frequency regulation power quantity acquisition error of the frequency regulation thermal power unit formed in the power Internet of Things, specifically including:

[0107] The frequency regulation power collection error of the thermal power units involved in frequency regulation formed in the power Internet of Things is jointly determined by the frequency regulation power demand of the power system and the frequency regulation power collection error of the thermal power units. is calculated according to the following formula:

[0108]

[0109] In the formula, Represents the union of fuzzy sets.

[0110] 1032. The process and method for calculating the frequency regulation quotation collection error of thermal power units involved in frequency regulation formed in the power Internet of Things, specifically including:

[0111] The frequency regulation quotation collection error of thermal power units participating in frequency regulation formed in the power Internet of Things is jointly determined by the power system frequency regulation power demand and the frequency regulation quotation collection error of thermal power units. is calculated according to the following formula:

[0112]

[0113] Step 104, calculating the information benefit value of the power Internet of Things in frequency modulation trading according to the data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale;

[0114] It should be noted that step 104 is a process and method for calculating the information benefit value of the power Internet of Things in frequency modulation trading, which specifically includes:

[0115] At the perception layer, the perception system is used to obtain data information such as electricity market prices, power demand for power system frequency regulation and its quotation, and frequency regulation power of various generator sets and their quotation. At the platform layer, with the help of the big data system, the sharing and interaction of power system frequency regulation data can be realized. At the network layer, with the help of the network system, data transmission can be realized, so that frequency regulation users can use the Internet of Things to obtain sufficient information and gain benefits. The information benefit value R of the power Internet of Things in frequency regulation transactions RP Calculate according to the following formula:

[0116]

[0117] In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Dvi is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M Tis the information benefit value formed by the Internet of Things providing data collection for thermal power units at the perception layer; E[] is the expected value of []. Represents the union of 9 fuzzy sets.

[0118] M T It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows:

[0119] M T =(M TL ,M TM ,M TU )=[(M TL1 ,M TL2 ,M TL3 ,M TL4 ;k HL ),

[0120] (M TM1 ,M TM2 ,M TM3 ,M TM4 ;k TM ),

[0121] (M TU1 ,M TU2 ,M TU3 ,M TU4 ;k TU )];

[0122] Where M T The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer, M TL 、M TM 、M TU and k TL , k TM , k TU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the thermal power unit in the perception layer. TLj 、M TMj 、M TUj (j=1,2,3,4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer.

[0123] Step 105, determining the information loss probability of the power Internet of Things in the frequency regulation transaction, and calculating the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error;

[0124] It should be noted that step 105 is a process and method for calculating the information loss value of the power Internet of Things in frequency modulation trading. Specifically, it includes:

[0125] The frequency regulation power and quotation collection errors of the thermal power units participating in frequency regulation formed in the power Internet of Things will cause information loss in frequency regulation transactions, and its value is calculated according to the following formula:

[0126]

[0127] Where, L RP The frequency regulation power and quotation collection errors of the thermal power units participating in the frequency regulation in the power Internet of Things will cause the information loss value of the frequency regulation transaction; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the thermal power units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the thermal power units involved in frequency regulation in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the thermal power units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the error in collecting frequency quotation of thermal power units participating in frequency regulation in the power Internet of Things.

[0128] Step 106: Calculate the average value of the trust of all users in the power Internet of Things for frequency regulation transactions of thermal power units based on the information loss value and the information gain value.

[0129] It should be noted that step 106 is a process and method for calculating the trust of the power Internet of Things, which specifically includes:

[0130] In the power Internet of Things for thermal power unit frequency regulation transactions, users at the perception layer, network layer, and platform layer provide information and benefit users. From this, the average trust of the power Internet of Things for all users can be calculated, and the calculation formula is:

[0131]

[0132] In one embodiment, the IoT trust analysis for frequency regulation trading management of thermal power units of the present application further includes: evaluating the trust level of the power IoT based on the average value of the trust of all users of the power IoT.

[0133] It should be noted that the trust level of all users can be evaluated based on the calculated average trust of the power Internet of Things:

[0134] When B I =1, the power Internet of Things is fully adapted to the application of frequency regulation trading of thermal power units.

[0135] When 0.8≤BI When <1, the power Internet of Things is well adapted to the application of frequency regulation trading of thermal power units.

[0136] When 0.5≤B I When <0.8, the power Internet of Things is better used in the frequency regulation trading of thermal power units.

[0137] When 0.3≤B I When <0.5, the power Internet of Things is suitable for the application of frequency regulation trading of thermal power units.

[0138] When 0≤B I When <0.3, the power Internet of Things cannot adapt to the application of frequency regulation trading of thermal power units.

[0139] The Internet of Things trust analysis method for the frequency regulation transaction management of thermal power units provided in the embodiment of the present application takes into account the data information formed by data transmission rate and data storage sharing, and the data errors and omissions formed by data collection, and uses the amount of information sufficiency and the amount of omission to calculate the trust of the power Internet of Things, and evaluates the impact of the trust on the frequency regulation transaction of thermal power units.

[0140] The above is an Internet of Things trust analysis method for frequency regulation transaction management of thermal power units provided in an embodiment of the present application, and the following is an Internet of Things trust analysis system for frequency regulation transaction management of thermal power units provided in an embodiment of the present application.

[0141] See also Figure 2 , an Internet of Things trust analysis system for frequency regulation transaction management of thermal power units provided in an embodiment of the present application includes:

[0142] The first calculation unit 201 is used to calculate the frequency regulation price, frequency regulation power demand, frequency regulation power, and three-dimensional trapezoidal fuzzy sets of the acquisition error of the power system in a certain period of time by using a statistical analysis method according to the frequency regulation price data, frequency regulation power demand data, frequency regulation power data of the thermal power units participating in the frequency regulation, and quotation data of the power system;

[0143] The second calculation unit 202 is used to calculate and determine the data transmission rate and the three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of the data storage sharing scale by using a statistical analysis method according to the data transmission rate data and the data storage sharing scale data of the Internet of Things monitoring data center;

[0144] The third calculation unit 203 is used to calculate the frequency regulation power acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the frequency regulation power, and calculate the frequency regulation quotation acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and quotation;

[0145] The fourth calculation unit 204 is used to calculate the information benefit value of the power Internet of Things in the frequency modulation transaction according to the data transmission rate and the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data storage sharing scale;

[0146] The fifth calculation unit 205 is used to determine the information loss probability of the power Internet of Things in the frequency regulation transaction, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error;

[0147] The sixth calculation unit 206 is used to calculate the average value of the trust of the power Internet of Things for all users in the power Internet of Things for frequency regulation transactions of thermal power units according to the information loss value and the information gain value.

[0148] The terms "first", "second", "third", "fourth", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0149] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0150] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0153] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0154] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An Internet of Things trust analysis method for frequency regulation transaction management of thermal power units, characterized in that: include: According to the frequency regulation price data, frequency regulation power demand data, frequency regulation power data of thermal power units involved in frequency regulation and quotation data of the power system, the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price, frequency regulation power demand, frequency regulation power and quotation of the power system in the determined period is calculated by using the statistical analysis method; According to the data transmission rate data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine the data transmission rate and a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale; The frequency regulation power collection error of the thermal power units participating in frequency regulation is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power, and the frequency regulation quotation collection error of the thermal power units participating in frequency regulation is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation price and quotation collection error; According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data transmission rate and data storage sharing scale, the information benefit value of power Internet of Things in frequency regulation trading is calculated; Determine the probability of information loss of the power Internet of Things in the frequency regulation transaction, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error; According to the information loss value and the information gain value, calculating the average trust of the power Internet of Things to all users in the power Internet of Things for frequency regulation transactions of thermal power units; in: The three-dimensional trapezoidal fuzzy set calculation of the frequency regulation power acquisition error of the thermal power units participating in the frequency regulation based on the frequency regulation power demand and the frequency regulation power specifically includes: Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power into the frequency regulation power acquisition error calculation formula to calculate the frequency regulation power acquisition error of the thermal power units involved in frequency regulation. Among them, the calculation formula for frequency modulation power acquisition error is: In the formula, is the three-dimensional trapezoidal fuzzy set of frequency modulation power demand, is the three-dimensional trapezoidal fuzzy set of FM power quantity, represents the union of fuzzy sets; The frequency regulation quotation collection error of the thermal power units participating in the frequency regulation is calculated based on the three-dimensional trapezoidal fuzzy set of the frequency regulation price and quotation collection error, specifically including: Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation price and quotation collection error into the frequency regulation quotation collection error calculation formula of thermal power units participating in frequency regulation, and calculate the frequency regulation quotation collection error of thermal power units participating in frequency regulation. Among them, the frequency regulation quotation collection error calculation formula of the thermal power units participating in frequency regulation is: In the formula, To determine the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t, The three-dimensional trapezoidal fuzzy set of the quotation collection error of the thermal power units participating in frequency regulation; The information benefit value of the power Internet of Things in frequency modulation trading is calculated based on the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale, specifically including: Substitute the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale into the information benefit value calculation formula, and calculate the information benefit value of the power Internet of Things in frequency regulation trading; The information benefit value calculation formula is: In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Di is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M T is the information benefit value formed by the Internet of Things providing data collection for thermal power units at the perception layer; E[] is the expected value of []. represents the union of 9 fuzzy sets; M T It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows: M T =(M TL ,M TM ,M TU )=[(M TL1 ,M TL2 ,M TL3 ,M TL4 ;k HL ), (M TM1 ,M TM2 ,M TM3 ,M TM4 ;k TM ), (M TU1 ,M TU2 ,M TU3 ,M TU4 ;k TU )]; Where M T The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer, M TL 、M TM 、M TU and k HL , k TM , k TU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the thermal power unit in the perception layer. TLj 、M TMj 、M TUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer; The information loss probability of the power Internet of Things in the frequency regulation transaction is determined, and according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error, the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things is calculated, specifically including: Substitute the probability of information loss, the frequency regulation power of thermal power units participating in frequency regulation, and the frequency regulation quotation collection error into the information loss value calculation formula, and calculate the information loss value of frequency regulation transactions caused by the frequency regulation power of thermal power units participating in frequency regulation and the quotation collection error formed in the power Internet of Things; The information loss value calculation formula is: Where L RP The frequency regulation power and quotation collection errors of the thermal power units involved in frequency regulation in the power Internet of Things will cause the information loss value of frequency regulation transactions; RP The probability of information loss of power Internet of Things in frequency regulation trading; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the thermal power units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the thermal power units involved in frequency regulation in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the thermal power units involved in frequency regulation formed in the power Internet of Things. is the unit loss value caused by the frequency regulation quotation collection error of thermal power units participating in frequency regulation in the power Internet of Things, N e is the number of time periods, To collect the frequency regulation power quantity error of the thermal power units involved in frequency regulation, To collect frequency regulation quotation errors for thermal power units participating in frequency regulation; The calculation of the average trust of the power Internet of Things for all users in the power Internet of Things for thermal power unit frequency regulation transactions based on the information loss value and the information gain value specifically includes: Substituting the information loss value and the information gain value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for frequency regulation transactions of thermal power units is calculated; The average value calculation formula is: In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RP is the information loss value.

2. The Internet of Things trust analysis method for frequency regulation transaction management of thermal power units according to claim 1 is characterized in that: The method further comprises calculating the average trust of all users of the power Internet of Things for frequency regulation transactions of thermal power units according to the information loss value and the information gain value, and then further comprising: The trust level of the power Internet of Things is evaluated based on the average value of the trust of all users of the power Internet of Things.

3. The Internet of Things trust analysis method for frequency regulation transaction management of thermal power units according to claim 1 is characterized in that: According to the frequency regulation power data of thermal power units participating in frequency regulation of the power system, the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the power system in the determined period is calculated by statistical analysis method, including: Using the IoT sensing system, the power market monitoring data center is used to obtain data information on the power demand for power system frequency modulation; According to the data information of frequency regulation power demand, the three-dimensional trapezoidal fuzzy set of frequency regulation power demand acquisition error of power system in period t is calculated by using statistical analysis method. Three-dimensional trapezoidal fuzzy set for power demand acquisition error of power system frequency regulation where t=1,2,...,N RP ; In the formula, and are the three-dimensional trapezoidal fuzzy set of the power demand for frequency regulation of the power system during period t or the fuzzy numbers and membership coefficients corresponding to the lower bound, middle bound and upper bound of the three-dimensional trapezoidal fuzzy set, respectively. and are the fuzzy numbers and membership coefficients corresponding to the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the power demand for frequency regulation of the power system during period t, respectively. The * in the Subscript in RPL , RPM , RPU , The * in the The subscript e in RPL 、e RPM 、e RPU .

4. The Internet of Things trust analysis method for frequency regulation transaction management of thermal power units according to claim 1 is characterized in that: According to the data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine several three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale, including: At the platform level of the power Internet of Things, the data information of the data storage sharing scale is obtained through the Internet of Things monitoring data center, and the statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy set S of nine fuzzy uncertainties of extremely low, very low, low, relatively low, medium, relatively high, high, very high and extremely high data storage sharing scale. Di , i=1,2,...,9: S Di =(S DiL ,S DiM ,S DiU )=[(S DiL1 ,S DiL2 ,S DiL3 ,S DiL4 ;k DSiL ), (S DiM1 ,S DiM2 ,S DiM3 ,S DiM4 ;k DSiM ), (S DiU1 ,S DiU2 ,S DiU3 ,S DiU4 ;k DSiU )]; In the formula, S Di is the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiL , S DiM , S DiU and k DSiL , k DSiM , k DSiU They are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the i-th three-dimensional trapezoidal fuzzy set of data storage sharing scale, S DiLj , S DiMj , S DiUj , j = 1, 2, 3, 4, which are the fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of the i-th three-dimensional trapezoidal fuzzy set of the data storage sharing scale respectively.

5. An Internet of Things trust analysis system for frequency regulation transaction management of thermal power units, characterized in that: include: The first calculation unit is used to calculate the frequency regulation price, frequency regulation power demand, frequency regulation power and quotation collection error of the power system in a certain period of time by using a statistical analysis method according to the frequency regulation price data, frequency regulation power demand data, frequency regulation power data of the thermal power units participating in the frequency regulation and quotation data of the power system; The second calculation unit is used to calculate and determine the data transmission rate and the three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of the data storage sharing scale by using a statistical analysis method according to the data transmission rate data and the data storage sharing scale data of the Internet of Things monitoring data center; The third calculation unit is used to calculate the frequency regulation power acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the frequency regulation power, and calculate the frequency regulation quotation acquisition error of the thermal power units participating in the frequency regulation according to the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and quotation; A fourth calculation unit is used to calculate the information benefit value of the power Internet of Things in frequency regulation trading according to a data transmission rate and a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale; A fifth calculation unit is used to determine the information loss probability of the power Internet of Things in the frequency regulation transaction, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power of the thermal power units participating in the frequency regulation and the quotation collection error formed in the power Internet of Things according to the information loss probability, the frequency regulation power of the thermal power units participating in the frequency regulation, and the frequency regulation quotation collection error; a sixth calculation unit, configured to calculate, according to the information loss value and the information gain value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for frequency regulation transactions of thermal power units; in: The third computing unit is specifically used for: Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power into the frequency regulation power acquisition error calculation formula to calculate the frequency regulation power acquisition error of the thermal power units involved in frequency regulation. Among them, the calculation formula for frequency modulation power acquisition error is: In the formula, is the three-dimensional trapezoidal fuzzy set of frequency modulation power demand, is the three-dimensional trapezoidal fuzzy set of FM power quantity, represents the union of fuzzy sets; Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation price and quotation collection error into the frequency regulation quotation collection error calculation formula of thermal power units participating in frequency regulation, and calculate the frequency regulation quotation collection error of thermal power units participating in frequency regulation. Among them, the frequency regulation quotation collection error calculation formula of the thermal power units participating in frequency regulation is: In the formula, To determine the three-dimensional trapezoidal fuzzy set of the frequency regulation price acquisition error of the power system in period t, The three-dimensional trapezoidal fuzzy set of the quotation collection error of the thermal power units participating in frequency regulation; The fourth computing unit is specifically configured to: Substitute the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate and data storage sharing scale into the information benefit value calculation formula, and calculate the information benefit value of the power Internet of Things in frequency regulation trading; The information benefit value calculation formula is: In the formula, R RP The information benefit value generated by the power Internet of Things providing users with data collection, transmission, storage and sharing in frequency regulation transactions; In order to provide users with the information benefit value formed by data transmission at 9 fuzzy uncertainty rates of very low, very low, low, lower, medium, higher, high, very high and very high, k Di is the unit profit value brought to users by the data transmission of the ith rate provided by the power Internet of Things; In order to provide users with the information benefit value of data storage sharing with 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, k DSi k is the unit benefit value brought to users by the data storage sharing of the ith rate provided by the power Internet of Things; Mi M T is the information benefit value formed by the Internet of Things providing data collection for thermal power units at the perception layer; E[] is the expected value of []. represents the union of 9 fuzzy sets; M T It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows: M T =(M TL ,M TM ,M TU )=[(M TL1 ,M TL2 ,M TL3 ,M TL4 ;k HL ), (M TM1 ,M TM2 ,M TM3 ,M TM4 ;k TM ), (M TU1 ,M TU2 ,M TU3 ,M TU4 ;k TU )]; Where M T The three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer, M TL 、M TM 、M TU and k HL , k TM , k TU The fuzzy sets and their membership coefficients are the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set that the Internet of Things provides for the thermal power unit in the perception layer. TLj 、M TMj 、M TUj (j=1, 2, 3, 4) are the fuzzy numbers of the lower, middle and upper bounds of the three-dimensional trapezoidal fuzzy set of the data collection scale provided by the Internet of Things for thermal power units at the perception layer; The fifth computing unit is specifically configured to: Substitute the probability of information loss, the frequency regulation power of thermal power units participating in frequency regulation, and the frequency regulation quotation collection error into the information loss value calculation formula, and calculate the information loss value of frequency regulation transactions caused by the frequency regulation power of thermal power units participating in frequency regulation and the quotation collection error formed in the power Internet of Things; The information loss value calculation formula is: Where L RP The frequency regulation power and quotation collection errors of the thermal power units involved in frequency regulation in the power Internet of Things will cause the information loss value of frequency regulation transactions; RP The probability of information loss of power Internet of Things in frequency regulation trading; It is the influence coefficient or weight coefficient of the frequency regulation power acquisition error of the thermal power units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the thermal power units involved in frequency regulation in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the thermal power units involved in frequency regulation formed in the power Internet of Things. is the unit loss value caused by the frequency regulation quotation collection error of thermal power units participating in frequency regulation in the power Internet of Things, N e is the number of time periods, To collect the frequency regulation power quantity error of the thermal power units involved in frequency regulation, To collect frequency regulation quotation errors for thermal power units participating in frequency regulation; The sixth computing unit is specifically configured to: Substituting the information loss value and the information gain value into the average value calculation formula, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for frequency regulation transactions of thermal power units is calculated; The average value calculation formula is: In the formula, B I is the average trust of all users in the power Internet of Things, R RP is the information benefit value, L RP is the information loss value.

6. The Internet of Things trust analysis system for frequency regulation transaction management of thermal power units according to claim 5 is characterized in that: Also includes: The evaluation unit is used to evaluate the trust level of the power Internet of Things according to the average value of the trust of all users of the power Internet of Things.

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