Method and system for analyzing the trustworthiness of frequency modulation transaction information of Internet of Things for gas-fired generator sets

Abstract

The present application discloses a method and system for analyzing the trust of frequency modulation trading information of the Internet of Things for gas-fired generator sets. The method 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 information sufficiency and omission to calculate the trust of frequency modulation trading information of the Internet of Things for gas-fired generator sets, and evaluates the impact of the trust on the frequency modulation trading information of gas-fired generator sets. The method targets the data information formed by data transmission rate and data storage sharing, and the data errors and omissions formed by data collection, and reflects the impact of the Internet of Things data information on the frequency modulation trading information of gas-fired generator sets. It provides theoretical guidance for the trust evaluation of frequency modulation trading information of the Internet of Things for gas-fired generator sets, and provides necessary technical support for frequency modulation trading 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 a method and system for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things (IoT) gas-fired generator set. 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 calculation method and existing technology of the trust of the Internet of Things frequency regulation transaction information of gas-fired generators. Therefore, there is an urgent need for a trust analysis method for the Internet of Things frequency regulation transaction information of gas-fired generators to calculate the trust of the power Internet of Things and evaluate the impact of trust on the frequency regulation transaction of gas-fired generators. Summary of the invention

[0005] The present application provides a method and system for analyzing the trust of frequency regulation transaction information of the Internet of Things of gas-fired power units, which are used to solve the problem of calculating the trust of the power Internet of Things and evaluate the impact of the trust on the frequency regulation transaction of gas-fired power units.

[0006] In view of this, the first aspect of the present application provides a method for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things gas-fired generator set, the method comprising:

[0007] According to the frequency regulation price data and frequency regulation power demand data of the power system, as well as the frequency regulation power data and quotation data of the gas-fired generator units participating in the frequency regulation, the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and frequency regulation power demand of the power system in the determined period, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the gas-fired generator units participating in the frequency regulation are calculated by using the statistical analysis method;

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

[0009] The frequency regulation power collection error of the gas-fired power units involved in frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of frequency regulation power demand and frequency regulation power collection error; the frequency regulation quotation collection error of the gas-fired power units involved in frequency regulation formed in the power Internet of Things 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 different levels of fuzzy uncertainty of data transmission rate, data bandwidth data, and data storage sharing scale, the information benefit value of the power Internet of Things at the perception layer, platform layer, and network layer is calculated respectively;

[0011] According to the frequency regulation power quantity collection error of the gas-fired power units participating in frequency regulation and the frequency regulation quotation collection error of the gas-fired power units participating in frequency regulation, the information loss value of the frequency regulation transaction caused by the frequency regulation power quantity and quotation collection error of the gas-fired power units participating in frequency regulation formed in the power Internet of Things is calculated;

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

[0013] Optionally, the calculating, according to the information gain value and the information loss value, an average value of the trust of all users of the power Internet of Things in the power Internet of Things for gas-fired generator frequency regulation transactions further includes:

[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 modulation power data of the gas-fired generator sets participating in the frequency modulation, a three-dimensional trapezoidal fuzzy set of acquisition errors of the frequency modulation power of the gas-fired generator sets participating in the frequency modulation in a certain period is calculated by a statistical analysis method, specifically including:

[0016] Using the IoT sensing system, the power market monitoring data center obtains data information on the frequency regulation power of gas-fired generators involved in frequency regulation;

[0017] According to the data information of the frequency modulation power of the gas-fired units participating in the frequency modulation, the statistical analysis method is used to calculate the three-dimensional trapezoidal fuzzy set of the frequency modulation power acquisition error of the gas-fired units participating in the frequency modulation during the period t. Where t=1,2,...,N RP :

[0018]

[0019]

[0020]

[0021] In the formula and are the frequency modulation power acquisition error three-dimensional trapezoidal fuzzy set of the gas-fired generator units participating in frequency modulation 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 trapezoidal fuzzy set, respectively. 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 modulation power acquisition error of the gas-fired generator units participating in frequency modulation in time period t.

[0022] Optionally, according to the data storage sharing scale data, 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: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di , where 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 ,S DiM3 ,S DiM4 ;k DSiM ),

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

[0027] Where 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 modulation power collection error of the gas-fired generator units participating in the frequency modulation formed in the power Internet of Things based on the frequency modulation power demand and the frequency modulation power collection error specifically includes:

[0029] The three-dimensional trapezoidal fuzzy set of frequency modulation power demand and frequency modulation power acquisition error is substituted into the frequency modulation power acquisition error calculation formula of the gas-fired generator units participating in frequency modulation, and the frequency modulation power acquisition error of the gas-fired generator units participating in frequency modulation formed in the power Internet of Things is calculated.

[0030] The frequency modulation power acquisition error calculation formula is:

[0031]

[0032] In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power demand, The three-dimensional trapezoidal fuzzy sets of frequency regulation power acquisition errors of gas-fired power units, thermal power units, nuclear power units, gas-fired power units, energy storage units, and west-to-east power transmission units participating in frequency regulation in time period t are respectively; Represents the union of fuzzy sets.

[0033] Optionally, the information benefit value of the power Internet of Things at the platform layer is calculated based on a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties at different levels of data storage sharing scale, specifically including:

[0034] Substitute several three-dimensional trapezoidal fuzzy sets of fuzzy uncertainty at different levels of data storage sharing scale into the information benefit value calculation formula of the platform layer to obtain the information benefit value of the power Internet of Things at the platform layer;

[0035] The information benefit value calculation formula is:

[0036]

[0037] In the formula, R RP2 The information revenue value generated by providing data storage and sharing to users in frequency regulation transactions through 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 It is the unit profit value brought to users by the data storage sharing at the i-th rate provided by the power Internet of Things.

[0038] Optionally, the frequency regulation power amount and quotation collection error of the gas-fired generator units participating in the frequency regulation, formed in the electric power Internet of Things, is calculated based on the frequency regulation power amount collection error of the gas-fired generator units participating in the frequency regulation, and the frequency regulation quotation collection error of the gas-fired generator units participating in the frequency regulation, which will cause the information loss value of the frequency regulation transaction, specifically including:

[0039] Substitute the frequency regulation power collection error of the gas-fired units participating in frequency regulation and the frequency regulation quotation collection error of the gas-fired units participating in frequency regulation into the information loss value calculation formula, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the gas-fired units participating in frequency regulation formed in the power Internet of Things;

[0040] Wherein, the information loss value calculation formula is:

[0041]

[0042] Where, L RP The frequency regulation power and quotation collection errors of gas-fired units involved in frequency regulation in the power Internet of Things will cause information loss value of frequency regulation transactions; It is the influence coefficient or weight coefficient of the frequency modulation power acquisition error of the gas-fired generator units involved in frequency modulation formed in the power Internet of Things. It is the unit loss value caused by the frequency modulation power collection error of the gas-fired generator units involved in frequency modulation in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the gas-fired generator units involved in frequency regulation formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of gas-fired units participating in frequency regulation in the power Internet of Things. To collect the frequency modulation power error of the gas-fired generator set, To collect errors in frequency regulation quotations for gas-fired power units participating in frequency regulation.

[0043] Optionally, the calculating, based on the information gain value and the information loss value, an average value of the trust of the power Internet of Things for all users in the power Internet of Things for gas-fired generator frequency regulation transactions specifically includes:

[0044] Substituting the information gain value and the information loss 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 gas-fired generator frequency regulation transactions is calculated;

[0045] Wherein, the average value calculation formula is:

[0046]

[0047] In the formula, B I is the average trust of all users in the power Internet of Things, R RP1 , R RP2 , R RP3 are the information benefits of the perception layer, platform layer, and network layer respectively, L RP is the information loss value.

[0048] A second aspect of the present application provides an Internet of Things frequency modulation transaction information trust analysis system for gas-fired generator sets, the system comprising:

[0049] The first calculation unit is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in a certain period of time, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and the quotation of the gas-fired generator units participating in the frequency regulation by using a statistical analysis method according to the frequency regulation price data and the frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the gas-fired generator units participating in the frequency regulation;

[0050] The second calculation unit is used to calculate and determine the data transmission rate, data bandwidth data and data storage sharing scale data of the Internet of Things monitoring data center by using a statistical analysis method, respectively, a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth data and data storage sharing scale;

[0051] The third calculation unit is used to calculate the frequency regulation power collection error of the gas-fired power units participating in the frequency regulation formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the collection error of the frequency regulation power, and calculate the frequency regulation quotation collection error of the gas-fired power units participating in the frequency regulation formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the collection error of the frequency regulation price and quotation;

[0052] The fourth calculation unit is used to calculate the information benefit values ​​of the power Internet of Things at the perception layer, the platform layer and the network layer respectively according to a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth data and data storage sharing scale;

[0053] A fifth calculation unit is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the gas-fired power units participating in the frequency regulation formed in the power Internet of Things according to the frequency regulation power collection error of the gas-fired power units participating in the frequency regulation and the frequency regulation quotation collection error of the gas-fired power units participating in the frequency regulation;

[0054] 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 gas-fired power units based on the information gain value and the information loss value.

[0055] 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.

[0056] It can be seen from the above technical solutions that this application has the following advantages:

[0057] The technical effect of this application is: the trust degree calculation method of Internet of Things frequency modulation transaction information for gas-fired generator sets proposed in this application can be used to calculate the trust degree of Internet of Things frequency modulation transaction information for gas-fired generator sets. This method of calculating the trust degree of Internet of Things frequency modulation transaction information for gas-fired generator sets simultaneously reflects the influence of the perception system, data transmission rate, and data storage sharing, provides theoretical guidance for the evaluation of the trust degree of Internet of Things frequency modulation transaction information for gas-fired generator sets, and provides necessary technical support for frequency modulation transactions based on the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a method for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things gas-fired generator set provided in an embodiment of the present application;

[0059] Figure 2 This is a structural diagram of a system for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things gas-fired generator set provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] 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.

[0061] See also Figure 1 , a method for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things gas-fired generator set provided in an embodiment of the present application includes:

[0062] Step 101: According to the frequency regulation price data and frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the gas-fired generator units participating in the frequency regulation, a statistical analysis method is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and frequency regulation power demand of the power system in a certain period, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and quotation of the gas-fired generator units participating in the frequency regulation;

[0063] It should be noted that step 101 is a process and method for evaluating the data collection error of gas-fired power unit frequency regulation transaction information in the Internet of Things. At the perception layer of the power Internet of Things, the frequency regulation price of the power system, the power demand for frequency regulation of the power system, the frequency regulation power of the gas-fired 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-fired 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.

[0064] Specific examples include:

[0065] 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)

[0066]

[0067]

[0068]

[0069] 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. 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.

[0070] 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

[0071]

[0072]

[0073]

[0074] 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 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.

[0075] 1013. Use the IoT sensing system to obtain relevant data information about the frequency modulation power of gas-fired units participating in frequency modulation from the power market monitoring data center. According to the obtained data about the frequency modulation power of gas-fired 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 3D trapezoidal fuzzy set of frequency modulation power acquisition error of gas-fired generator units involved in frequency modulation

[0076]

[0077]

[0078]

[0079] In the formula, and are the frequency modulation power acquisition error three-dimensional trapezoidal fuzzy set of the gas-fired generator units participating in frequency modulation 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 trapezoidal fuzzy set, respectively. 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 modulation power acquisition error of the gas-fired generator units participating in frequency modulation in time period t.

[0080] 1014. Use the Internet of Things sensing system to obtain relevant data information on the quotations of gas-fired units participating in frequency regulation from the power market monitoring data center. According to the quotations of gas-fired 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 3D trapezoidal fuzzy set of price quotation collection error of gas-fired generator sets involved in frequency regulation

[0081]

[0082]

[0083]

[0084] In the formula, and are the three-dimensional trapezoidal fuzzy set of the quotation collection error of the gas-fired generator units participating in frequency modulation 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 trapezoidal fuzzy set, 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 gas-fired power units participating in frequency modulation during time period t.

[0085] Step 102: According to the data transmission rate, data bandwidth data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to respectively calculate and determine a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth data and data storage sharing scale;

[0086] It should be noted that step 102 is a process and method for monitoring the data transmission rate and data bandwidth 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.

[0087] Specifically, they include:

[0088] 1021. The process and method for monitoring the data transmission rate and data bandwidth of the power Internet of Things for power market frequency regulation transactions.

[0089] 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):

[0090] v Di =(v DiL ,v DiM ,v DiU )=[(v DiL1 ,v DiL2 ,v DiL3 ,v DiL4 ;k DviL ),

[0091] (v DiM1 ,v DiM2 ,v DiM3 ,v DiM4 ;k DviM ),

[0092] (v DiU1 ,v DiU2 ,v DiU3 ,v DiU4 ;k DviU )];

[0093] 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.

[0094] At the network layer of the power Internet of Things, relevant data information of data bandwidth is obtained from the Internet of Things monitoring data center, and statistical analysis methods are used to calculate and determine the three-dimensional trapezoidal fuzzy sets v of nine fuzzy uncertainties of data bandwidth: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. Di (i=1,2,...,9):

[0095] B Di =(B DiL ,BDiM ,B DiU )=[(B DiL1 ,B DiL2 ,B DiL3 ,B DiL4 ;k DBiL ),

[0096] (B DiM1 ,B DiM2 ,B DiM3 ,B DiM4 ;k DBiM ),

[0097] (B DiU1 ,B DiU2 ,B DiU3 ,B DiU4 ;k DBiU )];

[0098] In the formula, B Di is the i-th three-dimensional trapezoidal fuzzy set of daily power generation, B DiL , B DiM , B DiU and k DBiL , k DBiM , k DBiU are the fuzzy sets and membership coefficients of the lower, middle and upper bounds of the ith three-dimensional trapezoidal fuzzy set of data bandwidth, respectively. DiLj , B DiMj , B 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 bandwidth respectively.

[0099] 1022. Process and method for evaluating the scale of power Internet of Things data storage sharing for power market frequency regulation transactions.

[0100] 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):

[0101] S Di =(S DiL ,S DiM ,S DiU )=[(S DiL1 ,S DiL2 ,S DiL3 ,S DiL4 ;k DSiL ),

[0102] (SDiM1 ,S DiM2 ,S DiM3 ,S DiM4 ;k DSiM ),

[0103] (S DiU1 ,S DiU2 ,S DiU3 ,S DiU4 ;k DSiU )];

[0104] Where 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.

[0105] Step 103, the frequency modulation power acquisition error of the gas-fired units participating in the frequency modulation formed in the power Internet of Things is calculated according to the three-dimensional trapezoidal fuzzy set of the frequency modulation power demand and the acquisition error of the frequency modulation power, and the frequency modulation quotation acquisition error of the gas-fired units participating in the frequency modulation formed in the power Internet of Things is calculated according to the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation price and quotation;

[0106] It should be noted that step 103 is the process and method for calculating the error in frequency regulation power collection of the frequency regulation units formed in the power Internet of Things; and the process and method for calculating the error in frequency regulation quotation collection of the frequency regulation units formed in the power Internet of Things.

[0107] Specific examples include:

[0108] 1031. The process and method for calculating the error in frequency regulation power collection of frequency regulation units formed in the power Internet of Things.

[0109] The frequency regulation power collection error of the frequency regulation units 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 various generator sets. The frequency regulation power collection error of various generator sets includes the frequency regulation power of gas-fired units participating in frequency regulation, the frequency regulation power of thermal power units participating in frequency regulation, the frequency regulation power of nuclear power units participating in frequency regulation, the frequency regulation power of gas-fired units participating in frequency regulation, the frequency regulation power of energy storage units participating in frequency regulation, and the frequency regulation power collection error of west-to-east power transmission units participating in frequency regulation. Therefore, the frequency regulation power collection error of the frequency regulation units participating in the power Internet of Things is is calculated according to the following formula:

[0110]

[0111] In the formula, Represents the union of fuzzy sets.

[0112] 1032. The process and method for calculating the error in collecting frequency quotation of frequency regulating units formed in the power Internet of Things.

[0113] The frequency regulation quotation collection error of the units participating 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 quotation collection error of various generating units. The frequency regulation quotation collection error of various generating units includes the frequency regulation quotation of gas-fired units participating in frequency regulation, the frequency regulation quotation of thermal power units participating in frequency regulation, the frequency regulation quotation of nuclear power units participating in frequency regulation, the frequency regulation quotation of gas-fired units participating in frequency regulation, the frequency regulation quotation of energy storage units participating in frequency regulation, and the frequency regulation quotation collection error of west-to-east power transmission units participating in frequency regulation. Therefore, the frequency regulation quotation collection error of the units participating in frequency regulation formed in the power Internet of Things is calculated according to the following formula:

[0114]

[0115] Step 104, according to the data transmission rate, data bandwidth data, and the three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of the data storage sharing scale, respectively calculate the information benefit value of the power Internet of Things at the perception layer, platform layer and network layer;

[0116] 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:

[0117] 1041. Calculation of information benefit value of the perception layer. In 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 quotations. The calculation formula for the information benefit value of the perception layer is as follows:

[0118] R RP1=E[k Mi M H ];

[0119] In the formula, R RP1 The information benefit value generated by the power Internet of Things providing data collection to users in frequency regulation transactions; k Mi M H is the information benefit value formed by the data collection provided by the Internet of Things for the gas-fired generator set at the perception layer; E[] is the expected value of []. Represents the union of 9 fuzzy sets.

[0120] M H It can be expressed by three-dimensional trapezoidal fuzzy sets, and the specific formula is as follows:

[0121] M H =(M HL ,M HM ,M HU )=[(M HL1 ,M HL2 ,M HL3 ,M HL4 ;k HL ),

[0122] (M HM1 ,M HM2 ,M HM3 ,M HM4 ;k HM ),

[0123] (M HU1 ,M HU2 ,M HU3 ,M HU4 ;k HU )];

[0124] Where M H The three-dimensional trapezoidal fuzzy set of data collection scale is provided for gas-fired units in the perception layer of the Internet of Things. HL 、M HM 、M HU and k HL , k HM , k HU 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 gas-fired unit in the perception layer. HLj 、M HMj 、M HUj (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 the gas-fired generator set at the perception layer.

[0125] 1042. Calculation of information benefit value at the platform layer. 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. The information benefit value calculation formula at the platform layer is as follows:

[0126]

[0127] In the formula, R RP2 The information revenue value generated by providing data storage and sharing to users in frequency regulation transactions through 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 It is the unit profit value brought to users by the data storage sharing at the i-th rate provided by the power Internet of Things.

[0128] 1043. Calculation of information benefit value at the network layer. At the network layer, with the help of the network system, data transmission is realized, so that frequency modulation users can obtain sufficient information and gain benefits by using the Internet of Things. The calculation formula of information benefit value at the network layer is as follows:

[0129]

[0130] In the formula, R RP3 The information revenue value generated by the power Internet of Things providing data transmission to users in frequency modulation 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 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 DBi is the unit profit value brought to users by the data transmission rate of the power Internet of Things.

[0131] Step 105: Calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the gas-fired generator units participating in the frequency regulation formed in the power Internet of Things according to the frequency regulation power collection errors and the frequency regulation quotation collection errors of the gas-fired generator units participating in the frequency regulation;

[0132] It should be noted that step 105 is a process and method for describing the calculation of the information loss value of the power Internet of Things in frequency modulation trading, which specifically includes:

[0133] The frequency regulation power and quotation collection errors of the frequency regulation units 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:

[0134]

[0135] Where, L RP The frequency regulation power and quotation collection errors of the frequency regulation units formed 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 frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; It is the influence coefficient or weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the error in collecting frequency quotation of frequency regulation units in the power Internet of Things.

[0136] Step 106: Calculate the average trust of all users in the power Internet of Things for frequency regulation transactions of gas-fired generator units based on the information gain value and the information loss value.

[0137] It should be noted that step 106 is a process and method for calculating the trust of the power Internet of Things, specifically:

[0138] In the power Internet of Things for gas-fired unit frequency regulation transaction information, 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:

[0139]

[0140] In one embodiment, the method for analyzing the trustworthiness of frequency modulation transaction information of the Internet of Things for gas-fired generator sets further includes:

[0141] 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.

[0142] 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:

[0143] When B I =1, the electric power Internet of Things is fully adapted to the application of frequency regulation transaction information of gas-fired power units.

[0144] When 0.8≤B I When <1, the power Internet of Things is well adapted to the application of frequency regulation transaction information of gas-fired power units.

[0145] When 0.5≤B I When <0.8, the electric power Internet of Things is better adapted to the application of frequency regulation transaction information of gas-fired power units.

[0146] When 0.3≤B I When <0.5, the power Internet of Things is suitable for the application of frequency regulation transaction information of gas-fired power units.

[0147] When 0≤B I When <0.3, the electric power Internet of Things cannot adapt to the application of frequency regulation transaction information of gas-fired power units.

[0148] The method for analyzing the trust of frequency modulation transaction information of the Internet of Things for gas-fired generator sets provided in this embodiment 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 information sufficiency and omission to calculate the trust of frequency modulation transaction information of the Internet of Things for gas-fired generator sets, and evaluates the impact of the trust on the frequency modulation transaction information of gas-fired generator sets. This method targets the data information formed by data transmission rate and data storage sharing, and the data errors and omissions formed by data collection, and reflects the impact of the Internet of Things data information on the frequency modulation transaction information of gas-fired generator sets.

[0149] The above is a method for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things gas-fired generator set provided in an embodiment of the present application, and the following is a system for analyzing the trustworthiness of frequency modulation transaction information of an Internet of Things gas-fired generator set provided in an embodiment of the present application.

[0150] See also Figure 2 , a gas-generator unit Internet of Things frequency modulation transaction information trust analysis system provided in an embodiment of the present application includes:

[0151] The first calculation unit 201 is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and the frequency regulation power demand of the power system in a certain period of time, and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power and the quotation of the gas-fired generator units participating in the frequency regulation by using a statistical analysis method according to the frequency regulation price data and the frequency regulation power demand data of the power system, and the frequency regulation power data and quotation data of the gas-fired generator units participating in the frequency regulation;

[0152] The second calculation unit 202 is used to calculate and determine the data transmission rate, data bandwidth data, and data storage sharing scale data of the Internet of Things monitoring data center by using a statistical analysis method, respectively, a plurality of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth data, and data storage sharing scale;

[0153] The third calculation unit 203 is used to calculate the frequency regulation power acquisition error of the gas-fired generator units participating in the frequency regulation formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand and the acquisition error of the frequency regulation power, and calculate the frequency regulation quotation acquisition error of the gas-fired generator units participating in the frequency regulation formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price and quotation;

[0154] The fourth calculation unit 204 is used to calculate the information benefit values ​​of the power Internet of Things at the perception layer, the platform layer and the network layer respectively according to a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth data and data storage sharing scale;

[0155] The fifth calculation unit 205 is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the gas-fired generator units participating in the frequency regulation formed in the power Internet of Things according to the frequency regulation power collection error of the gas-fired generator units participating in the frequency regulation and the frequency regulation quotation collection error of the gas-fired generator units participating in the frequency regulation;

[0156] The sixth calculation unit 206 is used to calculate the average value of the trust of all users in the power Internet of Things for frequency regulation transactions of gas-fired generators based on the information gain value and the information loss value.

[0157] In one embodiment, the Internet of Things frequency modulation transaction information trust analysis system for gas-fired generator sets further includes: an evaluation unit;

[0158] 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 the power Internet of Things to all users.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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. A method for analyzing the trustworthiness of frequency modulation transaction information of the Internet of Things of gas-fired generator sets, characterized in that: include: According to the frequency regulation price data and frequency regulation power demand data of the power system, as well as the frequency regulation power data and quotation data of the gas-fired generator units participating in the frequency regulation, a statistical analysis method is used to respectively calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price of the power system during the determined period and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power demand, as well as the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the gas-fired generator units participating in the frequency regulation and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotation; According to the data transmission rate, data bandwidth data and data storage sharing scale data of the IoT monitoring data center, a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties of different levels of data transmission rate, data bandwidth data and data storage sharing scale. The frequency regulation power collection error of the units participating in the frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand collection error and the three-dimensional trapezoidal fuzzy set of the frequency regulation power collection error; the frequency quotation collection error of the units participating in the frequency regulation formed in the power Internet of Things is calculated based on the three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error and the three-dimensional trapezoidal fuzzy set of the quotation collection error; According to the three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth data, and data storage sharing scale, the information benefit value of the power Internet of Things at the perception layer, platform layer, and network layer is calculated respectively; According to the frequency regulation power collection error of the frequency regulation units involved and the frequency regulation quotation collection error of the frequency regulation units involved, the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation units involved in the power Internet of Things is calculated; According to the information gain value and the information loss value, the average value of the trust of the power Internet of Things to all users in the power Internet of Things for gas-fired generator frequency regulation transactions is calculated; in, The method of calculating the frequency regulation power collection error of the frequency regulation units involved in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand collection error and the three-dimensional trapezoidal fuzzy set of the frequency regulation power collection error specifically includes: Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation power demand collection error and the three-dimensional trapezoidal fuzzy set of frequency regulation power collection error into the frequency regulation power collection error calculation formula of the frequency regulation units involved in the frequency regulation, and calculate the frequency regulation power collection error of the frequency regulation units involved in the power Internet of Things. ; The frequency modulation power acquisition error calculation formula is: ； In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power demand, , , , , , The time periods The three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of hydropower units, thermal power units, nuclear power units, gas power units, energy storage units, and west-to-east power transmission units involved in frequency regulation; represents the union of fuzzy sets; According to several three-dimensional trapezoidal fuzzy sets of fuzzy uncertainty at different levels of data storage and sharing scale, the information benefit value of the power Internet of Things at the platform layer is calculated, including: Substitute several three-dimensional trapezoidal fuzzy sets of fuzzy uncertainty at different levels of data storage sharing scale into the information benefit value calculation formula of the platform layer to obtain the information benefit value of the power Internet of Things at the platform layer; The information benefit value calculation formula is: ； In the formula, The information revenue value generated by providing data storage and sharing to users in frequency regulation transactions through the power Internet of Things; In order to provide users with information benefit values ​​formed by data storage sharing at 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, Provide the first The unit benefit value brought to users by data storage sharing at a certain rate; The frequency regulation power amount and quotation collection error of the frequency regulation units involved in the frequency regulation are calculated in the power Internet of Things, and the information loss value of the frequency regulation transaction caused by the frequency regulation power amount and quotation collection error of the frequency regulation units involved in the frequency regulation is specifically calculated as follows: Substitute the frequency regulation power collection error of the frequency regulation units and the frequency regulation quotation collection error of the frequency regulation units into the information loss value calculation formula, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation units formed in the power Internet of Things; Wherein, the information loss value calculation formula is: ； In the formula, The frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction; is the weight coefficient of the frequency regulation power acquisition error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; is the weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation units, Collect frequency quotation errors for frequency regulation units; The information gain value and the information loss value are used to calculate the average trust of all users in the power Internet of Things for gas-fired generator frequency regulation transactions, specifically including: Substituting the information gain value and the information loss 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 gas-fired generator frequency regulation transactions is calculated; Wherein, the average value calculation formula is: ； In the formula, is the average trust level of all users in the power Internet of Things. , , are the information benefits of the perception layer, platform layer, and network layer respectively. is the information loss value.

2. The method for analyzing the trustworthiness of frequency modulation transaction information of the Internet of Things of gas-fired generator sets according to claim 1 is characterized in that: The method further comprises calculating the average value of the trust of all users of the power Internet of Things in the power Internet of Things for gas-fired generator frequency regulation transactions based on the information gain value and the information loss 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 method for analyzing the trustworthiness of frequency modulation transaction information of the Internet of Things of gas-fired generator sets according to claim 1 is characterized in that: According to the frequency modulation power data of the gas-fired generator sets participating in frequency modulation, the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power of the gas-fired generator sets participating in frequency modulation in the determined period is calculated by statistical analysis method, specifically including: Using the IoT sensing system, the frequency regulation power data of the gas-fired generator units participating in the frequency regulation are obtained from the power market monitoring data center; According to the data information of the frequency modulation power of the gas-fired units involved in frequency modulation, the statistical analysis method is used to calculate and determine the time period , the three-dimensional trapezoidal fuzzy set of frequency modulation power acquisition error of gas-fired units involved in frequency modulation ,in, : ； ； ； In the formula, and are the membership coefficients corresponding to the three-dimensional trapezoidal fuzzy set or the lower bound, middle bound and upper bound of the frequency modulation power acquisition error of the gas-fired generator units participating in the frequency modulation in time period t, , , ( )and , , They are respectively the membership coefficients corresponding to the three-dimensional trapezoidal fuzzy set of the frequency regulation power acquisition error of the gas-fired generator units participating in the frequency regulation in time period t or the three-dimensional trapezoidal lower bound, middle bound and upper bound.

4. The method for analyzing the trustworthiness of frequency modulation transaction information of the Internet of Things of gas-fired generator sets according to claim 1 is characterized in that: According to the data storage sharing scale data, a statistical analysis method is used to calculate and determine the three-dimensional trapezoidal fuzzy sets of several 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 sets of nine fuzzy uncertainties: extremely low, very low, low, relatively low, medium, relatively high, high, very high, and extremely high. ,in, : ； ； ； In the formula For data storage sharing scale Three-dimensional trapezoidal fuzzy sets, , , and , , They are respectively the data storage sharing scale The fuzzy sets and membership coefficients of the lower, middle and upper bounds of three-dimensional trapezoidal fuzzy sets, , , , , respectively, data storage sharing scale The fuzzy numbers of the lower bound, middle bound and upper bound fuzzy sets of a three-dimensional trapezoidal fuzzy set.

5. A gas-fired generator Internet of Things frequency modulation transaction information trust analysis system, characterized in that: include: The first calculation unit is used to calculate the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation price of the power system in a certain period and the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power demand, as well as the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency regulation power of the gas-fired units participating in the frequency regulation and the three-dimensional trapezoidal fuzzy set of the acquisition error of the quotation by using a statistical analysis method according to the frequency regulation price data and the frequency regulation power demand data of the power system, as well as the frequency regulation power data and quotation data of the gas-fired units participating in the frequency regulation; The second calculation unit is used to calculate and determine the data transmission rate, data bandwidth data and data storage sharing scale data of the Internet of Things monitoring data center by using a statistical analysis method, respectively, a number of three-dimensional trapezoidal fuzzy sets of fuzzy uncertainties of different levels of data transmission rate, data bandwidth data and data storage sharing scale; The third calculation unit is used to calculate the frequency regulation power collection error of the participating frequency regulation units formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand collection error and the three-dimensional trapezoidal fuzzy set of the frequency regulation power collection error, and calculate the frequency regulation quotation collection error of the participating frequency regulation units formed in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation price collection error and the three-dimensional trapezoidal fuzzy set of the quotation collection error; The fourth calculation unit is used to calculate the information benefit values ​​of the power Internet of Things at the perception layer, the platform layer and the network layer respectively according to a plurality of three-dimensional trapezoidal fuzzy sets of different levels of fuzzy uncertainty of data transmission rate, data bandwidth data and data storage sharing scale; A fifth calculation unit is used to calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things according to the frequency regulation power collection errors of the frequency regulation units and the frequency regulation quotation collection errors of the frequency regulation units; a sixth calculation unit, configured to calculate, according to the information gain value and the information loss 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 gas-fired generator sets; in, The method of calculating the frequency regulation power collection error of the frequency regulation units involved in the power Internet of Things according to the three-dimensional trapezoidal fuzzy set of the frequency regulation power demand collection error and the three-dimensional trapezoidal fuzzy set of the frequency regulation power collection error specifically includes: Substitute the three-dimensional trapezoidal fuzzy set of frequency regulation power demand collection error and the three-dimensional trapezoidal fuzzy set of frequency regulation power collection error into the frequency regulation power collection error calculation formula of the frequency regulation units involved in the frequency regulation, and calculate the frequency regulation power collection error of the frequency regulation units involved in the power Internet of Things. ; The frequency modulation power acquisition error calculation formula is: ； In the formula, is the three-dimensional trapezoidal fuzzy set of the acquisition error of the frequency modulation power demand, , , , , , The time periods The three-dimensional trapezoidal fuzzy set of frequency regulation power acquisition error of hydropower units, thermal power units, nuclear power units, gas power units, energy storage units, and west-to-east power transmission units involved in frequency regulation; represents the union of fuzzy sets; According to the three-dimensional trapezoidal fuzzy sets of several fuzzy uncertainties at different levels of data storage and sharing scale, the information benefit value of the power Internet of Things at the platform layer is calculated, including: Substitute several three-dimensional trapezoidal fuzzy sets of fuzzy uncertainty at different levels of data storage sharing scale into the information benefit value calculation formula of the platform layer to obtain the information benefit value of the power Internet of Things at the platform layer; The information benefit value calculation formula is: ； In the formula, The information revenue value generated by providing data storage and sharing to users in frequency regulation transactions through the power Internet of Things; In order to provide users with information benefit values ​​formed by data storage sharing at 9 fuzzy uncertainty scales of very low, very low, low, lower, medium, higher, high, very high and very high, Provide the first The unit benefit value brought to users by data storage sharing at a certain rate; The frequency regulation power amount and quotation collection error of the frequency regulation units involved in the frequency regulation are calculated in the electric power Internet of Things, and the information loss value of the frequency regulation transaction caused by the frequency regulation power amount and quotation collection error of the frequency regulation units involved in the frequency regulation is specifically calculated as follows: Substitute the frequency regulation power collection error of the frequency regulation units and the frequency regulation quotation collection error of the frequency regulation units into the information loss value calculation formula, and calculate the information loss value of the frequency regulation transaction caused by the frequency regulation power and quotation collection error of the frequency regulation units formed in the power Internet of Things; Wherein, the information loss value calculation formula is: ； In the formula, The frequency regulation power and quotation collection errors of the frequency regulation units formed in the power Internet of Things will cause the information loss value of the frequency regulation transaction; is the weight coefficient of the frequency regulation power acquisition error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation power collection error of the frequency regulation units formed in the power Internet of Things; is the weight coefficient of the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. It is the unit loss value caused by the frequency regulation quotation collection error of the frequency regulation units formed in the power Internet of Things. To collect the frequency regulation power error of the frequency regulation units, Collect frequency quotation errors for frequency regulation units; The information gain value and the information loss value are used to calculate the average trust of all users in the power Internet of Things for gas-fired generator frequency regulation transactions, specifically including: Substituting the information gain value and the information loss 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 gas-fired generator frequency regulation transactions is calculated; Wherein, the average value calculation formula is: ； In the formula, is the average trust level of all users in the power Internet of Things. , , are the information benefits of the perception layer, platform layer, and network layer respectively. is the information loss value.

6. The Internet of Things frequency modulation transaction information trust analysis system of gas-fired generator set 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 the power Internet of Things to all users.

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