A wind power network business flow control system

Abstract

The present invention provides a wind power network service flow control system, which belongs to the technical field of wind power network service flow; the system comprises: an environment acquisition module: collecting and analyzing the specific environment information of the target wind power equipment at each execution moment, and obtaining the environment coefficient and operation monitoring value of the target wind power equipment at the corresponding execution moment; an information capture module: acquiring the network service flow sent to the target wind power equipment, and capturing the execution information of the target wind power equipment at each execution moment according to the network service flow; an abnormality prediction module: performing abnormality prediction on the environment coefficient, operation monitoring value and execution information at each execution moment, and feeding back the service adjustment instruction to the future execution moment, so as to control the corresponding target wind power equipment to work continuously, thereby improving the effectiveness of abnormality judgment of the wind power equipment and providing a data basis for the stable operation of the wind power equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power network service flow technology, and in particular to a wind power network service flow control system. Background Art

[0002] At present, the emergence of wind power network business flow control system stems from the development and application of wind power generation technology. With the scale and complexity of wind power generation equipment, the traditional manual detection and maintenance methods can no longer meet the needs of efficient and accurate management of wind power equipment. Wind power operation and maintenance management methods based on network monitoring and control have been widely used.

[0003] The above method generally monitors the wind power equipment itself by installing monitoring components on the wind power equipment itself to determine whether there is an abnormality, and the abnormality is only for the current moment. Moreover, since it is only monitoring the wind power equipment itself, the wind power equipment is seriously dependent on the current environmental wind conditions. In summary, it will undoubtedly reduce the effectiveness of abnormality determination, and thus reduce the reliability of stable operation of the wind power equipment.

[0004] Therefore, the present invention proposes a wind power network service flow control system. Summary of the invention

[0005] The present invention provides a wind power network service flow control system, which is used to capture the execution results of wind power equipment according to the network service flow and combine it with real-time collection of the environment to effectively predict the abnormalities of wind power equipment, so as to facilitate timely adjustment and control at future times, ensure the stable operation of wind power equipment, and improve the reliability of wind power equipment.

[0006] The present invention provides a wind power network service flow control system, comprising:

[0007] Environmental acquisition module: collects and analyzes the specific environmental information of the target wind power equipment at each execution time, and obtains the environmental coefficient and operation monitoring value of the target wind power equipment at the corresponding execution time;

[0008] Information capturing module: acquiring the network service flow sent to the target wind power equipment, and capturing the execution information of the target wind power equipment at each execution time according to the network service flow;

[0009] Abnormal prediction module: It predicts abnormalities of environmental coefficients, operation monitoring values ​​and execution information at each execution moment, and feeds back business adjustment instructions to future execution moments to control the corresponding target wind power equipment to work continuously.

[0010] In one possible implementation, it also includes:

[0011] Tag setting module: setting a first electronic tag for each target wind power device, and at the same time, setting a second electronic tag for each environmental monitoring sensor device;

[0012] Mapping and matching module: maps and matches the first electronic tag with the second electronic tag, determines the environmental monitoring sensor device that monitors the same target wind power equipment, and realizes the collection of specific environmental information.

[0013] In a possible implementation, the environment acquisition module includes:

[0014] Distribution unit: obtain the current distribution location of each target wind power equipment, the geographical environment of the current distribution location, and the equipment type, and construct a detection area based on the target wind power equipment;

[0015] Detection unit: Based on the positional relationship between the environmental monitoring sensor equipment and the detection area, the specific environmental information of each target wind power equipment at each execution moment is detected.

[0016] In a possible implementation, the environment acquisition module further includes:

[0017] A matrix construction unit: extracting wind parameters from the specific environmental information of each target wind power equipment detected, and constructing a wind parameter matrix for the target wind power equipment, wherein the rows of the wind parameter matrix contain all wind parameters involved in the same execution subframe, and the columns contain the same wind parameters involved in different execution subframes;

[0018] Matrix analysis unit: performing characteristic calculation on the wind parameter matrix to obtain a characteristic vector, and at the same time, performing a parameter comprehensive analysis of the corresponding execution subframe on each row in the wind parameter matrix to obtain a first coefficient, and performing a parameter trend analysis at the execution time on each column in the wind parameter matrix to obtain a second coefficient;

[0019] A coefficient determination unit: based on all the first coefficients and the second coefficients, determines an environment coefficient corresponding to the execution time;

[0020] Value determination unit: obtains the operation monitoring value matching the feature vector from the vector-value mapping table.

[0021] In a possible implementation, the information capturing module includes:

[0022] A service flow comparison unit: obtaining a network service flow sent to the target wind power equipment and a current service flow received by the target wind power equipment, and determining whether the current service flow is consistent with the network service flow;

[0023] Information capturing unit: if yes, capturing the first information of the target wind power equipment at each execution time according to the network service flow;

[0024] Otherwise, capturing the second information of the target wind power equipment at each execution moment according to the current business flow;

[0025] Information processing unit: pre-processes the final information obtained to obtain execution information.

[0026] In a possible implementation, the information processing unit includes:

[0027] The first determination block: logically analyzes each sub-information in the last information obtained at each execution moment based on the logic analysis model, and spatially maps each sub-information based on the logic-space mapping table to determine the business logic and the logical space matching the business logic;

[0028] The second determination block: Count the number of business logics in the same logical space to determine the business importance and information density of the same logical space;

[0029] ; Where n1 represents the business logic statistics in the same logical space; Indicates the logical length of the i1th business logic in the same logical space; Indicates the total length of information contained in the same logical space; Represents the spatial weight of the same logical space; Indicates that based on all The variance of Indicates the logical weight of the i1th business logic in the same logical space; Indicates the total maximum weight when the same logical space is filled; It represents the total average weight of the same logical space when it is filled; Z1 represents the business importance of the same logical space; R1 represents the information density of the same logical space; It indicates the total number of planned logics when the same logic space is filled; lg indicates the logarithmic function symbol;

[0030] Filter block: Filter and retain sub-information from all sub-information at the same execution time according to business importance and information density to obtain execution information at the same execution time.

[0031] In a possible implementation, the abnormality prediction module includes:

[0032] Standard threshold determination unit: obtain the environmental coefficient at the corresponding execution time And running monitoring value Based on the preset standard threshold YH for each environmental parameter in the specific environmental information:

[0033] ;in, Represents the mean value of the standardized data of the specific environment information at the corresponding execution time; Represents the normalized value of the jth environmental parameter at the corresponding execution time; represents the variance of the standardized specific environment information at the corresponding execution time; 0.01 represents the error coefficient of the overall environment; n represents the number of environmental parameters involved in the specific environment information; represents the preset standard threshold value under the j-th environmental parameter at the corresponding execution time; Indicates based on running monitoring value The threshold adjustment coefficient of is in the range of (0.01, 0.02);

[0034] Operation monitoring abnormality judgment unit: extracts features from the execution information at the corresponding execution time, and analyzes the feature values ​​and preset standard thresholds to determine whether the corresponding target wind power equipment has an abnormality at the corresponding execution time;

[0035] ;in, Indicates all preset standard thresholds at the corresponding execution time The mean of ; m represents the total number of sub-information in the execution information at the corresponding execution time; represents the signal value of the kth sub-information at the corresponding execution time after being standardized according to the environmental parameters; represents the analysis value at the corresponding execution time; ln represents the logarithmic function sign;

[0036] If the analysis value is greater than the preset execution value, it is determined that there is no abnormality in the target wind power equipment at the corresponding execution time.

[0037] In a possible implementation, the abnormality prediction module further includes:

[0038] Analysis unit: statistics the analysis values ​​at different execution times, constructs an analysis curve, and performs periodic analysis on the analysis curve and analysis on the number of abnormal occurrences in the same period;

[0039] Prediction unit: According to the periodic analysis results and the analysis results of the number of abnormal occurrences, the high-frequency abnormal points in the future period are determined, and the future execution time consistent with the high-frequency abnormal points is locked to retrieve the normal execution parameters consistent with the high-frequency abnormal points and the abnormal adjustment parameters for abnormal conversion to normal from the analysis curve, and the future execution parameters of the future execution time are predicted and adjusted and fed back to control the corresponding target wind power equipment to work continuously.

[0040] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0041] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 It is a structural diagram of a wind power network service flow control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0045] Embodiment 1:

[0046] The present invention provides a wind power network service flow control system, such as Figure 1 As shown, including:

[0047] Environmental acquisition module: collects and analyzes the specific environmental information of the target wind power equipment at each execution time, and obtains the environmental coefficient and operation monitoring value of the target wind power equipment at the corresponding execution time;

[0048] Information capturing module: acquiring the network service flow sent to the target wind power equipment, and capturing the execution information of the target wind power equipment at each execution time according to the network service flow;

[0049] Abnormal prediction module: It predicts abnormalities of environmental coefficients, operation monitoring values ​​and execution information at each execution moment, and feeds back business adjustment instructions to future execution moments to control the corresponding target wind power equipment to work continuously.

[0050] In this embodiment, the wind power equipment is a device for converting wind energy into electrical energy, and includes the following main components: a wind turbine, a generator, a tower, a control system, a converter, and a grid connection system.

[0051] In this embodiment, the environmental information includes basic environmental data around the wind power equipment, such as wind speed, wind direction, temperature, and humidity.

[0052] In this embodiment, the environmental coefficient refers to a parameter used to evaluate the performance and reliability of wind power equipment under specific environmental conditions, reflecting the degree of influence of the environment on the operation of wind power equipment and the adaptability of the equipment under different environmental conditions.

[0053] In this embodiment, the operation monitoring value refers to an indicator or value reflecting the operation status and performance of the wind power equipment, which is obtained based on the environmental information collected and analyzed by the environment acquisition module, and is used to evaluate the health status and operation efficiency of the wind power equipment.

[0054] In this embodiment, the network service flow refers to the propagation of various instructions, data and information transmitted through the network during the operation of the wind power equipment, including various communication and data exchange processes required for monitoring, controlling and managing the wind power equipment.

[0055] In this embodiment, the execution information refers to the specific operation and status information of the target wind power equipment at each execution time according to the received network service flow, including the parameter values ​​and current status of the equipment execution and other related information.

[0056] In this embodiment, the business adjustment instruction refers to an instruction sent to the target wind power equipment according to the prediction result and control requirements given by the abnormal prediction module, and is used to adjust the operation state, parameter setting or alarm processing of the equipment. For example, if the abnormal prediction module finds that the equipment may fail, the corresponding business adjustment instruction can be given to adjust the operation parameters of the equipment, thereby reducing the probability of failure.

[0057] The working principle and beneficial effect of the above technical solution are: the system collects environmental information, captures execution information, and uses the abnormal prediction module to monitor and predict the equipment, thereby realizing the control of the continuous working state of the equipment. By analyzing the environmental information and execution information, the accuracy of the judgment of the wind turbine at the current moment is improved, and the situation of unstable operation of wind power equipment is reduced.

[0058] Embodiment 2:

[0059] Based on the above embodiment 1, it also includes:

[0060] Tag setting module: setting a first electronic tag for each target wind power device, and at the same time, setting a second electronic tag for each environmental monitoring sensor device;

[0061] Mapping and matching module: maps and matches the first electronic tag with the second electronic tag, determines the environmental monitoring sensor device that monitors the same target wind power equipment, and realizes the collection of specific environmental information.

[0062] In this embodiment, the first electronic tag is an electronic identifier used to identify and track the target wind power equipment.

[0063] In this embodiment, the environmental monitoring sensor equipment is a device used to collect and monitor various environmental parameters in the wind farm, including various sensors and related data acquisition systems, such as wind speed, temperature and humidity, air pressure and other environmental parameters.

[0064] In this embodiment, the second electronic tag is an electronic identifier used to identify and track the environment monitoring sensor device.

[0065] In this embodiment, mapping and matching refers to a process of corresponding or matching a first electronic tag with another second electronic tag.

[0066] The working principle and beneficial effects of the above technical solution are: through the tag setting module and the mapping matching module, the target wind power equipment and the environmental monitoring sensor equipment can be accurately matched, thereby realizing the collection of specific environmental information, providing high-quality environmental data, and reducing the possibility of inaccurate data acquisition due to matching anomalies.

[0067] Embodiment 3:

[0068] Based on the above embodiment 1, the environment acquisition module includes:

[0069] Distribution unit: obtain the current distribution location of each target wind power equipment, the geographical environment of the current distribution location, and the equipment type, and construct a detection area based on the target wind power equipment;

[0070] Detection unit: Based on the positional relationship between the environmental monitoring sensor equipment and the detection area, the specific environmental information of each target wind power equipment at each execution moment is detected.

[0071] In this embodiment, the geographical environment refers to the specific geographical conditions where the target wind power equipment is located, including: topography, climate conditions, soil types, hydrological conditions and other specific geographical features.

[0072] In this embodiment, the equipment type refers to the type of wind power equipment, and is used to describe the specific characteristics and performance of the target wind power equipment.

[0073] In this embodiment, the detection area refers to an area within a specific area where monitoring and detection work on the status, performance, etc. of wind power equipment is performed.

[0074] In this embodiment, the position relationship refers to the relative position and spatial arrangement between the environmental monitoring sensor equipment and the target wind power equipment in the detection area.

[0075] The working principle and beneficial effect of the above technical solution are: through the environmental monitoring sensor equipment, the specific environmental information of each target wind power equipment at each execution time can be obtained in real time, including meteorological parameters, temperature and humidity, wind speed, etc., which reduces the data abnormality error caused by insensitivity to changes in the environment where the wind power equipment is located, and reduces the possibility of unreliable data.

[0076] Embodiment 4:

[0077] Based on the above embodiment 1, the environment acquisition module further includes:

[0078] A matrix construction unit: extracting wind parameters from the specific environmental information of each target wind power equipment detected, and constructing a wind parameter matrix for the target wind power equipment, wherein the rows of the wind parameter matrix contain all wind parameters involved in the same execution subframe, and the columns contain the same wind parameters involved in different execution subframes;

[0079] Matrix analysis unit: performing characteristic calculation on the wind parameter matrix to obtain a characteristic vector, and at the same time, performing a parameter comprehensive analysis of the corresponding execution subframe on each row in the wind parameter matrix to obtain a first coefficient, and performing a parameter trend analysis at the execution time on each column in the wind parameter matrix to obtain a second coefficient;

[0080] A coefficient determination unit: based on all the first coefficients and the second coefficients, determines an environment coefficient corresponding to the execution time;

[0081] Value determination unit: obtains the operation monitoring value matching the feature vector from the vector-value mapping table.

[0082] In this embodiment, the wind force parameters refer to parameters related to wind force extracted from environmental information, including wind speed, wind direction, temperature, and wind intensity environmental parameters.

[0083] In this embodiment, the wind parameter matrix is ​​a multidimensional matrix for storing and organizing the detected wind parameter data of the target wind power equipment. The rows of the matrix represent all wind parameters involved in the same execution subframe. Assuming that the current wind parameter matrix has 3 parameters and 2 execution subframes, the current wind parameter matrix is: ,in Indicates the first parameter of the first execution subframe, Indicates the first parameter of the first execution subframe, Indicates the first parameter of the first execution subframe.

[0084] In this embodiment, the execution subframe refers to a cycle of sensor collection and data processing for the target wind power equipment within a specific time period. During this cycle, the sensor will collect and record the environmental parameters around the target equipment.

[0085] In this embodiment, the vector-value mapping table is a mapping table that represents the relationship between the feature vector and the operation monitoring value matched.

[0086] In this embodiment, the first coefficient refers to the characteristic value calculated when performing comprehensive parameter analysis on each row in the wind parameter matrix, and the second coefficient refers to the characteristic value obtained by performing parameter trend analysis on each column in the wind parameter matrix at the execution time. The first coefficient is obtained by describing the changing trend of the wind parameters in different execution subframes, that is, after the wind parameter matrix is ​​normalized, the numerical values ​​and weights of the parameters in the same row are calculated, and the numerical values ​​of the parameters in the same column are accumulated and averaged, and the average value is calculated again with the fitting value obtained after the fitting analysis, and finally multiplied by the weight of the corresponding row parameter to obtain the second coefficient.

[0087] The environmental coefficient is obtained by adding the cumulative sum of all the first coefficients and the cumulative sum of all the second coefficients and dividing the result by the product of the number of rows and the number of columns of the wind parameter matrix.

[0088] The working principle and beneficial effect of the above technical solution are: by combining the matrix construction unit and the matrix analysis unit, the wind parameters of the target wind power equipment environmental information are extracted and analyzed. The environmental information provides a more comprehensive and intuitive data expression method, reduces the possibility of calculation errors in the data, and reduces the occurrence of invalid abnormal judgments.

[0089] Embodiment 5:

[0090] Based on the above embodiment 1, the information capturing module includes:

[0091] A service flow comparison unit: obtaining a network service flow sent to the target wind power equipment and a current service flow received by the target wind power equipment, and determining whether the current service flow is consistent with the network service flow;

[0092] Information capturing unit: if yes, capturing the first information of the target wind power equipment at each execution time according to the network service flow;

[0093] Otherwise, capturing the second information of the target wind power equipment at each execution moment according to the current business flow;

[0094] Information processing unit: pre-processes the final information obtained to obtain execution information.

[0095] In this embodiment, the current service flow is the network service flow currently received by the target wind power equipment, indicating the real-time network data flow being processed.

[0096] In this embodiment, the first information is a signal received by the target wind power equipment at each execution time according to the network service flow when the current service flow is consistent with the network service flow.

[0097] In this embodiment, the second information is the signal received by the target wind power equipment according to the network business flow at each execution time when the business flow is inconsistent with the network business flow, and the first information and the second information are essentially related operation information after the business flow is executed.

[0098] In this embodiment, preprocessing is to perform preliminary processing and screening on the acquired information to obtain more useful and effective execution information.

[0099] The working principle and beneficial effects of the above technical solution are: the method of comparing network business flows and capturing information improves the efficiency of wind power equipment monitoring and data processing, reduces analysis inefficiency, and reduces the possibility of unstable operation of wind power equipment.

[0100] Embodiment 6:

[0101] Based on the above embodiment 1, the information processing unit includes:

[0102] The first determination block: logically analyzes each sub-information in the last information obtained at each execution moment based on the logic analysis model, and spatially maps each sub-information based on the logic-space mapping table to determine the business logic and the logical space matching the business logic;

[0103] The second determination block: Count the number of business logics in the same logical space to determine the business importance and information density of the same logical space;

[0104] ; Where n1 represents the business logic statistics in the same logical space; Indicates the logical length of the i1th business logic in the same logical space; Indicates the total length of information contained in the same logical space; Represents the spatial weight of the same logical space; Indicates that based on all The variance of Indicates the logical weight of the i1th business logic in the same logical space; Indicates the total maximum weight when the same logical space is filled; It represents the total average weight of the same logical space when it is filled; Z1 represents the business importance of the same logical space; R1 represents the information density of the same logical space; It indicates the total number of planned logics when the same logic space is filled; lg indicates the logarithmic function symbol;

[0105] Filter block: Filter and retain sub-information from all sub-information at the same execution time according to business importance and information density to obtain execution information at the same execution time.

[0106] In this embodiment, the logic parsing model is a model for logical analysis and interpretation of natural language. The sentences in the text are parsed and converted into logical forms for logical reasoning and understanding. The results that can be fed back by each sub-information must have a certain logic, and the space for the logic is also pre-set.

[0107] In this embodiment, the sub-information is a component of the entire service flow, including execution information such as the current operating status of the equipment, wind speed and direction, temperature and humidity.

[0108] In this embodiment, logic parsing is a process of converting natural language text in execution information into a logical form.

[0109] In this embodiment, the logic-space mapping table is a mapping table that represents the relationship between logical information and logical space.

[0110] In this embodiment, space mapping refers to mapping the sub-information obtained by logical analysis into a corresponding logical space to facilitate further processing and analysis.

[0111] In this embodiment, the business logic expresses the business process of the wind power equipment based on the information obtained by logical analysis, and is a kind of logical relationship of description and expression.

[0112] In this embodiment, the logical space refers to a structured space for storing logical relationships.

[0113] In this embodiment, the business importance refers to the importance of the business logic in the current logic space, reflecting the importance of a business logic to the implementation of the current logic.

[0114] In this embodiment, it refers to the density of information related to the business logic in the current logical space.

[0115] In this embodiment, the logic length is the number of symbols or bits required to represent a logic.

[0116] In this embodiment, the space weight is a weight parameter of the business logic in the current logical space and is preset.

[0117] In the process of screening and retaining sub-information, the business importance is greater than the preset importance, and the information density is greater than the preset density as the benchmark conditions to obtain the information, and the preset importance and the preset density are both preset.

[0118] The working principle and beneficial effect of the above technical solution are as follows: based on the evaluation results of business importance and information density, sub-information with higher business importance and information density is screened and retained from all sub-information at the same execution time to obtain execution information at the same execution time. The screening and filtering process of execution information can be optimized to reduce the probability of invalid judgment anomalies.

[0119] Embodiment 7:

[0120] Based on the above embodiment 1, the abnormality prediction module includes:

[0121] Standard threshold determination unit: obtain the environmental coefficient at the corresponding execution time And running monitoring value Based on the preset standard threshold YH for each environmental parameter in the specific environmental information:

[0122] ;in, Represents the mean value of the standardized data of the specific environment information at the corresponding execution time; Represents the normalized value of the jth environmental parameter at the corresponding execution time; represents the variance of the standardized specific environment information at the corresponding execution time; 0.01 represents the error coefficient of the overall environment; n represents the number of environmental parameters involved in the specific environment information; represents the preset standard threshold value under the j-th environmental parameter at the corresponding execution time; Indicates based on running monitoring value The threshold adjustment coefficient of is in the range of (0.01, 0.02);

[0123] Operation monitoring abnormality judgment unit: extracts features from the execution information at the corresponding execution time, and analyzes the feature values ​​and preset standard thresholds to determine whether the corresponding target wind power equipment has an abnormality at the corresponding execution time;

[0124] ;in, Indicates all preset standard thresholds at the corresponding execution time The mean of ; m represents the total number of sub-information in the execution information at the corresponding execution time; represents the signal value of the kth sub-information at the corresponding execution time after being standardized according to the environmental parameters; represents the analysis value at the corresponding execution time; ln represents the logarithmic function sign;

[0125] If the analysis value is greater than the preset execution value, it is determined that there is no abnormality in the target wind power equipment at the corresponding execution time.

[0126] In this embodiment, the preset standard threshold is a preset threshold obtained according to the environmental coefficient and the operation monitoring value at a specific moment.

[0127] In this embodiment, the standardization process refers to converting specific environmental information at the execution time into data within a specific range.

[0128] In this embodiment, the error coefficient refers to an error constant that is fixed in calculating the preset standard threshold value, and is taken as 0.01 here.

[0129] In this embodiment, the threshold adjustment coefficient is a constant representing the threshold adjustment range that the operation will control, and the value range is (0.01, 0.02);

[0130] In this embodiment, feature extraction is to extract features that can reflect the state of the target wind power equipment from the execution information, and determine whether the equipment is abnormal based on these features.

[0131] In this embodiment, the preset execution value is generally 0.5.

[0132] In this embodiment, the characteristic value is a value obtained by extracting characteristics from the execution information.

[0133] The working principle and beneficial effect of the above technical solution are: extracting features from the execution information at the corresponding execution time, and analyzing the feature values ​​and preset standard thresholds to determine whether the corresponding target wind power equipment has an abnormality at the corresponding execution time. Multiple data processing methods are used to reduce the possibility of misjudgment by a single method.

[0134] Embodiment 8:

[0135] Based on the above embodiment 1, the abnormality prediction module further includes:

[0136] Analysis unit: statistics the analysis values ​​at different execution times, constructs an analysis curve, and performs periodic analysis on the analysis curve and analysis on the number of abnormal occurrences in the same period;

[0137] Prediction unit: According to the periodic analysis results and the analysis results of the number of abnormal occurrences, the high-frequency abnormal points in the future period are determined, and the future execution time consistent with the high-frequency abnormal points is locked to retrieve the normal execution parameters consistent with the high-frequency abnormal points and the abnormal adjustment parameters for abnormal conversion to normal from the analysis curve, and the future execution parameters of the future execution time are predicted and adjusted and fed back to control the corresponding target wind power equipment to work continuously.

[0138] In this embodiment, the analysis value is a parameter used in the standard threshold determination unit and the operation monitoring abnormality judgment unit, and describes the comprehensive evaluation value of the device status at the corresponding execution time.

[0139] In this embodiment, the analysis curve is a continuous curve drawn from analysis values ​​corresponding to multiple execution moments.

[0140] In this embodiment, the periodicity analysis is a process of analyzing the periodic characteristics in the analysis curve.

[0141] In this embodiment, the high-frequency abnormal point refers to an abnormal point that appears frequently in the analysis curve.

[0142] In this embodiment, the abnormal adjustment parameter refers to the parameter required to adjust the device from an abnormal state to a normal working state. For example, when the value of parameter 1 is a1, the execution state is abnormal. At this time, when the value of parameter 1 is a2, the execution state is normal. At this time, parameter 1 is the abnormal adjustment parameter.

[0143] In this embodiment, the future execution time refers to, for example, the general occurrence time of the high-frequency abnormal point is the time t2 in a cycle, so the future execution time will lock the time t2 in the future cycle.

[0144] In this embodiment, the purpose of prediction and adjustment feedback is to ensure that the target wind power equipment can continue to work normally at the future time t2.

[0145] The working principle and beneficial effects of the above technical solution are: by analyzing curves, periodicity analysis and abnormality occurrence frequency analysis, high-frequency abnormal points in future cycles are predicted, and by adjusting the feedback control equipment to continue working, the possibility of unstable operation of wind power equipment is reduced and the accuracy of wind power equipment analysis is improved.

[0146] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A wind power network service flow control system, characterized in that: include: Environmental acquisition module: collects and analyzes the specific environmental information of the target wind power equipment at each execution time, and obtains the environmental coefficient and operation monitoring value of the target wind power equipment at the corresponding execution time; Information capturing module: acquiring the network service flow sent to the target wind power equipment, and capturing the execution information of the target wind power equipment at each execution time according to the network service flow; Abnormal prediction module: It predicts abnormalities of environmental coefficients, operation monitoring values ​​and execution information at each execution moment, and feeds back business adjustment instructions to future execution moments to control the corresponding target wind power equipment to continue working; Wherein, the abnormal prediction module includes: Standard threshold determination unit: obtain the environmental coefficient at the corresponding execution time And running monitoring value Based on the preset standard threshold YH for each environmental parameter in the specific environmental information: ;in, Represents the mean value of the standardized data of the specific environment information at the corresponding execution time; Represents the normalized value of the jth environmental parameter at the corresponding execution time; represents the variance of the specific environment information at the corresponding execution time after normalization; 0.01 represents the error coefficient of the overall environment; n represents the number of environmental parameters involved in the specific environment information; represents the preset standard threshold value under the j-th environmental parameter at the corresponding execution time; Indicates based on running monitoring value The threshold adjustment coefficient is in the range of (0.01, 0.02); Operation monitoring abnormality judgment unit: extracts features from the execution information at the corresponding execution time, and analyzes the feature values ​​and preset standard thresholds to determine whether the corresponding target wind power equipment has an abnormality at the corresponding execution time; ;in, Indicates all preset standard thresholds at the corresponding execution time The mean of ; m represents the total number of sub-information in the execution information at the corresponding execution time; represents the signal value of the kth sub-information at the corresponding execution time after being standardized according to the environmental parameters; represents the analysis value at the corresponding execution time; ln represents the logarithmic function sign; If the analysis value is greater than the preset execution value, it is determined that there is no abnormality in the target wind power equipment at the corresponding execution time; Wherein, the information capturing module comprises: A service flow comparison unit: obtaining a network service flow sent to the target wind power equipment and a current service flow received by the target wind power equipment, and determining whether the current service flow is consistent with the network service flow; Information capturing unit: if yes, capturing the first information of the target wind power equipment at each execution time according to the network service flow; Otherwise, capturing the second information of the target wind power equipment at each execution moment according to the current business flow; Information processing unit: pre-processes the last acquired information to obtain execution information; Wherein, the information processing unit includes: The first determination block: logically analyzes each sub-information in the last information obtained at each execution moment based on the logic analysis model, and spatially maps each sub-information based on the logic-space mapping table to determine the business logic and the logical space matching the business logic; The second determination block: Count the number of business logics in the same logical space to determine the business importance and information density of the same logical space; ; Where n1 represents the business logic statistics in the same logical space; Indicates the logical length of the i1th business logic in the same logical space; Indicates the total length of information contained in the same logical space; Represents the spatial weight of the same logical space; Indicates that based on all The variance of Indicates the logical weight of the i1th business logic in the same logical space; Indicates the total maximum weight when the same logical space is filled; It represents the total average weight of the same logical space when it is filled; Z1 represents the business importance of the same logical space; R1 represents the information density of the same logical space; It indicates the total number of planned logics when the same logic space is filled; lg indicates the logarithmic function symbol; Filter block: Filter and retain sub-information from all sub-information at the same execution time according to business importance and information density to obtain execution information at the same execution time.

2. A wind power network service flow control system according to claim 1, characterized in that: Also includes: Tag setting module: setting a first electronic tag for each target wind power device, and at the same time, setting a second electronic tag for each environmental monitoring sensor device; Mapping and matching module: maps and matches the first electronic tag with the second electronic tag, determines the environmental monitoring sensor device that monitors the same target wind power equipment, and realizes the collection of specific environmental information.

3. A wind power network service flow control system according to claim 2, characterized in that: Environment acquisition module, including: Distribution unit: obtain the current distribution location of each target wind power equipment, the geographical environment of the current distribution location, and the equipment type, and construct a detection area based on the target wind power equipment; Detection unit: Based on the positional relationship between the environmental monitoring sensor equipment and the detection area, the specific environmental information of each target wind power equipment at each execution moment is detected.

4. A wind power network service flow control system according to claim 3, characterized in that: The environment acquisition module further includes: A matrix construction unit: extracting wind parameters from the specific environmental information of each target wind power equipment detected, and constructing a wind parameter matrix for the target wind power equipment, wherein the rows of the wind parameter matrix contain all wind parameters involved in the same execution subframe, and the columns contain the same wind parameters involved in different execution subframes; Matrix analysis unit: performing characteristic calculation on the wind parameter matrix to obtain a characteristic vector, and at the same time, performing a parameter comprehensive analysis of the corresponding execution subframe on each row in the wind parameter matrix to obtain a first coefficient, and performing a parameter trend analysis at the execution time on each column in the wind parameter matrix to obtain a second coefficient; A coefficient determination unit: based on all the first coefficients and the second coefficients, determines an environment coefficient corresponding to the execution time; Value determination unit: obtains the operation monitoring value matching the feature vector from the vector-value mapping table.

5. A wind power network service flow control system according to claim 1, characterized in that: The abnormality prediction module further includes: Analysis unit: statistics the analysis values ​​at different execution times, constructs an analysis curve, and performs periodic analysis on the analysis curve and analysis on the number of abnormal occurrences in the same period; Prediction unit: According to the periodic analysis results and the analysis results of the number of abnormal occurrences, the high-frequency abnormal points in the future period are determined, and the future execution time consistent with the high-frequency abnormal points is locked to retrieve the normal execution parameters consistent with the high-frequency abnormal points and the abnormal adjustment parameters for abnormal conversion to normal from the analysis curve, and the future execution parameters of the future execution time are predicted and adjusted and fed back to control the corresponding target wind power equipment to work continuously.

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