A wind turbine cabin smoke visualization detection system and method

Abstract

The invention relates to the technical field of data transmission, and discloses a smoke visualization detection system and method for a wind turbine generator cabin. When a video monitoring module determines that smoke exists in the wind turbine generator cabin, a smoke change instruction is issued; the smoke detection module collects smoke visualization detection data in multiple collection time intervals; the data analysis module analyzes the smoke visualization detection data, calculates a sub-smoke visualization detection data change coefficient and a smoke visualization detection data transmission coefficient; the environment analysis module determines historical transmission behavior and current data transmission environment data, and calculates a data transmission influence coefficient; the data transmission module obtains a target smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, adjusts a data transmission strategy, realizes complete transmission of smoke visualization detection data, adjusts a data transmission strategy, ensures the transmission speed of smoke visualization detection data, improves transmission stability and real-time performance, and improves the fire safety level of the wind turbine generator cabin.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a wind turbine generator cabin smoke visualization detection system and method. Background Art

[0002] A wind turbine nacelle usually refers to the nacelle cover of a wind turbine generator set, which is an important structure used to accommodate and protect key components. It is the protective shell of the wind turbine generator set, covering all components in the nacelle and isolating them from the outside. With the continuous development and innovation of technology, the performance and efficiency of wind turbine nacelles will continue to improve. Among the many factors that affect the safety of wind turbine generator sets, the monitoring and management of smoke in the nacelle is particularly critical, which is directly related to the safe operation of the unit and the prevention and control of potential fire risks.

[0003] In the prior art, the common method for transmitting smoke visualization detection data (such as key information such as smoke concentration and smoke temperature) uses a multi-hop routing protocol to determine the data transmission path of the smoke visualization detection data, and then uses a fixed data transmission strategy, such as a fixed transmission speed, to transmit the smoke visualization detection data. This method has certain limitations. Due to the limiting factors of the data transmission path itself, the smoke visualization detection data becomes unstable or even interrupted during high-speed transmission, and the prior art is unable to adaptively adjust the data transmission strategy, which further aggravates the phenomenon of data transmission delay and data loss, and directly affects the fire department's judgment and decision-making speed on the fire situation. Summary of the invention

[0004] The embodiment of the present invention provides a wind turbine cabin smoke visualization detection system and method. The present invention can realize the complete transmission of smoke visualization detection data, ensure the transmission speed of smoke visualization detection data, improve the transmission stability and real-time performance, and provide a reliable premise for the fire department to judge and make decisions on the fire situation.

[0005] In order to achieve the above object, the present invention provides a wind turbine cabin smoke visualization detection system, comprising:

[0006] A video monitoring module is used to perform real-time video monitoring of the wind turbine cabin, determine whether there is smoke in the wind turbine cabin, and issue a smoke change instruction when there is smoke in the wind turbine cabin;

[0007] A smoke detection module, for collecting smoke visualization detection data corresponding to a plurality of collection time intervals of the wind turbine generator cabin based on a preset smoke sensor and a temperature sensor when the smoke change instruction is detected, wherein the smoke visualization detection data includes smoke visualization concentration and smoke visualization temperature, and each collection time interval is provided with a plurality of sub-collection time nodes;

[0008] A data analysis module, used to analyze the smoke visualization detection data corresponding to each acquisition time interval, calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval, and calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the sub-smoke visualization detection data variation coefficient of each acquisition time interval;

[0009] An environment analysis module, used to determine the historical transmission behavior of a data transmission path, determine the current data transmission environment data of the data transmission path, and calculate the data transmission influence coefficient of the data transmission path according to the historical transmission behavior and the current data transmission environment data;

[0010] The data transmission module is used to adjust the smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, obtain the target smoke visualization detection data transmission coefficient, and adjust the data transmission strategy of the smoke visualization detection data based on the target smoke visualization detection data transmission coefficient.

[0011] Furthermore, the data analysis module is used to:

[0012] The data analysis module is used to extract the initial smoke visualization concentration corresponding to the initial sub-collection time node in the collection time interval;

[0013] The data analysis module is used to extract the final smoke visualization density corresponding to the final sub-collection time node in the collection time interval;

[0014] The data analysis module is used to calculate the smoke visualization concentration difference between the initial smoke visualization concentration and the final smoke visualization concentration;

[0015] The data analysis module is used to calculate the smoke visualization concentration factor of the acquisition time interval according to the initial smoke visualization concentration, the final smoke visualization concentration and the smoke visualization concentration difference;

[0016] The data analysis module is used to extract the initial smoke visualization temperature corresponding to the initial sub-collection time node in the collection time interval;

[0017] The data analysis module is used to extract the final smoke visualization temperature corresponding to the final sub-collection time node in the collection time interval;

[0018] The data analysis module is used to calculate the smoke visualization temperature difference between the initial smoke visualization temperature and the final smoke visualization temperature;

[0019] The data analysis module is used to calculate the smoke visualization temperature factor of the acquisition time interval according to the initial smoke visualization temperature, the final smoke visualization temperature and the smoke visualization temperature difference;

[0020] The data analysis module is used to calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval based on the smoke visualization concentration factor and the smoke visualization temperature factor.

[0021] Furthermore, the data analysis module is used to:

[0022] The data analysis module is used to calculate the smoke visualization concentration factor of the acquisition time interval according to the following formula:

[0023] ;

[0024] Among them, q1 is the smoke visualization concentration factor of the acquisition time interval, w1 is the initial smoke visualization concentration, w2 is the final smoke visualization concentration, w3 is the smoke visualization concentration difference, e1 is the calculation coefficient corresponding to the initial smoke visualization concentration, e2 is the calculation coefficient corresponding to the final smoke visualization concentration, e3 is the calculation coefficient corresponding to the smoke visualization concentration difference, e2>e3>e1>0, r is the number of sub-acquisition time nodes in the acquisition time interval, t1 is the mean of all remaining smoke visualization concentrations except the initial smoke visualization concentration, t2 is the mean of all remaining smoke visualization concentrations except the final smoke visualization concentration, and t3 is the mean of all smoke visualization concentrations.

[0025] Furthermore, the data analysis module is used to:

[0026] The data analysis module is used to calculate the variation coefficient of the sub-smoke visualization detection data in the acquisition time interval according to the following formula:

[0027] ;

[0028] Among them, y is the coefficient of variation of the sub-smoke visualization detection data in the acquisition time interval, u1 is the weight corresponding to the smoke visualization concentration factor, u2 is the weight corresponding to the smoke visualization temperature factor, and q2 is the smoke visualization temperature factor.

[0029] Furthermore, the data analysis module is used to:

[0030] The data analysis module is used to map all the sub-smoke visualization detection data variation coefficients into a point diagram to determine the sub-smoke visualization detection data variation coefficient point diagram;

[0031] The data analysis module is used to calibrate the same sub-smoke visualization detection data variation coefficient on the sub-smoke visualization detection data variation coefficient point diagram for the same points, and determine the first number of the same points;

[0032] The data analysis module is used to extract a sub-smoke visualization detection data variation coefficient from each identical point and calculate the corresponding first sub-smoke visualization detection data variation coefficient and value;

[0033] The data analysis module is used to calculate the coefficient mean of the variation coefficients of all sub-smoke visualization detection data, and delete all the same points that are less than the coefficient mean;

[0034] The data analysis module is used to count the remaining identical points to determine the number of second identical points;

[0035] The data analysis module is used to extract a sub-smoke visualization detection data variation coefficient from each remaining identical point, and calculate the corresponding second sub-smoke visualization detection data variation coefficient and value;

[0036] The data analysis module is used to calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the first number of similar points, the second number of similar points, the first sub-smoke visualization detection data variation coefficient and value, and the second sub-smoke visualization detection data variation coefficient and value.

[0037] Furthermore, the environmental analysis module is used to:

[0038] The environment analysis module is used to divide the historical transmission behavior into historical stable transmission behavior and historical abnormal transmission behavior;

[0039] The environment analysis module is used to analyze each historical abnormal transmission behavior and determine the corresponding historical abnormal environment data;

[0040] The environment analysis module is used to count the number of occurrences of historical abnormal environment data corresponding to each historical abnormal environment data;

[0041] The environment analysis module is used to sort the number of occurrences of all historical abnormal environment data from large to small, extract the number of occurrences of the first p historical abnormal environment data, and determine the corresponding historical abnormal environment data as the extracted historical abnormal environment data;

[0042] The environment analysis module is used to analyze each historical stable transmission behavior and determine the corresponding historical stable environment data;

[0043] The environment analysis module is used to count the number of occurrences of the historical stable environment data corresponding to the extracted historical abnormal environment data in all the historical stable environment data;

[0044] The environment analysis module is used to partition the extracted historical abnormal environment data according to the relationship between the extracted historical abnormal environment data and the historical preset environment data, and when the extracted historical abnormal environment data is smaller than the historical preset environment data, the corresponding extracted historical abnormal environment data is divided into a first data area;

[0045] The environment analysis module is used for dividing the corresponding extracted historical abnormal environment data into a second data area when the extracted historical abnormal environment data is greater than the historical preset environment data;

[0046] The environmental analysis module is used to calculate the environmental data difference between each extracted historical abnormal environmental data and the historical preset environmental data in the first data area, and obtain a first environmental data difference area;

[0047] The environmental analysis module is used to calculate the environmental data difference between each extracted historical abnormal environmental data and the historical preset environmental data in the second data area, and obtain a second environmental data difference area;

[0048] The environment analysis module is used to determine the current data transmission environment data corresponding to the extracted historical abnormal environment data from the current data transmission environment data, and use it as the current data transmission environment data to be processed;

[0049] The environment analysis module is used to bind the current data transmission environment data to be processed according to the relationship between the current data transmission environment data to be processed and the current preset data transmission environment data, and if the current data transmission environment data to be processed is smaller than the current preset data transmission environment data, then the current data transmission environment data to be processed is bound to the first environment data difference area;

[0050] The environment analysis module is used to bind the current data transmission environment data to be processed with the second environment data difference area if the current data transmission environment data to be processed is greater than the current preset data transmission environment data;

[0051] The environment analysis module is used to calculate the data transmission impact coefficient of the data transmission path based on the first environment data difference area, the second environment data difference area and the binding result.

[0052] Furthermore, the environmental analysis module is used to:

[0053] The environment analysis module is used to calculate the data transmission influence coefficient of the data transmission path according to the following formula:

[0054] ;

[0055] ;

[0056] ;

[0057] Where s is the data transmission influence coefficient of the data transmission path, a is the number of environmental data differences in the first environmental data difference area, and d1 i is the ith environmental data difference in the first environmental data difference area, g is the number of environmental data differences in the second environmental data difference area, d2 j is the jth environmental data difference in the second environmental data difference area, v1 is the number of current data transmission environmental data to be processed that is bound to the first environmental data difference area, c b k1 is the bth current data transmission environment data to be processed that is bound to the first environment data difference area, b k2 is the number of occurrences of historical abnormal environment data of the bth current data transmission environment data to be processed, b is the number of occurrences of the historical stable environment data of the bth current data transmission environment data to be processed, v2 is the number of current data transmission environment data to be processed bound to the second environment data difference area, m n f1 is the current data transmission environment data to be processed that is bound to the nth difference area of ​​the second environment data, n f2 is the number of occurrences of historical abnormal environment data of the nth current data transmission environment data to be processed, n It is the number of occurrences of the historical stable environment data of the nth current data transmission environment data to be processed.

[0058] Furthermore, the data transmission module is used for:

[0059] The data transmission module is used to set a plurality of preset data transmission influence coefficients;

[0060] The data transmission module is used to set a plurality of preset adjustment coefficients;

[0061] The data transmission module is used to select a corresponding preset adjustment coefficient according to the data transmission influence coefficient and the preset data transmission influence coefficient, adjust the smoke visualization detection data transmission coefficient, and obtain a target smoke visualization detection data transmission coefficient, wherein the target smoke visualization detection data transmission coefficient is the product of the smoke visualization detection data transmission coefficient and the preset adjustment coefficient, and the data transmission influence coefficient and the preset data transmission influence coefficient are inversely proportional.

[0062] Furthermore, the data transmission module is used for:

[0063] The data transmission module is used to determine the current data transmission speed of the data transmission path;

[0064] The data transmission module is used to set a plurality of preset target smoke visualization detection data transmission coefficients;

[0065] The data transmission module is used to set a plurality of preset adjustment coefficients;

[0066] The data transmission module is used to select a corresponding preset adjustment coefficient according to the target smoke visualization detection data transmission coefficient and the preset target smoke visualization detection data transmission coefficient, and calculate the product value of the current data transmission speed and the preset adjustment coefficient, wherein the target smoke visualization detection data transmission coefficient and the preset adjustment coefficient are in a positive proportional relationship.

[0067] In order to achieve the above object, the present invention also provides a wind turbine cabin smoke visualization detection method, comprising:

[0068] Performing real-time video monitoring of the wind turbine cabin to determine whether there is smoke in the wind turbine cabin, and issuing a smoke change instruction when smoke is present in the wind turbine cabin;

[0069] When the smoke change instruction is detected, smoke visualization detection data corresponding to multiple collection time intervals of the wind turbine generator cabin are collected based on the pre-set smoke sensor and temperature sensor, wherein the smoke visualization detection data includes smoke visualization concentration and smoke visualization temperature, and each collection time interval is provided with multiple sub-collection time nodes;

[0070] Analyze the smoke visualization detection data corresponding to each acquisition time interval, calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval, and calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the sub-smoke visualization detection data variation coefficient of each acquisition time interval;

[0071] Determine a historical transmission behavior of a data transmission path, determine current data transmission environment data of the data transmission path, and calculate a data transmission influence coefficient of the data transmission path according to the historical transmission behavior and the current data transmission environment data;

[0072] The smoke visualization detection data transmission coefficient is adjusted based on the data transmission influence coefficient to obtain a target smoke visualization detection data transmission coefficient, and the data transmission strategy of the smoke visualization detection data is adjusted based on the target smoke visualization detection data transmission coefficient.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention discloses a smoke visualization detection system and method for a wind turbine generator cabin. When a video monitoring module determines that smoke exists in the wind turbine generator cabin, a smoke change instruction is issued; the smoke detection module collects smoke visualization detection data in multiple collection time intervals; the data analysis module analyzes the smoke visualization detection data, calculates a sub-smoke visualization detection data change coefficient and a smoke visualization detection data transmission coefficient; the environment analysis module determines historical transmission behavior and current data transmission environment data, and calculates a data transmission influence coefficient; the data transmission module obtains a target smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, adjusts the data transmission strategy, realizes the complete transmission of the smoke visualization detection data, adjusts the data transmission strategy, ensures the transmission speed of the smoke visualization detection data, improves the transmission stability and real-time performance, and improves the fire safety level of the wind turbine generator cabin. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0076] Figure 1 A schematic diagram of the structure of a wind turbine cabin smoke visualization detection system according to an embodiment of the present invention is shown;

[0077] Figure 2 A schematic flow chart of a method for visually detecting smoke in a wind turbine cabin according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0078] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0079] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0080] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0081] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0082] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0083] like Figure 1 As shown, an embodiment of the present invention discloses a wind turbine cabin smoke visualization detection system, comprising:

[0084] A video monitoring module is used to perform real-time video monitoring of the wind turbine cabin, determine whether there is smoke in the wind turbine cabin, and issue a smoke change instruction when there is smoke in the wind turbine cabin;

[0085] A smoke detection module, for collecting smoke visualization detection data corresponding to a plurality of collection time intervals of the wind turbine generator cabin based on a preset smoke sensor and a temperature sensor when the smoke change instruction is detected, wherein the smoke visualization detection data includes smoke visualization concentration and smoke visualization temperature, and each collection time interval is provided with a plurality of sub-collection time nodes;

[0086] A data analysis module, used to analyze the smoke visualization detection data corresponding to each acquisition time interval, calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval, and calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the sub-smoke visualization detection data variation coefficient of each acquisition time interval;

[0087] An environment analysis module, used to determine the historical transmission behavior of a data transmission path, determine the current data transmission environment data of the data transmission path, and calculate the data transmission influence coefficient of the data transmission path according to the historical transmission behavior and the current data transmission environment data;

[0088] The data transmission module is used to adjust the smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, obtain the target smoke visualization detection data transmission coefficient, and adjust the data transmission strategy of the smoke visualization detection data based on the target smoke visualization detection data transmission coefficient.

[0089] In this embodiment, the video monitoring module is equipped with a high-resolution image sensor that can capture subtle smoke changes in the wind turbine cabin and the operating status of the equipment. For example, in the early stages of a fire, even a small amount of smoke can be clearly presented on the monitoring screen, providing accurate raw data for subsequent analysis. It supports multiple image format outputs, facilitates data processing and storage, and ensures compatibility and efficient transmission of image data between different systems and devices.

[0090] In this embodiment, the smoke sensor and the temperature sensor can collect the visualized smoke concentration and the visualized smoke temperature.

[0091] In this embodiment, the collection time intervals are pre-set, such as [1 second, 10 seconds], [11 seconds, 20 seconds], [21 seconds, 30 seconds], etc., which are not shown one by one here. In order to prevent the spread of fire, 5 collection time intervals are generally set, and each collection time interval is provided with a plurality of sub-collection time nodes, such as the above-mentioned [1 second, 10 seconds], then the corresponding sub-collection time nodes are [1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, 7 seconds, 8 seconds, 9 seconds, 10 seconds], and the remaining ones are not shown one by one, that is, each collection time interval can collect 10 groups of smoke visualization concentration and smoke visualization temperature.

[0092] The beneficial effects of the above technical scheme are: the present invention obtains the target smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, adjusts the data transmission strategy, realizes the complete transmission of the smoke visualization detection data, adjusts the data transmission strategy, ensures the transmission speed of the smoke visualization detection data, improves the transmission stability and real-time performance, and improves the fire safety level of the wind turbine cabin.

[0093] In some embodiments of the present application, the data analysis module is used to:

[0094] The data analysis module is used to extract the initial smoke visualization concentration corresponding to the initial sub-collection time node in the collection time interval;

[0095] The data analysis module is used to extract the final smoke visualization density corresponding to the final sub-collection time node in the collection time interval;

[0096] The data analysis module is used to calculate the smoke visualization concentration difference between the initial smoke visualization concentration and the final smoke visualization concentration;

[0097] The data analysis module is used to calculate the smoke visualization concentration factor of the acquisition time interval according to the initial smoke visualization concentration, the final smoke visualization concentration and the smoke visualization concentration difference;

[0098] The data analysis module is used to extract the initial smoke visualization temperature corresponding to the initial sub-collection time node in the collection time interval;

[0099] The data analysis module is used to extract the final smoke visualization temperature corresponding to the final sub-collection time node in the collection time interval;

[0100] The data analysis module is used to calculate the smoke visualization temperature difference between the initial smoke visualization temperature and the final smoke visualization temperature;

[0101] The data analysis module is used to calculate the smoke visualization temperature factor of the acquisition time interval according to the initial smoke visualization temperature, the final smoke visualization temperature and the smoke visualization temperature difference;

[0102] The data analysis module is used to calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval based on the smoke visualization concentration factor and the smoke visualization temperature factor.

[0103] In this embodiment, the initial sub-collection time node is the moment when a collection time interval starts, and the last sub-collection time node is the moment when a collection time interval ends. As mentioned above, the initial sub-collection time node is the 1st second, and the last sub-collection time node is the 10th second.

[0104] The beneficial effect of the above technical solution is: the present invention realizes the initial analysis of the smoke visualization detection data by calculating the smoke visualization concentration factor and the smoke visualization temperature factor, and calculates the sub-smoke visualization detection data variation coefficient of the acquisition time interval based on the smoke visualization concentration factor and the smoke visualization temperature factor. The smoke change situation in this acquisition time interval can be reflected by the sub-smoke visualization detection data variation coefficient, thereby avoiding the phenomenon that the smoke changes too quickly and the data is transmitted too slowly, affecting the fire situation judgment.

[0105] In some embodiments of the present application, the data analysis module is used to:

[0106] The data analysis module is used to calculate the smoke visualization concentration factor of the acquisition time interval according to the following formula:

[0107] ;

[0108] Among them, q1 is the smoke visualization concentration factor of the acquisition time interval, w1 is the initial smoke visualization concentration, w2 is the final smoke visualization concentration, w3 is the smoke visualization concentration difference, e1 is the calculation coefficient corresponding to the initial smoke visualization concentration, e2 is the calculation coefficient corresponding to the final smoke visualization concentration, e3 is the calculation coefficient corresponding to the smoke visualization concentration difference, e2>e3>e1>0, r is the number of sub-acquisition time nodes in the acquisition time interval, t1 is the mean of all remaining smoke visualization concentrations except the initial smoke visualization concentration, t2 is the mean of all remaining smoke visualization concentrations except the final smoke visualization concentration, and t3 is the mean of all smoke visualization concentrations.

[0109] In this embodiment, the calculation process of the smoke visualization concentration factor is described above, and the calculation process of the smoke visualization temperature factor will not be repeated here. The calculation process of the smoke visualization temperature factor is consistent with the above, and can be adaptively calculated according to the above formula.

[0110] In some embodiments of the present application, the data analysis module is used to:

[0111] The data analysis module is used to calculate the variation coefficient of the sub-smoke visualization detection data in the acquisition time interval according to the following formula:

[0112] ;

[0113] Among them, y is the coefficient of variation of the sub-smoke visualization detection data in the acquisition time interval, u1 is the weight corresponding to the smoke visualization concentration factor, u2 is the weight corresponding to the smoke visualization temperature factor, and q2 is the smoke visualization temperature factor.

[0114] In some embodiments of the present application, the data analysis module is used to:

[0115] The data analysis module is used to map all the sub-smoke visualization detection data variation coefficients into a point diagram to determine the sub-smoke visualization detection data variation coefficient point diagram;

[0116] The data analysis module is used to calibrate the same sub-smoke visualization detection data variation coefficient on the sub-smoke visualization detection data variation coefficient point diagram for the same points, and determine the first number of the same points;

[0117] The data analysis module is used to extract a sub-smoke visualization detection data variation coefficient from each identical point and calculate the corresponding first sub-smoke visualization detection data variation coefficient and value;

[0118] The data analysis module is used to calculate the coefficient mean of the variation coefficients of all sub-smoke visualization detection data, and delete all the same points that are less than the coefficient mean;

[0119] The data analysis module is used to count the remaining identical points to determine the number of second identical points;

[0120] The data analysis module is used to extract a sub-smoke visualization detection data variation coefficient from each remaining identical point, and calculate the corresponding second sub-smoke visualization detection data variation coefficient and value;

[0121] The data analysis module is used to calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the first number of similar points, the second number of similar points, the first sub-smoke visualization detection data variation coefficient and value, and the second sub-smoke visualization detection data variation coefficient and value.

[0122] In this embodiment, the point map is a point-based data visualization form, in which each data point is represented as an independent point, and the specific determination method will not be introduced in detail.

[0123] In this embodiment, each identical point may correspond to two identical sub-smoke visualization detection data variation coefficients, or may correspond to two or more sub-smoke visualization detection data variation coefficients, which can be calibrated in accordance with actual conditions.

[0124] In this embodiment, the smoke visualization detection data transmission coefficient of the wind turbine generator cabin is calculated according to the following formula:

[0125] ;

[0126] in, is the data transmission coefficient of smoke visualization detection in wind turbine cabin, is the number of first similar points, is the second number of similar points, is the coefficient and value of variation of the first sub-smoke visualization detection data, Visualize the detection data variation coefficient and value for the second sub-smoke.

[0127] The beneficial effect of the above technical solution is: the present invention calculates the smoke visualization detection data transmission coefficient of the wind turbine compartment according to the first number of similar points, the second number of similar points, the first sub-smoke visualization detection data change coefficient and value, and the second sub-smoke visualization detection data change coefficient and value, and uses the smoke visualization detection data transmission coefficient as the basis for adjusting the data transmission strategy. When the smoke visualization detection data transmission coefficient is larger, the data transmission speed is faster. Conversely, when the smoke visualization detection data transmission coefficient is smaller, the data transmission speed is slower.

[0128] In some embodiments of the present application, the environment analysis module is used to:

[0129] The environment analysis module is used to divide the historical transmission behavior into historical stable transmission behavior and historical abnormal transmission behavior;

[0130] The environment analysis module is used to analyze each historical abnormal transmission behavior and determine the corresponding historical abnormal environment data;

[0131] The environment analysis module is used to count the number of occurrences of historical abnormal environment data corresponding to each historical abnormal environment data;

[0132] The environment analysis module is used to sort the number of occurrences of all historical abnormal environment data from large to small, extract the number of occurrences of the first p historical abnormal environment data, and determine the corresponding historical abnormal environment data as the extracted historical abnormal environment data;

[0133] The environment analysis module is used to analyze each historical stable transmission behavior and determine the corresponding historical stable environment data;

[0134] The environment analysis module is used to count the number of occurrences of the historical stable environment data corresponding to the extracted historical abnormal environment data in all the historical stable environment data;

[0135] The environment analysis module is used to partition the extracted historical abnormal environment data according to the relationship between the extracted historical abnormal environment data and the historical preset environment data, and when the extracted historical abnormal environment data is smaller than the historical preset environment data, the corresponding extracted historical abnormal environment data is divided into a first data area;

[0136] The environment analysis module is used for dividing the corresponding extracted historical abnormal environment data into a second data area when the extracted historical abnormal environment data is greater than the historical preset environment data;

[0137] The environmental analysis module is used to calculate the environmental data difference between each extracted historical abnormal environmental data and the historical preset environmental data in the first data area, and obtain a first environmental data difference area;

[0138] The environmental analysis module is used to calculate the environmental data difference between each extracted historical abnormal environmental data and the historical preset environmental data in the second data area, and obtain a second environmental data difference area;

[0139] The environment analysis module is used to determine the current data transmission environment data corresponding to the extracted historical abnormal environment data from the current data transmission environment data, and use it as the current data transmission environment data to be processed;

[0140] The environment analysis module is used to bind the current data transmission environment data to be processed according to the relationship between the current data transmission environment data to be processed and the current preset data transmission environment data, and if the current data transmission environment data to be processed is smaller than the current preset data transmission environment data, then the current data transmission environment data to be processed is bound to the first environment data difference area;

[0141] The environment analysis module is used to bind the current data transmission environment data to be processed with the second environment data difference area if the current data transmission environment data to be processed is greater than the current preset data transmission environment data;

[0142] The environment analysis module is used to calculate the data transmission impact coefficient of the data transmission path based on the first environment data difference area, the second environment data difference area and the binding result.

[0143] In this embodiment, the historical transmission behavior refers to the historical data transmission record of the data transmission path, and the process from the start of data transmission to the end of data transmission is one historical transmission behavior.

[0144] In this embodiment, if data loss or data delay occurs during the data transmission process, it is regarded as a historical abnormal transmission behavior; if there is no data loss or data delay, it is regarded as a historical stable transmission behavior.

[0145] In this embodiment, each historical abnormal transmission behavior has a reason that leads to abnormal transmission, and this reason is the historical abnormal environment data, including network bandwidth, electromagnetic interference level, data transmission equipment temperature, network processing number per unit time, etc. For example, if the electromagnetic interference level is large, the temperature of the data transmission equipment is low or high.

[0146] In this embodiment, each historical abnormal transmission behavior corresponds to different historical abnormal environment data. Each time the historical abnormal environment data appears, the corresponding number of occurrences of the historical abnormal environment data increases by one, such as the network bandwidth appears 3 times, the electromagnetic interference degree appears 5 times, and so on.

[0147] In this embodiment, p is preferably 4.

[0148] In this embodiment, if the electromagnetic interference level mentioned above occurs 5 times, the electromagnetic interference level is used as the historical abnormal environment data for extraction.

[0149] In this embodiment, the historical stable transmission behavior also has corresponding historical stable environment data. For example, if the electromagnetic interference degree is used as the historical abnormal environment data to extract, the number of occurrences of the historical stable environment data of the electromagnetic interference degree, such as 4 times, is extracted from all the historical stable environment data.

[0150] In this embodiment, each extracted historical abnormal environment data corresponds to a historical preset environment data, and the historical preset environment data is used to determine whether the historical environment data is abnormal.

[0151] In this embodiment, as the electromagnetic interference degree mentioned above, the greater the electromagnetic interference degree, the greater the impact on data transmission, and the smaller the electromagnetic interference degree, the smaller the impact on data transmission. The electromagnetic interference degree must fall into the second data area, but for the temperature of the data transmission device, when the temperature of the data transmission device is lower or higher, it will affect the data transmission. Therefore, it is necessary to judge whether to divide the data transmission device temperature (extracting historical abnormal environment data) into the first data area or the second data area according to the actual situation. In summary, two division situations are given here, which can be judged in combination with the actual situation.

[0152] In this embodiment, the current data transmission environment data includes network bandwidth, electromagnetic interference level, data transmission equipment temperature, network processing number per unit time, etc.

[0153] In this embodiment, the current data transmission environment data to be processed corresponds to the above-mentioned extracted historical abnormal environment data, such as the degree of electromagnetic interference, the temperature of the data transmission equipment, etc.

[0154] In this embodiment, the current preset data transmission environment data corresponds one-to-one to the current data transmission environment data to be processed, and is used to determine whether the current data transmission environment data to be processed is abnormal.

[0155] In this embodiment, when the current data transmission environment data to be processed is equal to the current preset data transmission environment data, it does not participate in the calculation. At this time, the current data transmission environment data to be processed is normal.

[0156] The beneficial effect of the above technical solution is: the present invention calculates the data transmission influence coefficient of the data transmission path based on the first environmental data difference area, the second environmental data difference area and the binding result, and combines the historical transmission behavior with the current data transmission environment data. It can accurately solve the problem of instability or even interruption of smoke visualization detection data during high-speed transmission due to the limiting factors of the data transmission path itself, thereby ensuring efficient data transmission.

[0157] In some embodiments of the present application, the environment analysis module is used to:

[0158] The environment analysis module is used to calculate the data transmission influence coefficient of the data transmission path according to the following formula:

[0159] ;

[0160] ;

[0161] ;

[0162] Where s is the data transmission influence coefficient of the data transmission path, a is the number of environmental data differences in the first environmental data difference area, and d1 i is the ith environmental data difference in the first environmental data difference area, g is the number of environmental data differences in the second environmental data difference area, d2 j is the jth environmental data difference in the second environmental data difference area, v1 is the number of current data transmission environmental data to be processed that is bound to the first environmental data difference area, c b k1 is the bth current data transmission environment data to be processed that is bound to the first environment data difference area, b k2 is the number of occurrences of historical abnormal environment data of the bth current data transmission environment data to be processed, b is the number of occurrences of the historical stable environment data of the bth current data transmission environment data to be processed, v2 is the number of current data transmission environment data to be processed bound to the second environment data difference area, m n f1 is the current data transmission environment data to be processed that is bound to the nth difference area of ​​the second environment data, n f2 is the number of occurrences of historical abnormal environment data of the nth current data transmission environment data to be processed, n It is the number of occurrences of the historical stable environment data of the nth current data transmission environment data to be processed.

[0163] In this embodiment, as described above, when calculating the electromagnetic interference degree, since the electromagnetic interference degree must fall into the second data area, at this time, Recorded as 1, when calculating the temperature of the data transmission device, it falls into the first data area or the second data area. At this time, it can be calculated according to the actual situation. For example, when calculating the network bandwidth, the network bandwidth must fall into the first data area. Therefore, at this time, Recorded as 1, only in this special case, and It is recorded as 1, and the rest are calculated according to the actual situation.

[0164] In this embodiment, ,in, It means to sum all s1. It means to sum all s2. For example, if an s2 is calculated based on the electromagnetic interference level and an s2 is calculated based on the temperature of the data transmission device, then the two s2 are summed to get .

[0165] In some embodiments of the present application, the data transmission module is used to:

[0166] The data transmission module is used to set a plurality of preset data transmission influence coefficients;

[0167] The data transmission module is used to set a plurality of preset adjustment coefficients;

[0168] The data transmission module is used to select a corresponding preset adjustment coefficient according to the data transmission influence coefficient and the preset data transmission influence coefficient, adjust the smoke visualization detection data transmission coefficient, and obtain a target smoke visualization detection data transmission coefficient, wherein the target smoke visualization detection data transmission coefficient is the product of the smoke visualization detection data transmission coefficient and the preset adjustment coefficient, and the data transmission influence coefficient and the preset data transmission influence coefficient are inversely proportional.

[0169] In this embodiment, the first preset data transmission influence coefficient is set, and preferably is 12, and the second preset data transmission influence coefficient is set, and preferably is 16.

[0170] In this embodiment, the first preset adjustment coefficient is set, and is preferably 0.85, the second preset adjustment coefficient is set, and is preferably 0.9, and the third preset adjustment coefficient is set, and is preferably 0.95.

[0171] In this embodiment, when the data transmission influence coefficient is less than the first preset data transmission influence coefficient, the third preset adjustment coefficient is selected to adjust the smoke visualization detection data transmission coefficient; when the data transmission influence coefficient is greater than or equal to the first preset data transmission influence coefficient and less than the second preset data transmission influence coefficient, the second preset adjustment coefficient is selected to adjust the smoke visualization detection data transmission coefficient; when the data transmission influence coefficient is greater than or equal to the second preset data transmission influence coefficient, the first preset adjustment coefficient is selected to adjust the smoke visualization detection data transmission coefficient.

[0172] The beneficial effect of the above technical solution is: the present invention adjusts the smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, and obtains the target smoke visualization detection data transmission coefficient, which comprehensively considers the influence of the smoke visualization detection data and the data transmission path on the data transmission speed, and can not only ensure the data transmission efficiency, but also avoid the limitation of the data transmission path on data transmission, and avoid the problem of data loss or delayed transmission.

[0173] In some embodiments of the present application, the data transmission module is used to:

[0174] The data transmission module is used to determine the current data transmission speed of the data transmission path;

[0175] The data transmission module is used to set a plurality of preset target smoke visualization detection data transmission coefficients;

[0176] The data transmission module is used to set a plurality of preset adjustment coefficients;

[0177] The data transmission module is used to select a corresponding preset adjustment coefficient according to the target smoke visualization detection data transmission coefficient and the preset target smoke visualization detection data transmission coefficient, and calculate the product value of the current data transmission speed and the preset adjustment coefficient, wherein the target smoke visualization detection data transmission coefficient and the preset adjustment coefficient are in a positive proportional relationship.

[0178] In this embodiment, the product value of the current data transmission speed and the preset adjustment coefficient is used as the data transmission speed of the smoke visualization detection data.

[0179] In this embodiment, the first preset target smoke visualization detection data transmission coefficient is set, and preferably 4, and the second preset target smoke visualization detection data transmission coefficient is set, and preferably 7.

[0180] In this embodiment, the first preset adjustment coefficient is set, and is preferably 0.95, the second preset adjustment coefficient is set, and is preferably 1.05, and the third preset adjustment coefficient is set, and is preferably 1.15.

[0181] In this embodiment, when the target smoke visualization detection data transmission coefficient is less than the first preset target smoke visualization detection data transmission coefficient, the first preset adjustment coefficient is selected; when the target smoke visualization detection data transmission coefficient is greater than or equal to the first preset target smoke visualization detection data transmission coefficient and is less than the second preset target smoke visualization detection data transmission coefficient, the second preset adjustment coefficient is selected; when the target smoke visualization detection data transmission coefficient is greater than or equal to the second preset target smoke visualization detection data transmission coefficient, the third preset adjustment coefficient is selected.

[0182] The beneficial effect of the above technical solution is: the present invention adjusts the data transmission speed of the smoke visualization detection data based on the target smoke visualization detection data transmission coefficient, realizes the dynamic adjustment of the data transmission speed, avoids the singleness and limitation of the data transmission speed setting, can avoid data loss caused by too fast data transmission speed, and can also avoid data delay caused by too slow data transmission speed, thereby ensuring the real-time data transmission, providing a reliable premise for the fire department to judge and make decisions on the fire situation, providing convenient conditions for personnel evacuation and fire rescue, and ensuring that the fire protection system of the entire wind turbine cabin can respond quickly, work in coordination, and effectively deal with fire accidents.

[0183] In some embodiments of the present application, it also includes: a black box module for storing smoke visualization detection data, using high temperature resistant and fireproof storage media, and being able to store data for a long time in a high temperature fire environment. The recorded content includes video surveillance images, audio information (such as the sound of equipment operation in the cabin, alarm sound, etc.), smoke and temperature detection data, and operation logs of the data processing and control module, etc., within a period of time before the fire. It has data encryption and anti-tampering functions to ensure the authenticity and integrity of the recorded information, providing a strong basis for accident cause analysis, responsibility determination, and subsequent optimization and improvement of the fire protection system, filling the gap in the existing wind turbine cabin fire protection system in this regard.

[0184] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0185] Correspondingly, such as Figure 2 As shown, the present application also provides a wind turbine cabin smoke visualization detection method, comprising:

[0186] S110: Performing real-time video monitoring of the wind turbine cabin to determine whether there is smoke in the wind turbine cabin, and issuing a smoke change instruction when there is smoke in the wind turbine cabin;

[0187] S120: When the smoke change instruction is detected, smoke visualization detection data corresponding to multiple collection time intervals of the wind turbine generator cabin are collected based on the pre-set smoke sensor and temperature sensor, wherein the smoke visualization detection data includes smoke visualization concentration and smoke visualization temperature, and each collection time interval is set with multiple sub-collection time nodes;

[0188] S130: Analyze the smoke visualization detection data corresponding to each acquisition time interval, calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval, and calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the sub-smoke visualization detection data variation coefficient of each acquisition time interval;

[0189] S140: Determine a historical transmission behavior of a data transmission path, determine current data transmission environment data of the data transmission path, and calculate a data transmission influence coefficient of the data transmission path according to the historical transmission behavior and the current data transmission environment data;

[0190] S150: adjusting the smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, and obtaining a target smoke visualization detection data transmission coefficient, and adjusting the data transmission strategy of the smoke visualization detection data based on the target smoke visualization detection data transmission coefficient.

[0191] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0192] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0193] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wind turbine cabin smoke visualization detection system, characterized in that: include: A video monitoring module is used to perform real-time video monitoring of the wind turbine cabin, determine whether there is smoke in the wind turbine cabin, and issue a smoke change instruction when there is smoke in the wind turbine cabin; A smoke detection module, for collecting smoke visualization detection data corresponding to a plurality of collection time intervals of the wind turbine generator cabin based on a preset smoke sensor and a temperature sensor when the smoke change instruction is detected, wherein the smoke visualization detection data includes smoke visualization concentration and smoke visualization temperature, and each collection time interval is provided with a plurality of sub-collection time nodes; A data analysis module, used to analyze the smoke visualization detection data corresponding to each acquisition time interval, calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval, and calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the sub-smoke visualization detection data variation coefficient of each acquisition time interval; An environment analysis module, used to determine the historical transmission behavior of a data transmission path, determine the current data transmission environment data of the data transmission path, and calculate the data transmission influence coefficient of the data transmission path according to the historical transmission behavior and the current data transmission environment data; A data transmission module, used to adjust the smoke visualization detection data transmission coefficient based on the data transmission influence coefficient, and obtain a target smoke visualization detection data transmission coefficient, and adjust the data transmission strategy of the smoke visualization detection data based on the target smoke visualization detection data transmission coefficient; The data analysis module is used to map all the sub-smoke visualization detection data variation coefficients into a point diagram to determine the sub-smoke visualization detection data variation coefficient point diagram; The data analysis module is used to calibrate the same sub-smoke visualization detection data variation coefficient on the sub-smoke visualization detection data variation coefficient point diagram for the same points, and determine the first number of the same points; The data analysis module is used to extract a sub-smoke visualization detection data variation coefficient from each identical point and calculate the corresponding first sub-smoke visualization detection data variation coefficient and value; The data analysis module is used to calculate the coefficient mean of the variation coefficients of all sub-smoke visualization detection data, and delete all the same points that are less than the coefficient mean; The data analysis module is used to count the remaining identical points to determine the number of second identical points; The data analysis module is used to extract a sub-smoke visualization detection data variation coefficient from each remaining identical point, and calculate the corresponding second sub-smoke visualization detection data variation coefficient and value; The data analysis module is used to calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the first number of similar points, the second number of similar points, the first sub-smoke visualization detection data variation coefficient and value, and the second sub-smoke visualization detection data variation coefficient and value; The data transmission module is used to determine the current data transmission speed of the data transmission path; The data transmission module is used to set a plurality of preset target smoke visualization detection data transmission coefficients; The data transmission module is used to set a plurality of preset adjustment coefficients; The data transmission module is used to select a corresponding preset adjustment coefficient according to the target smoke visualization detection data transmission coefficient and the preset target smoke visualization detection data transmission coefficient, and calculate the product value of the current data transmission speed and the preset adjustment coefficient, wherein the target smoke visualization detection data transmission coefficient and the preset adjustment coefficient are in a positive proportional relationship.

2. The wind turbine cabin smoke visualization detection system according to claim 1, characterized in that: The data analysis module is used for: The data analysis module is used to extract the initial smoke visualization concentration corresponding to the initial sub-collection time node in the collection time interval; The data analysis module is used to extract the final smoke visualization density corresponding to the final sub-collection time node in the collection time interval; The data analysis module is used to calculate the smoke visualization concentration difference between the initial smoke visualization concentration and the final smoke visualization concentration; The data analysis module is used to calculate the smoke visualization concentration factor of the acquisition time interval according to the initial smoke visualization concentration, the final smoke visualization concentration and the smoke visualization concentration difference; The data analysis module is used to extract the initial smoke visualization temperature corresponding to the initial sub-collection time node in the collection time interval; The data analysis module is used to extract the final smoke visualization temperature corresponding to the final sub-collection time node in the collection time interval; The data analysis module is used to calculate the smoke visualization temperature difference between the initial smoke visualization temperature and the final smoke visualization temperature; The data analysis module is used to calculate the smoke visualization temperature factor of the acquisition time interval according to the initial smoke visualization temperature, the final smoke visualization temperature and the smoke visualization temperature difference; The data analysis module is used to calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval based on the smoke visualization concentration factor and the smoke visualization temperature factor.

3. The wind turbine cabin smoke visualization detection system according to claim 2 is characterized in that: The data analysis module is used for: The data analysis module is used to calculate the smoke visualization concentration factor of the acquisition time interval according to the following formula: ； Among them, q1 is the smoke visualization concentration factor of the acquisition time interval, w1 is the initial smoke visualization concentration, w2 is the final smoke visualization concentration, w3 is the smoke visualization concentration difference, e1 is the calculation coefficient corresponding to the initial smoke visualization concentration, e2 is the calculation coefficient corresponding to the final smoke visualization concentration, e3 is the calculation coefficient corresponding to the smoke visualization concentration difference, e2>e3>e1>0, r is the number of sub-acquisition time nodes in the acquisition time interval, t1 is the mean of all remaining smoke visualization concentrations except the initial smoke visualization concentration, t2 is the mean of all remaining smoke visualization concentrations except the final smoke visualization concentration, and t3 is the mean of all smoke visualization concentrations.

4. The wind turbine cabin smoke visualization detection system according to claim 3 is characterized in that: The data analysis module is used for: The data analysis module is used to calculate the variation coefficient of the sub-smoke visualization detection data in the acquisition time interval according to the following formula: ； Among them, y is the coefficient of variation of the sub-smoke visualization detection data in the acquisition time interval, u1 is the weight corresponding to the smoke visualization concentration factor, u2 is the weight corresponding to the smoke visualization temperature factor, and q2 is the smoke visualization temperature factor.

5. The wind turbine cabin smoke visualization detection system according to claim 1, characterized in that: The environmental analysis module is used to: The environment analysis module is used to divide the historical transmission behavior into historical stable transmission behavior and historical abnormal transmission behavior; The environment analysis module is used to analyze each historical abnormal transmission behavior and determine the corresponding historical abnormal environment data; The environment analysis module is used to count the number of occurrences of historical abnormal environment data corresponding to each historical abnormal environment data; The environment analysis module is used to sort the number of occurrences of all historical abnormal environment data from large to small, extract the number of occurrences of the first p historical abnormal environment data, and determine the corresponding historical abnormal environment data as the extracted historical abnormal environment data; The environment analysis module is used to analyze each historical stable transmission behavior and determine the corresponding historical stable environment data; The environment analysis module is used to count the number of occurrences of the historical stable environment data corresponding to the extracted historical abnormal environment data in all the historical stable environment data; The environment analysis module is used to partition the extracted historical abnormal environment data according to the relationship between the extracted historical abnormal environment data and the historical preset environment data, and when the extracted historical abnormal environment data is smaller than the historical preset environment data, the corresponding extracted historical abnormal environment data is divided into a first data area; The environment analysis module is used for dividing the corresponding extracted historical abnormal environment data into a second data area when the extracted historical abnormal environment data is greater than the historical preset environment data; The environmental analysis module is used to calculate the environmental data difference between each extracted historical abnormal environmental data and the historical preset environmental data in the first data area, and obtain a first environmental data difference area; The environmental analysis module is used to calculate the environmental data difference between each extracted historical abnormal environmental data and the historical preset environmental data in the second data area, and obtain a second environmental data difference area; The environment analysis module is used to determine the current data transmission environment data corresponding to the extracted historical abnormal environment data from the current data transmission environment data, and use it as the current data transmission environment data to be processed; The environment analysis module is used to bind the current data transmission environment data to be processed according to the relationship between the current data transmission environment data to be processed and the current preset data transmission environment data, and if the current data transmission environment data to be processed is smaller than the current preset data transmission environment data, then the current data transmission environment data to be processed is bound to the first environment data difference area; The environment analysis module is used to bind the current data transmission environment data to be processed with the second environment data difference area if the current data transmission environment data to be processed is greater than the current preset data transmission environment data; The environment analysis module is used to calculate the data transmission impact coefficient of the data transmission path based on the first environment data difference area, the second environment data difference area and the binding result.

6. The wind turbine cabin smoke visualization detection system according to claim 5, characterized in that: The environmental analysis module is used to: The environment analysis module is used to calculate the data transmission influence coefficient of the data transmission path according to the following formula: ； ； ； Where s is the data transmission influence coefficient of the data transmission path, a is the number of environmental data differences in the first environmental data difference area, and d1 i is the ith environmental data difference in the first environmental data difference area, g is the number of environmental data differences in the second environmental data difference area, d2 j is the jth environmental data difference in the second environmental data difference area, v1 is the number of current data transmission environmental data to be processed that is bound to the first environmental data difference area, c b k1 is the bth current data transmission environment data to be processed that is bound to the first environment data difference area, b k2 is the number of occurrences of historical abnormal environment data of the bth current data transmission environment data to be processed, b is the number of occurrences of the historical stable environment data of the bth current data transmission environment data to be processed, v2 is the number of current data transmission environment data to be processed bound to the second environment data difference area, m n f1 is the current data transmission environment data to be processed that is bound to the nth difference area of ​​the second environment data, n f2 is the number of occurrences of historical abnormal environment data of the nth current data transmission environment data to be processed, n It is the number of occurrences of the historical stable environment data of the nth current data transmission environment data to be processed.

7. The wind turbine cabin smoke visualization detection system according to claim 1, characterized in that: The data transmission module is used for: The data transmission module is used to set a plurality of preset data transmission influence coefficients; The data transmission module is used to set a plurality of preset adjustment coefficients; The data transmission module is used to select a corresponding preset adjustment coefficient according to the data transmission influence coefficient and the preset data transmission influence coefficient, adjust the smoke visualization detection data transmission coefficient, and obtain a target smoke visualization detection data transmission coefficient, wherein the target smoke visualization detection data transmission coefficient is the product of the smoke visualization detection data transmission coefficient and the preset adjustment coefficient, and the data transmission influence coefficient and the preset data transmission influence coefficient are inversely proportional.

8. A method for visual detection of smoke in a wind turbine generator cabin, applied to a visual detection system for smoke in a wind turbine generator cabin as claimed in any one of claims 1 to 7, characterized in that: include: Performing real-time video monitoring of the wind turbine cabin to determine whether there is smoke in the wind turbine cabin, and issuing a smoke change instruction when smoke is present in the wind turbine cabin; When the smoke change instruction is detected, smoke visualization detection data corresponding to multiple collection time intervals of the wind turbine generator cabin are collected based on the pre-set smoke sensor and temperature sensor, wherein the smoke visualization detection data includes smoke visualization concentration and smoke visualization temperature, and each collection time interval is provided with multiple sub-collection time nodes; Analyze the smoke visualization detection data corresponding to each acquisition time interval, calculate the sub-smoke visualization detection data variation coefficient of the acquisition time interval, and calculate the smoke visualization detection data transmission coefficient of the wind turbine cabin according to the sub-smoke visualization detection data variation coefficient of each acquisition time interval; Determine a historical transmission behavior of a data transmission path, determine current data transmission environment data of the data transmission path, and calculate a data transmission influence coefficient of the data transmission path according to the historical transmission behavior and the current data transmission environment data; The smoke visualization detection data transmission coefficient is adjusted based on the data transmission influence coefficient to obtain a target smoke visualization detection data transmission coefficient, and the data transmission strategy of the smoke visualization detection data is adjusted based on the target smoke visualization detection data transmission coefficient.

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