Smart grid data transmission method based on carrier modulation

Abstract

The present invention relates to the field of data transmission, and in particular to a smart grid data transmission method based on carrier modulation, comprising: periodically acquiring key equipment, and determining a node analysis method according to the category of each key equipment; determining a set of monitoring nodes to be analyzed in a corresponding dense area according to quality parameters; determining a key section analysis method according to the number of operating execution personnel and the proportion of execution sections; determining a transmission monitoring node set according to the quality parameters of associated monitoring nodes of each type II key equipment and an average line distance; under the condition that the set is confirmed to be completed, setting a matching control node corresponding to each transmission monitoring node set; under the condition that information is transmitted, periodically determining whether to update a transmission execution plan according to the proportion of fluctuating transmission lines. The present invention improves the validity of information obtained by users, and thereby improves the accuracy of judgment results based on the obtained data on abnormal causes.

Description

Technical Field

[0001] The present invention relates to the field of data transmission, and in particular to a smart grid data transmission method based on carrier modulation. Background Art

[0002] Real-time monitoring of the power grid is an important means to ensure stable power supply. Among them, the acquired monitoring video can assist technicians in determining the cause of the abnormality. However, the existing power grid data transmission method based on carrier modulation often fails to take into account the impact of the transmission content and the stability of the transmission process on the effectiveness of the information obtained by the user, resulting in poor accuracy in the user's judgment of the cause of the abnormality. Therefore, how to adjust the data transmission arrangement according to the abnormal conditions and transmission status of the actual management area to ensure the effectiveness of the information obtained by the user.

[0003] Chinese patent publication number CN116260484A discloses a factory power grid intelligent monitoring system based on power carrier, including a data analysis module, a site importance analysis module, an intelligent delineation module and an equipment analysis module. The site importance analysis module is used to analyze the importance of sites in the factory power grid, the equipment analysis module is used to analyze the equipment conditions of power generation equipment in sites in the factory power grid, the intelligent delineation module is used to intelligently delineate power generation equipment in sites in the factory power grid, and the data analysis module is used to analyze the working conditions of power generation equipment in sites in the factory power grid to generate a normal working signal or an abnormal working signal. However, the above scheme has the following problems: the arrangement of data transmission cannot be adjusted according to the abnormal conditions and transmission status of the actual management area, resulting in low effectiveness of the information obtained by the user, and then resulting in low accuracy of the abnormal cause judgment result. Summary of the invention

[0004] To this end, the present invention provides a smart grid data transmission method based on carrier modulation to overcome the problem that the prior art fails to adjust the data transmission arrangement according to the abnormal conditions and transmission status of the actual management area, resulting in low effectiveness of the information obtained by the user, and further resulting in low accuracy of the abnormal cause judgment result.

[0005] To achieve the above object, the present invention provides a smart grid data transmission method based on carrier modulation, comprising:

[0006] Periodically test the data of various devices in the target management area to obtain key devices, and determine the node analysis method based on the types of key devices in the target management area. Test the quality parameters of the associated monitoring nodes of each type of key equipment in each dense area or each type of key equipment in each type.

[0007] If there is a type of key equipment in the target management area, determine the key analysis equipment in the corresponding dense area according to the relevant influence coefficient of each type of key equipment in each dense area, and determine the set of monitoring nodes to be analyzed in each dense area according to the quality parameters;

[0008] Obtain the monitoring data to be analyzed, determine the key paragraph analysis method according to the number of operation execution personnel and the proportion of execution paragraphs to obtain the key paragraphs, and determine the transmission monitoring node and the transmission monitoring node set according to whether the key paragraph exists in each monitoring node to be analyzed;

[0009] The key paragraph analysis method includes detecting the overlap of data fluctuation cycles and abnormal behavior characteristics of each operator;

[0010] If there are Class II key devices in the target management area, the transmission monitoring nodes are determined according to the quality parameters of the associated monitoring nodes of each Class II key device, and the transmission monitoring node set is determined according to the average line distance;

[0011] When the set is confirmed to be complete, the matching control nodes corresponding to each transmission monitoring node set are set;

[0012] Under the information transmission condition, it is determined whether to update the transmission execution plan for the corresponding transmission monitoring node set based on the fluctuating transmission line proportion of each transmission monitoring node set periodically.

[0013] Furthermore, the data of various devices in the target management area are periodically tested to obtain key devices;

[0014] If there are key devices in the target management area, the node analysis method is determined according to whether there are Class I key devices or Class II key devices in the target management area. The node analysis method includes testing the relevant influence coefficients of each Class I key device in each dense area, and testing the quality parameters of the associated monitoring nodes of each Class II key device;

[0015] The key device is a target device having any device data whose value is not within the corresponding preset data range.

[0016] Further, the category of each key equipment is determined based on the number of related key equipment within the relevant scope of each key equipment;

[0017] If the number of related key devices within the relevant range of a key device is greater than the preset number of related key devices, the key distribution state of the key device is determined to be a dense state, and the key device is recorded as a first-class key device;

[0018] If the number of related key devices within the relevant range of a key device is less than or equal to the preset number of related key devices, the key distribution state of the key device is determined to be a discrete state, and the key device is recorded as a Class II key device;

[0019] If the correlation coefficient between the two key devices is greater than a preset correlation coefficient, the two key devices are mutually correlated key devices, and the correlation coefficient is determined according to the reference abnormal correlation frequency and the correlation influence coefficient between the key devices;

[0020] The relevant range is determined according to the monitoring range of the associated monitoring node corresponding to each key device.

[0021] Further, the relevant influence coefficient of each type of key equipment in each dense area is detected, and the key analysis equipment of each dense area is set according to the relevant influence coefficient of each type of key equipment, and the type of key equipment with a relevant influence coefficient greater than the preset relevant influence coefficient is recorded as the key analysis equipment of the corresponding dense area;

[0022] For any dense area, detect the quality parameters of each associated monitoring node of each key analysis device, and determine the set of monitoring nodes to be analyzed in the corresponding dense area according to the quality parameters;

[0023] The relevant influence coefficient is determined according to the relevant radiation degree of each type of key equipment and the abnormal moment correlation.

[0024] Further, the monitoring data to be analyzed of each set of monitoring nodes to be analyzed is obtained, and the key paragraph analysis method corresponding to each monitoring data to be analyzed is determined according to the number of operation execution personnel and the proportion of execution paragraphs in each monitoring data to be analyzed. The key paragraph analysis method is to detect the overlap of data fluctuation cycles or to detect abnormal behavior characteristics of each operation execution personnel;

[0025] The monitoring nodes to be analyzed with key sections are recorded as transmission monitoring nodes, and the transmission monitoring nodes in the same dense area are recorded as a transmission monitoring node set.

[0026] Furthermore, if the number of operation execution personnel in a monitoring data to be analyzed is greater than the preset number of execution personnel or the execution section ratio is greater than the preset execution section ratio, the data fluctuation cycle overlap of each operation execution section in the monitoring data to be analyzed is detected, and the key section is determined according to the data fluctuation cycle overlap, and the operation execution section whose data fluctuation cycle overlap is greater than the preset cycle overlap is recorded as the key section;

[0027] If the number of operating executors in a monitoring data to be analyzed is less than or equal to the preset number of executors and the proportion of execution sections is less than or equal to the preset proportion of execution sections, abnormal behavior feature detection is performed on each operating executor in the monitoring data to be analyzed, and the corresponding moment of each abnormal behavior feature is recorded as a key moment. The extraction duration of the corresponding extracted key section is determined according to the abnormal duration coefficient of each key moment, and the extraction duration is positively correlated with the abnormal duration coefficient of the corresponding key moment.

[0028] Further, the quality parameters of the associated monitoring nodes of each of the second-class key devices are detected, and the transmission monitoring node set is determined according to the quality parameters of each associated monitoring node and the average line distance between the transmission monitoring nodes;

[0029] The associated monitoring nodes whose quality parameters are greater than the preset quality parameters are recorded as transmission monitoring nodes, and the average line distance between the transmission monitoring nodes in any transmission monitoring node set is less than the preset average line distance;

[0030] Under the condition that the set is confirmed to be complete, the matching control nodes corresponding to each transmission monitoring node set are set according to the transmission busy coefficient of each control node in the target monitoring scene and the node interval distance;

[0031] The set confirmation completion condition is to complete the selection of the transmission monitoring node in the target monitoring scene and complete the setting of the transmission monitoring node set.

[0032] Furthermore, the information transmission condition is that each transmission monitoring node set and its matching transmission node in the target monitoring scene are set up;

[0033] A fluctuating transmission line is a transmission line whose stability coefficient is less than a preset stability coefficient.

[0034] Furthermore, for a single transmission monitoring node set, if the proportion of fluctuating transmission lines is greater than a preset proportion of fluctuating transmission lines, the transmission execution scheme for the transmission monitoring node set is updated, wherein:

[0035] Detecting the matching coefficient between each transmission monitoring node and other transmission monitoring nodes in the transmission monitoring node set;

[0036] The matching coefficient between any two transmission monitoring nodes is determined based on the reference stability coefficient and the line distance.

[0037] Further, the transmission execution scheme is updated according to the matching coefficient between the transmission monitoring nodes, and within the latest transmission monitoring cycle, the transmission execution scheme corresponding to the largest reference matching coefficient is selected to transmit the data information;

[0038] The reference matching coefficient is an average value of matching coefficients between transmission monitoring nodes that have a transmission relationship within the transmission execution scheme.

[0039] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical solution of the present invention, a targeted node analysis method is determined according to the category of key equipment to ensure the validity of the acquired video segments, and the transmission execution plan is periodically updated to ensure the stability of the data transmission process, thereby ensuring the integrity of the information obtained by the user. The present invention improves the accuracy of the judgment results on the causes of abnormalities based on the acquired data.

[0040] Furthermore, the present invention determines the node analysis method according to the category of each key equipment, so that the analysis method for key equipment in different distribution states is more in line with the actual situation, and the relevant influence coefficients are detected for key equipment in a dense state to further determine the key equipment for key monitoring, so as to avoid the poor data transmission efficiency and effectiveness of data transmission due to excessive amount of data to be transmitted. The present invention improves the effectiveness of information obtained by users.

[0041] Furthermore, the present invention extracts operation executors and operation execution sections in the monitoring data to be analyzed, and determines the key section analysis method according to the complexity coefficient of the executors and the proportion of the execution sections. When the number of operation executors is large or the proportion of the execution sections is large, the coincidence of the data fluctuation cycles of each operation execution section is detected, and the operation execution sections with a larger coincidence of the data fluctuation cycles are recorded as key sections to ensure that they have a higher probability of being associated with the abnormal causes of key equipment. The present invention improves the effectiveness of the transmitted information.

[0042] Furthermore, the stability coefficient of the carrier signal of each transmission line is detected periodically, and whether to update the transmission execution plan for the transmission monitoring node set is determined according to the proportion of fluctuating transmission lines of each transmission monitoring node set, so as to ensure the stability of the carrier signal during the transmission process when information is transmitted, avoid the loss of transmission content during the transmission process, and ensure the integrity of the transmitted data. The present invention improves the effectiveness of the information obtained by the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of a smart grid data transmission method based on carrier modulation according to the present invention;

[0044] Figure 2 A flow chart of determining the category of each key device according to the number of related key devices within the related range of each key device in the present invention;

[0045] Figure 3This is a flow chart of the present invention for determining the key analysis equipment according to the relevant influence coefficients of each type of key equipment;

[0046] Figure 4 The present invention is a flow chart of determining a key paragraph analysis method according to the complexity coefficient of the executors and the proportion of the executed paragraphs. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0049] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0050] In addition, it should be noted that in the description of the present invention, 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 those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] See also Figures 1 to 4 As shown, the present invention provides a smart grid data transmission method based on carrier modulation, comprising:

[0052] Periodically test the data of various devices in the target management area to obtain key devices, and determine the node analysis method based on the types of key devices in the target management area. Test the quality parameters of the associated monitoring nodes of each type of key equipment in each dense area or each type of key equipment in each type.

[0053] If there is a type of key equipment in the target management area, determine the key analysis equipment in the corresponding dense area according to the relevant influence coefficient of each type of key equipment in each dense area, and determine the set of monitoring nodes to be analyzed in each dense area according to the quality parameters;

[0054] Obtain the monitoring data to be analyzed, determine the key paragraph analysis method according to the number of operation execution personnel and the proportion of execution paragraphs to obtain the key paragraphs, and determine the transmission monitoring node and the transmission monitoring node set according to whether the key paragraph exists in each monitoring node to be analyzed;

[0055] The key paragraph analysis method includes detecting the overlap of data fluctuation cycles and abnormal behavior characteristics of each operator;

[0056] If there are Class II key devices in the target management area, the transmission monitoring nodes are determined according to the quality parameters of the associated monitoring nodes of each Class II key device, and the transmission monitoring node set is determined according to the average line distance;

[0057] When the set is confirmed to be complete, the matching control nodes corresponding to each transmission monitoring node set are set;

[0058] Under the information transmission condition, it is determined whether to update the transmission execution plan for the corresponding transmission monitoring node set based on the fluctuating transmission line proportion of each transmission monitoring node set periodically.

[0059] Among them, the target management area in the present invention is the area where the power equipment that needs to be monitored is located, and the electrical equipment that needs to be monitored is recorded as the target equipment. There are several monitoring nodes in the present invention for collecting monitoring videos for the target management area. Any monitoring node has at least one other monitoring node that can transmit data. The present invention adopts power carrier technology for data transmission, and transmits data information through the power lines originally provided in the target management area. The data information is the collected monitoring video. When transmitting data information, the data information that needs to be transmitted is encoded and converted into a digital signal, and the converted digital signal is modulated to adapt to power line transmission. The modulated digital signal is recorded as a carrier signal. The user performs signal processing steps such as demodulation on the received signal to restore the data information. The present invention does not limit the device model corresponding to the specific monitoring node and the model of the transmission line. The user can set it according to the actual working scenario. How to convert data information into a digital signal and how to modulate, demodulate and other signal processing processes are easy for technicians in this field to understand, so they will not be repeated here.

[0060] The present invention applies several historical monitoring records, and any one of the historical monitoring records records the values ​​of various equipment data in at least one monitoring record for the target management area, the duration of the overlap anomaly, the correlation coefficient, the correlation influence coefficient, the quality parameters, the number of operating execution personnel, the execution section ratio, the feature similarity, the average line distance and the fluctuation transmission line ratio, and each historical monitoring record corresponds to a qualified mark, which records whether the validity requirements of the monitoring results of each target device in the target management area meet the user needs.

[0061] Specifically, the data of various devices in the target management area are periodically tested to obtain key devices;

[0062] If there are key devices in the target management area, the node analysis method is determined according to whether there are Class I key devices or Class II key devices in the target management area. The node analysis method includes testing the relevant influence coefficients of each Class I key device in each dense area, and testing the quality parameters of the associated monitoring nodes of each Class II key device;

[0063] The key device is a target device having any device data whose value is not within the corresponding preset data range.

[0064] Among them, if there are Class I key equipment and Class II key equipment in the target management area, the relevant influence coefficients of each Class I key equipment in each dense area are detected, and the quality parameters of the associated monitoring nodes of each Class II key equipment are detected; if there is only Class I key equipment in the target management area, only the relevant influence coefficients of each Class I key equipment in each dense area are detected; if there is only Class II key equipment in the target management area, only the quality parameters of the associated monitoring nodes of each Class II key equipment are detected;

[0065] The present invention adopts a cyclic equipment detection cycle, and the duration of the equipment detection cycle can be determined by the user. The higher the user's requirements for the validity of the monitoring results of each target equipment in the target management area, the shorter the duration of the equipment detection cycle. A duration of an equipment detection cycle is provided, and the equipment detection cycle is 10 minutes. At the end of each equipment detection cycle, various equipment data in the target management area are detected; the equipment data in the present invention includes but is not limited to: voltage, power and current. Any equipment data in the present invention is set with a corresponding preset data range. The user can set the maximum and minimum values ​​of the preset data range according to the actual working scene and historical monitoring records. For any equipment data, the maximum value of the equipment data in the historical monitoring records that meets the user's requirements for the equipment operation status is the maximum value of the preset data range, and the minimum value of the equipment data in the historical monitoring records that meets the user's requirements for the equipment operation status is the minimum value of the preset data range.

[0066] Specifically, the category of each key equipment is determined according to the number of related key equipment within the relevant scope of each key equipment;

[0067] If the number of related key devices within the relevant range of a key device is greater than the preset number of related key devices, the key distribution state of the key device is determined to be a dense state, and the key device is recorded as a first-class key device;

[0068] If the number of related key devices within the relevant range of a key device is less than or equal to the preset number of related key devices, the key distribution state of the key device is determined to be a discrete state, and the key device is recorded as a Class II key device;

[0069] If the correlation coefficient between the two key devices is greater than a preset correlation coefficient, the two key devices are mutually correlated key devices, and the correlation coefficient is determined according to the reference abnormal correlation frequency and the correlation influence coefficient between the key devices;

[0070] The relevant range is determined according to the monitoring range of the associated monitoring node corresponding to each key device.

[0071] Among them, the categories of key equipment include Class I key equipment and Class II key equipment, the key distribution states include dense states and discrete states, the correlation coefficient between any two key equipment is the product of the reference abnormal correlation frequency corresponding to the two key equipment and the correlation influence coefficient, the reference abnormal correlation frequency = the number of related detection cycles of the two key equipment in the historical monitoring records / the number of key cycles in the historical monitoring records, if in an equipment detection cycle, both target equipment are recorded as key equipment, then the equipment detection cycle is recorded as the related detection cycle of the two key equipment, the number of key cycles is the number of equipment detection cycles in which any one of the target equipment corresponding to the two key equipment is recorded as a key equipment, the correlation influence coefficient is positively correlated with the duration of the coincidence anomaly of the two key equipment in the historical monitoring records, and the duration of the coincidence anomaly is the maximum value of the duration of the two target equipment being key equipment at the same time in each historical monitoring record;

[0072] The values ​​of the preset correlation coefficient and the preset number of related key devices can be determined by the user according to the actual working scenario. For example, the user can set them according to historical monitoring records. A method for setting the value of the preset correlation coefficient is provided, and the average value of the correlation coefficient between the related key devices in the historical monitoring records that meet the user's requirements for the validity of the monitoring results of each target device in the target management area is recorded as the number of related key devices. For a single key device, the related range is the set of monitoring ranges of the associated monitoring nodes of the key device, the associated monitoring nodes are the monitoring nodes that can photograph the key device, and the monitoring range is the area that can be illuminated by the monitoring nodes in the target management area.

[0073] Specifically, the relevant influence coefficient of each type of key equipment in each dense area is detected, and the key analysis equipment in each dense area is set according to the relevant influence coefficient of each type of key equipment, and the type of key equipment with a relevant influence coefficient greater than the preset relevant influence coefficient is recorded as the key analysis equipment of the corresponding dense area;

[0074] For any dense area, detect the quality parameters of each associated monitoring node of each key analysis device, and determine the set of monitoring nodes to be analyzed in the corresponding dense area according to the quality parameters;

[0075] The relevant influence coefficient is determined according to the relevant radiation degree of each type of key equipment and the abnormal moment correlation.

[0076] Among them, a type of key equipment with a correlation influence coefficient less than or equal to a preset correlation influence coefficient is recorded as a conventional analysis equipment of the corresponding dense area, and is determined for the dense area in the target management area. For a single dense area, any type of key equipment in the dense area is at least present in the correlation range of another type of key equipment. For a single type of key equipment, the correlation influence coefficient is the natural logarithm of the product of the correlation radiation and the abnormal moment correlation. The correlation radiation is the number of key equipment with which the type of key equipment is associated in the dense area where the type of key equipment is located. If the two key equipment are related key equipment to each other or one of the two key equipment is present in the correlation range of the related key equipment of the other key equipment, it is determined that there is an association between the two key equipments. The abnormal moment correlation is the average of the interval time between the abnormal moment of the type of key equipment and the abnormal moment of the related key equipment in the dense area where the type of key equipment is located. The abnormal moment is the starting moment of the device detection cycle when the target device is first set as a key device in the preset evaluation cycle before the current moment. The duration of the preset evaluation cycle can be determined by the user according to the actual working scenario. A preset evaluation cycle duration is provided, and the preset evaluation cycle duration is 10 times the duration of the device detection cycle.

[0077] The value of the preset relevant influence coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the historical monitoring records. The higher the user's requirement for the validity of the monitoring results of each target device in the target management area, the greater the value of the preset relevant influence coefficient. The average value of the relevant influence coefficient of each key analysis device in the historical monitoring records of the user's requirement for the validity of the monitoring results of each target device in the target management area is recorded as the preset relevant influence coefficient;

[0078] An associated monitoring node of any key analysis device, the quality parameter of the associated monitoring node is determined according to the resolution of the video collected for the corresponding key analysis device and the monitoring interval distance between the associated monitoring node and the corresponding key analysis device, the quality parameter = ln (resolution of the collected video / monitoring interval distance between the associated monitoring node and the corresponding key analysis device), the monitoring interval distance is the interval distance between the location of the associated monitoring node and the location of the monitored key analysis device, for any key analysis device, the associated monitoring node with a quality parameter greater than a preset quality parameter is recorded as a monitoring node to be analyzed, the value of the preset quality parameter can be determined by the user according to the actual working scenario, for example, the user can set it according to historical monitoring records, the higher the user's requirement for the validity of the monitoring results of each target device in the target management area, the larger the value of the preset quality parameter, a method for setting the value of the preset quality parameter is provided, the average value of the quality parameters of each monitoring node to be analyzed in the historical monitoring records that meet the user's requirement for the validity of the monitoring results of each target device in the target management area is recorded as the preset quality parameter, for a single dense area, all monitoring nodes to be analyzed in the dense area are recorded as the set of monitoring nodes to be analyzed in the dense area.

[0079] Specifically, the monitoring data to be analyzed of each set of monitoring nodes to be analyzed is obtained, and the key paragraph analysis method corresponding to each monitoring data to be analyzed is determined according to the number of operation execution personnel and the proportion of execution paragraphs in each monitoring data to be analyzed. The key paragraph analysis method is to detect the overlap of data fluctuation cycles or to detect abnormal behavior characteristics of each operation execution personnel;

[0080] The monitoring nodes to be analyzed with key sections are recorded as transmission monitoring nodes, and the transmission monitoring nodes in the same dense area are recorded as a transmission monitoring node set.

[0081] Among them, the monitoring data to be analyzed is a set of monitoring videos obtained from each set of monitoring nodes to be analyzed within a preset evaluation period before the current moment, the operation execution personnel are the personnel who perform debugging operations on the key analysis equipment, the execution segment ratio = the sum of the duration of each operation execution segment in the monitoring data to be analyzed / the duration of the monitoring data to be analyzed, and the operation execution segment is the video segment corresponding to the debugging operation performed by the operation execution personnel on the key analysis equipment.

[0082] Specifically, if the number of operation execution personnel in a monitoring data to be analyzed is greater than the preset number of execution personnel or the execution section ratio is greater than the preset execution section ratio, the data fluctuation cycle overlap of each operation execution section in the monitoring data to be analyzed is detected, and the key section is determined according to the data fluctuation cycle overlap, and the operation execution section whose data fluctuation cycle overlap is greater than the preset cycle overlap is recorded as the key section;

[0083] If the number of operating executors in a monitoring data to be analyzed is less than or equal to the preset number of executors and the proportion of execution sections is less than or equal to the preset proportion of execution sections, abnormal behavior feature detection is performed on each operating executor in the monitoring data to be analyzed, and the corresponding moment of each abnormal behavior feature is recorded as a key moment. The extraction duration of the corresponding extracted key section is determined according to the abnormal duration coefficient of each key moment, and the extraction duration is positively correlated with the abnormal duration coefficient of the corresponding key moment.

[0084] Among them, the values ​​of the preset number of execution personnel and the preset execution section ratio can be determined by the user according to the actual work scenario. For example, the user can set it according to the historical monitoring records, and a method for determining the value of the preset number of execution personnel is provided, and the historical monitoring records for detecting the overlap of the data fluctuation cycles of each operation execution section are recorded as key reference records, and the minimum value of the number of operation execution personnel in the key reference records that meet the user's requirements for the validity of the monitoring results of each target device in the target management area is recorded as the preset number of execution personnel, and a method for determining the preset execution section ratio is provided, and the minimum value of the execution section ratio in the key reference records that meet the user's requirements for the validity of the monitoring results of each target device in the target management area is recorded as the preset execution section ratio;

[0085] If the number of operation execution personnel is greater than the preset number of execution personnel or the execution section ratio is greater than the preset execution section ratio, the data fluctuation cycle overlap of each operation execution section is detected. For a single operation execution section, the data fluctuation cycle overlap is negatively correlated with the start interval duration. The start interval duration is the interval duration between the start time of the operation execution section and the start time of the device detection cycle at the current time. The operation execution section with a data fluctuation cycle overlap greater than the preset cycle overlap is recorded as a key section;

[0086] If the number of operators is less than or equal to the preset number of operators and the execution section ratio is less than or equal to the preset execution section ratio, abnormal behavior feature detection is performed for each operator in the monitoring data to be analyzed, and the behavior feature whose feature similarity with the dangerous behavior feature is greater than the preset feature similarity is recorded as an abnormal behavior feature. The user can set the dangerous behavior feature according to the actual work scenario. The value of the preset feature similarity can be determined by the user according to the actual work scenario. For example, the user can set it according to the historical monitoring records, and a method for determining the value of the preset number of operators is provided. The minimum value of the feature similarity of each abnormal behavior feature in the historical monitoring records that meets the user's requirements for the validity of the monitoring results of each target device in the target management area is recorded as the preset feature similarity; the moment when the abnormal behavior feature is detected in the monitoring data to be analyzed is recorded as the key moment. Each abnormal behavior feature corresponds to an operator and a key device. How to set the dangerous behavior feature, how to extract the behavior feature of the operator for the video, and how to determine the feature similarity between the behavior feature and the dangerous behavior feature are contents that are easy to understand for technicians in this field, and are not limited to the specific execution process and will not be repeated here.

[0087] The number of device detection cycles in which abnormalities exist in the monitoring data to be analyzed for the key equipment corresponding to each key moment is detected. For any target equipment, if the target equipment is recorded as a key equipment in a device detection cycle, then the device detection cycle has an abnormality. For any key moment, the abnormality persistence coefficient is positively correlated with the number of device detection cycles in which abnormalities exist for the key equipment corresponding to the key moment. The extraction time of the extracted key paragraph is positively correlated with the abnormality persistence coefficient of the key moment. The key moment is the middle moment of the extracted key paragraph. For a dense area, the dense area transmits the determined key paragraph to the user.

[0088] Specifically, the quality parameters of the associated monitoring nodes of each of the second-class key devices are detected, and the transmission monitoring node set is determined according to the quality parameters of each associated monitoring node and the average line distance between the transmission monitoring nodes;

[0089] The associated monitoring nodes whose quality parameters are greater than the preset quality parameters are recorded as transmission monitoring nodes, and the average line distance between the transmission monitoring nodes in any transmission monitoring node set is less than the preset average line distance;

[0090] Under the condition that the set is confirmed to be complete, the matching control nodes corresponding to each transmission monitoring node set are set according to the transmission busy coefficient of each control node in the target monitoring scene and the node interval distance;

[0091] The set confirmation completion condition is to complete the selection of the transmission monitoring node in the target monitoring scene and complete the setting of the transmission monitoring node set.

[0092] Among them, for any Class II key equipment, the associated monitoring nodes whose quality parameters are greater than the preset quality parameters are recorded as transmission monitoring nodes, and a transmission monitoring node set is set according to the average line distance between the transmission monitoring nodes. The average line distance is the average value of the lengths of the transmission lines between the transmission monitoring nodes with transmission lines. The average line distance between the transmission monitoring nodes in any transmission monitoring node set is less than the preset average line distance. The value of the preset average line distance can be determined by the user according to the actual working scenario. For example, the user can set it according to historical monitoring records. The higher the user's requirements for the validity of the monitoring results of each target device in the target management area, the smaller the value of the preset average line distance is, so as to avoid affecting the transmission effect due to the excessively long transmission line. A method for setting the preset average line distance is provided, and the maximum value of the average line distance between the transmission monitoring nodes in each transmission monitoring node set in the historical monitoring records that meet the user's requirements for the validity of the monitoring results of each target device in the target management area is recorded as the preset average transmission distance; for each Class II key equipment, all the monitoring data acquired by it is transmitted to the user.

[0093] The control node is used to send data to be transmitted to the user, and the data to be transmitted is a video segment that needs to be sent to the user. For any control node, the transmission busy coefficient is positively correlated with the amount of data to be transmitted that remains untransmitted at the current moment. The node interval distance is the minimum value of the transmission line length between the control node and each transmission monitoring node in the transmission monitoring node set. The matching control node of the transmission monitoring node set is a control node with the largest matching reference value, and the matching reference value = 1 / (transmission busy coefficient + node interval distance).

[0094] Specifically, the information transmission condition is that all transmission monitoring node sets and their matching transmission nodes in the target monitoring scene are set;

[0095] A fluctuating transmission line is a transmission line whose stability coefficient is less than a preset stability coefficient.

[0096] Among them, the stability coefficient = ln (carrier signal distribution coefficient / transmission attenuation coefficient), the carrier signal distribution coefficient = the sum of two unequal stage carrier values ​​obtained randomly / the average value of the two stage carrier values, the transmission attenuation coefficient is the product of the average attenuation intensity of the power carrier communication signal and the attenuation radius of the transmission signal, the stage carrier value is the frequency of the carrier signal in the transmission line at a random moment, the average attenuation intensity refers to the average value of the difference between the signal strength of the carrier signal of the transmission line detected in each historical monitoring record and the transmission time, how to obtain the stage carrier value, the average attenuation intensity and the transmission signal attenuation radius is easy to understand for technicians in this field and will not be elaborated here, the proportion of fluctuating transmission lines = the number of fluctuating transmission lines in the transmission lines of each transmission monitoring node set / the number of transmission lines of each transmission monitoring node set.

[0097] Specifically, for a single transmission monitoring node set, if the proportion of fluctuating transmission lines is greater than the preset proportion of fluctuating transmission lines, the transmission execution plan for the transmission monitoring node set is updated, wherein:

[0098] Detecting the matching coefficient between each transmission monitoring node and other transmission monitoring nodes in the transmission monitoring node set;

[0099] The matching coefficient between any two transmission monitoring nodes is determined based on the reference stability coefficient and the line distance.

[0100] Among them, the value of the preset fluctuating transmission line ratio can be determined by the user according to the actual working scenario. For example, the user can set it according to the historical monitoring records. The higher the user's requirement on the validity of the monitoring results of each target device in the target management area, the smaller the value of the preset fluctuating transmission line ratio is. A method for determining the value of the preset fluctuating transmission line ratio is provided, and the maximum value of the fluctuating transmission line ratio for which the transmission execution plan has not been updated in the historical monitoring records that meet the user's requirement on the validity of the monitoring results of each target device in the target management area is recorded as the preset fluctuating transmission ratio; the reference stability coefficient between two transmission monitoring nodes is the average value of the stability coefficients of the transmission line between the two transmission monitoring nodes in the historical monitoring records, the line distance between the two transmission monitoring nodes is the length of the transmission line between the two transmission monitoring nodes, and the matching coefficient between the two transmission monitoring nodes = ln (reference stability coefficient between the two transmission monitoring nodes / line distance between the two transmission monitoring nodes).

[0101] Specifically, the transmission execution scheme is updated according to the matching coefficient between the transmission monitoring nodes, and within the latest transmission monitoring cycle, the transmission execution scheme corresponding to the largest reference matching coefficient is selected to transmit data information;

[0102] The reference matching coefficient is an average value of matching coefficients between transmission monitoring nodes that have a transmission relationship within the transmission execution scheme.

[0103] The latest transmission monitoring period is the transmission monitoring period with the shortest interval between the starting time after the current time and the current time.

[0104] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. 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 smart grid data transmission method based on carrier modulation, characterized in that: include: Periodically test the data of various devices in the target management area to obtain key devices, and determine the node analysis method based on the types of key devices in the target management area. Test the quality parameters of the associated monitoring nodes of each type of key equipment in each dense area or each type of key equipment in each type. The category of each key equipment is determined based on the number of related key equipment within the relevant scope of each key equipment; If the number of related key devices within the relevant range of a key device is greater than the preset number of related key devices, the key distribution state of the key device is determined to be a dense state, and the key device is recorded as a first-class key device; If the number of related key devices within the relevant range of a key device is less than or equal to the preset number of related key devices, the key distribution state of the key device is determined to be a discrete state, and the key device is recorded as a Class II key device; If there is a type of key equipment in the target management area, determine the key analysis equipment in the corresponding dense area according to the relevant influence coefficient of each type of key equipment in each dense area, and determine the set of monitoring nodes to be analyzed in each dense area according to the quality parameters; Obtain the monitoring data to be analyzed, determine the key paragraph analysis method according to the number of operation execution personnel and the proportion of execution paragraphs to obtain the key paragraphs, and determine the transmission monitoring node and the transmission monitoring node set according to whether the key paragraph exists in each monitoring node to be analyzed; The key paragraph analysis method includes detecting the overlap of data fluctuation cycles and abnormal behavior characteristics of each operator; If there are Class II key devices in the target management area, the transmission monitoring nodes are determined according to the quality parameters of the associated monitoring nodes of each Class II key device, and the transmission monitoring node set is determined according to the average line distance; Under the set confirmation completion condition, the matching control node corresponding to each transmission monitoring node set is set, and the set confirmation completion condition is to complete the selection of the transmission monitoring node in the target monitoring scene and complete the setting of the transmission monitoring node set; Under the information transmission condition, periodically determine whether to update the transmission execution plan for the corresponding transmission monitoring node set according to the proportion of fluctuating transmission lines of each transmission monitoring node set, update the transmission execution plan according to the matching coefficient between the transmission monitoring nodes, and select the transmission execution plan corresponding to the largest reference matching coefficient to transmit data information within the latest transmission monitoring cycle; The reference matching coefficient is the average value of the matching coefficients between the transmission monitoring nodes having a transmission relationship in the transmission execution scheme; Execution segment ratio = the sum of the duration of each operation execution segment in the monitoring data to be analyzed / the duration of the monitoring data to be analyzed. The operation execution segment is the video segment corresponding to the debugging operation performed by the operation execution personnel on the key analysis equipment; For a single operation execution section, the data fluctuation cycle overlap is negatively correlated with the start interval duration, and the start interval duration is the interval duration between the start time of the operation execution section and the start time of the device detection cycle at the current time; The proportion of fluctuating transmission lines = the number of fluctuating transmission lines in the transmission lines of each transmission monitoring node set / the number of transmission lines of each transmission monitoring node set.

2. The smart grid data transmission method based on carrier modulation according to claim 1 is characterized in that: The key device is a target device having any device data whose value is not within the corresponding preset data range.

3. The smart grid data transmission method based on carrier modulation according to claim 2 is characterized in that: If the correlation coefficient between the two key devices is greater than a preset correlation coefficient, the two key devices are mutually correlated key devices, and the correlation coefficient is determined according to the reference abnormal correlation frequency and the correlation influence coefficient between the key devices; The relevant range is determined according to the monitoring range of the associated monitoring node corresponding to each key device.

4. The smart grid data transmission method based on carrier modulation according to claim 3 is characterized in that: Detect the relevant influence coefficient of each type of key equipment in each dense area, set the key analysis equipment for each dense area according to the relevant influence coefficient of each type of key equipment, and record the type of key equipment with a relevant influence coefficient greater than the preset relevant influence coefficient as the key analysis equipment of the corresponding dense area; For any dense area, detect the quality parameters of each associated monitoring node of each key analysis device, and determine the set of monitoring nodes to be analyzed in the corresponding dense area according to the quality parameters; The relevant influence coefficient is determined according to the relevant radiation degree of each type of key equipment and the abnormal moment correlation.

5. The smart grid data transmission method based on carrier modulation according to claim 4 is characterized in that: Obtain the monitoring data to be analyzed of each set of monitoring nodes to be analyzed, and determine the key paragraph analysis method corresponding to each monitoring data to be analyzed according to the number of operators and the proportion of execution paragraphs in each monitoring data to be analyzed. The key paragraph analysis method is to detect the overlap of data fluctuation cycles or to detect abnormal behavior characteristics of each operator; The monitoring nodes to be analyzed with key sections are recorded as transmission monitoring nodes, and the transmission monitoring nodes in the same dense area are recorded as a transmission monitoring node set.

6. The smart grid data transmission method based on carrier modulation according to claim 5, characterized in that: If the number of operation execution personnel in a monitoring data to be analyzed is greater than the preset number of execution personnel or the execution section ratio is greater than the preset execution section ratio, the data fluctuation cycle overlap of each operation execution section in the monitoring data to be analyzed is detected, and the key section is determined according to the data fluctuation cycle overlap, and the operation execution section whose data fluctuation cycle overlap is greater than the preset cycle overlap is recorded as the key section; If the number of operators in a monitoring data to be analyzed is less than or equal to the preset number of operators and the execution section ratio is less than or equal to the preset execution section ratio, abnormal behavior feature detection is performed for each operator in the monitoring data to be analyzed, and the corresponding moment of each abnormal behavior feature is recorded as a key moment, and the extraction time of the corresponding extracted key section is determined according to the abnormal persistence coefficient of each key moment, and the extraction time is positively correlated with the abnormal persistence coefficient of the corresponding key moment; For any critical moment, the abnormality persistence coefficient is positively correlated with the number of device detection cycles in which the key equipment corresponding to the critical moment has abnormalities.

7. The smart grid data transmission method based on carrier modulation according to claim 6 is characterized in that: Detecting the quality parameters of the associated monitoring nodes of each of the second-class key devices, and determining the transmission monitoring node set according to the quality parameters of each associated monitoring node and the average line distance between the transmission monitoring nodes; The associated monitoring nodes whose quality parameters are greater than the preset quality parameters are recorded as transmission monitoring nodes, and the average line distance between the transmission monitoring nodes in any transmission monitoring node set is less than the preset average line distance; When the set is confirmed to be complete, the matching control nodes corresponding to each transmission monitoring node set are set according to the transmission busy coefficient of each control node in the target monitoring scene and the node spacing distance.

8. The smart grid data transmission method based on carrier modulation according to claim 7, characterized in that: The information transmission condition is that all transmission monitoring node sets and their matching transmission nodes in the target monitoring scene are set; A fluctuating transmission line is a transmission line whose stability coefficient is less than a preset stability coefficient.

9. The smart grid data transmission method based on carrier modulation according to claim 8, characterized in that: For a single transmission monitoring node set, if the proportion of fluctuating transmission lines is greater than the preset proportion of fluctuating transmission lines, the transmission execution plan for the transmission monitoring node set is updated, where: Detecting the matching coefficient between each transmission monitoring node and other transmission monitoring nodes in the transmission monitoring node set; The matching coefficient between any two transmission monitoring nodes is determined based on the reference stability coefficient and the line distance.

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