Power communication network fault locating method and system based on alarm correlation

Abstract

The application discloses a power communication network fault positioning method and system based on alarm correlation and relates to the technical field of digital information transmission. The power communication network fault positioning method based on alarm correlation determines a fault device, establishes a time correlation sequence, and obtains a plurality of first alternative fault correlation combinations from a database by using the time correlation sequence; a relative position diagram of the fault device is established, the relative position diagram of the fault device is compared with a matching position diagram stored in the database, a second alternative fault correlation combination is selected; and a power communication network fault source is determined from the database based on the second alternative fault correlation combination, so that the fault device can be quickly identified and correlated by comprehensively analyzing a fault code, a time stamp and a relative position of the device, the time for troubleshooting is reduced, the downtime of the power communication network is reduced, and the reliability and stability of the power system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital information transmission, in particular to a power communication network fault locating method and system based on alarm association. BACKGROUND

[0002] The power communication network has the characteristics of large scale and complex structure, which brings challenges to timely and accurately identify the fault source when the fault occurs. When there is an individual fault alarm in the network, the existing network management and monitoring means can be used to locate the fault source relatively easily. However, when a large number of fault alarm information is monitored in the network, a serious network connectivity problem has often occurred, and not every alarm information means that the corresponding network facility has failed. The failure of some network nodes or network links in key positions may trigger a large area of fault alarms.

[0003] Although the prior art can realize fault location based on alarm association, it has the problems of inaccurate fault location and low fault location efficiency. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a power communication network fault locating method and system based on alarm association, which solves the problems of inaccurate fault location and low fault location efficiency of the existing fault location method.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme: a power communication network fault locating method based on alarm association, comprising the following steps: determining the fault equipment based on the obtained power communication network fault code; establishing a time association sequence based on the time stamp of the alarm information, and obtaining a plurality of first candidate fault association combinations from the database using the time association sequence; establishing a fault equipment relative position map based on the power communication network fault code, comparing the fault equipment relative position map with the matching position map stored in the database, and selecting a second candidate fault association combination; determining the power communication network fault source from the database based on the second candidate fault association combination.

[0006] Further, the time stamp based on the alarm information establishes a time correlation sequence, and the time correlation sequence is used to obtain a plurality of first candidate fault correlation combinations from the database, including the following steps: extracting the time stamp in the power communication network fault code, arranging the fault devices according to the order of the time stamp, and constructing a time correlation sequence; comparing the season where the time stamp is located with the seasons stored in the database to determine the corresponding fault correlation combination set of the season where the time stamp is located in the database, wherein the fault correlation combination set includes a plurality of candidate fault correlation combinations; selecting a plurality of preselected fault correlation combinations from the fault correlation combination set according to the order of the fault devices and the number of the fault devices; obtaining the time difference between any two adjacent fault devices in the time correlation sequence, creating a time difference combination; obtaining a preselected time difference combination of each preselected fault correlation combination, and performing any analysis on the time difference combination and the preselected time difference combination to determine a plurality of first candidate fault correlation combinations.

[0007] Further, the time difference combination and the preselected time difference combination are analyzed to determine a plurality of first candidate fault correlation combinations, including the following steps: comparing the time difference combination with each preselected time difference combination one by one to obtain a time comparison coefficient, wherein the calculation formula of the time comparison coefficient is:

[0008]

[0009] In the formula, is the number of the preselected time difference combination, , is the total number of the preselected time difference combination, is the number of the time difference in the time difference combination, which is also the number of the time difference in each preselected time difference combination, , is the total number of the time difference in the time difference combination, which is also the total number of the time difference in each preselected time difference combination, is the first time difference in the time difference combination, is the first time difference in the first preselected time difference combination, is the second time difference in the time difference combination, is the second time difference in the second preselected time difference combination, is the third time difference in the time difference combination, is the third time difference in the third preselected time difference combination, is the fourth time difference in the time difference combination,

[0010] Furthermore, the method of establishing a relative position diagram of faulty equipment based on the fault code of the electric power communication network and comparing the relative position diagram of the faulty equipment with the matching position diagram of the first alternative fault association combination stored in the database includes the following steps: taking the coordinates of the first faulty equipment in the time association sequence as the origin, and establishing a rectangular coordinate system, marking other faulty equipment in the time association sequence in the coordinate system to form a relative position diagram of the faulty equipment; fusing and analyzing the coordinates of each faulty equipment in the relative position diagram of the faulty equipment with the coordinates of each faulty equipment in each matching position diagram to obtain a position deviation coefficient; and recording the first alternative fault association combination corresponding to the matching position diagram corresponding to the smallest position deviation coefficient as the second alternative fault association combination.

[0011] Furthermore, the calculation formula of the position deviation coefficient is as follows:

[0012]

[0013] Where, To match the location map number, , is the total number of matching position graphs, The number of the faulty device in the relative position diagram of the faulty device, and also the number of the faulty device in each matching position diagram. , is the total number of faulty devices in the faulty device relative position diagram, and also the total number of faulty devices in each matching position diagram. The faulty device in the relative position diagram is The horizontal coordinate of the faulty device, For the The first matching position in the map The horizontal coordinate of the faulty device, The faulty device in the relative position diagram is The vertical coordinate of the faulty device, For the The first matching position in the map The vertical coordinate of the faulty device, For the Position deviation coefficient.

[0014] Furthermore, if there are multiple second alternative fault association combinations, a second alternative fault association combination is determined, and the process is as follows: obtain the environmental feature data and fault feature data of each fault device in the time association sequence; obtain the environmental feature matching data and fault feature matching data of each fault device in the second alternative fault association combination; determine the fault matching coefficient based on the environmental feature data and fault feature data of each fault device and the environmental feature matching data and fault feature matching data of each fault device in the second alternative fault association combination; determine the second alternative fault association combination corresponding to the smallest fault matching coefficient as the final second alternative fault association combination.

[0015] Furthermore, the environmental feature data includes the standard deviation of the equipment environment temperature, the standard deviation of the equipment environment humidity, and the standard deviation of the equipment environment wind speed; the environmental feature matching data includes the standard deviation of the equipment environment temperature matching, the standard deviation of the equipment environment humidity matching, and the standard deviation of the equipment environment wind speed matching; the fault feature data includes the voltage peak value at the time of the fault and the temperature peak value at the time of the fault; the fault feature matching data includes the matching voltage peak value at the time of the fault and the matching temperature peak value at the time of the fault; the process of determining the fault matching coefficient is as follows: fusing the environmental feature data and the environmental feature matching data to obtain the environmental feature matching coefficient; fusing the fault feature data and the fault feature matching data to obtain the fault feature matching coefficient; weighting the environmental feature matching coefficient and the fault feature matching coefficient to obtain the fault matching coefficient, wherein the calculation formula of the fault matching coefficient is as follows:

[0016]

[0017] Where, is the number of the second alternative fault association combination, For the Fault matching coefficient, For the Environmental feature matching coefficient, For the Fault feature matching coefficient, is the environmental feature matching coefficient weight factor stored in the database, is the fault signature matching coefficient weight factor stored in the database.

[0018] Furthermore, the calculation formula of the environmental feature matching coefficient is as follows:

[0019]

[0020] Where, For the Environmental feature matching coefficient, is the standard deviation of the equipment ambient temperature, is the standard deviation of the equipment environment humidity, is a device environment wind force standard deviation, is a device environment temperature matching standard deviation, is a device environment temperature matching standard deviation, is a device environment humidity matching standard deviation, is a device environment humidity matching standard deviation, is a device environment wind force matching standard deviation, is a device environment wind force matching standard deviation, is a natural constant.

[0021] Further, the calculation formula of the fault feature matching coefficient is as follows:

[0022]

[0023] In the formula, is the first environment feature matching coefficient, is a fault voltage peak value, is a fault temperature peak value, is a fault voltage peak value, is a fault temperature peak value. is a fault voltage peak value, is a fault temperature peak value. is a fault temperature peak value.

[0024] A power communication network fault positioning system based on alarm association is used for the power communication network fault positioning method based on alarm association, and comprises a fault device analysis module, a time association establishment module, a fault matching module and a fault source determination module, wherein: the fault device analysis module is used for determining a fault device based on an obtained power communication network fault code; the time association establishment module is used for establishing a time association sequence based on a time stamp of alarm information, and obtaining a plurality of first alternative fault association combinations from a database by using the time association sequence; the fault matching module is used for establishing a fault device relative position map based on the power communication network fault code, comparing the fault device relative position map with a matching position map stored in the database from the first alternative fault association combination, and selecting a second alternative fault association combination; and the fault source determination module is used for determining a power communication network fault source from the database based on the second alternative fault association combination.

[0025] The present application has the following beneficial effects:

[0026] The power communication network fault positioning method and system based on alarm association can quickly identify and associate fault devices by comprehensively analyzing fault codes, time stamps and device relative positions, reduce fault troubleshooting time, reduce power communication network downtime, and thus improve the reliability and stability of the power system. The power communication network fault positioning method based on alarm association can effectively improve the accuracy and efficiency of fault positioning.

[0027] Of course, implementing any product of the application does not necessarily require achieving all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 A flowchart of a method for locating faults in a power communication network based on alarm correlation according to the present application.

[0029] Figure 2 A flowchart of a new system for locating faults in a power communication network based on alarm correlation according to the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application achieve quick identification and correlation of fault devices by comprehensive analysis of fault codes, time stamps and relative positions of devices, reduce the time for troubleshooting, reduce the downtime of the power communication network, and thus improve the reliability and stability of the power system. The method for locating faults in a power communication network based on alarm correlation can effectively improve the accuracy and efficiency of fault location.

[0031] Please refer to Figure 1 The embodiments of the present application provide a technical solution: a method for locating faults in a power communication network based on alarm correlation, comprising the following steps: determining a fault device based on an acquired power communication network fault code; establishing a time correlation sequence based on time stamps of alarm information, and acquiring a plurality of first alternative fault correlation combinations from a database by using the time correlation sequence; establishing a relative position map of the fault device based on the power communication network fault code, comparing the relative position map of the fault device with a matching position map stored in the database, and selecting a second alternative fault correlation combination; and determining a fault source of the power communication network from the database based on the second alternative fault correlation combination.

[0032] The fault code is accurately positioned to the fault device, laying a foundation for subsequent steps. The time sequence is established based on the time stamps of the alarm information, which can efficiently focus on the most recent fault and improve the relevance of fault analysis. According to the time correlation and the fault code, a plurality of possible fault correlation combinations are acquired from the database, further narrowing down the fault range. The relative position map of the fault device is compared with the matching position map in the database, and a more possible fault combination is selected to enhance the accuracy of positioning. Finally, the fault source of the power communication network is identified based on the more accurate second alternative fault correlation combination. In the database, the fault source information corresponding to the second alternative fault correlation combination is stored, which is determined based on historical fault information.

[0033] Through systematic steps, the fault range is quickly narrowed down, the troubleshooting time is reduced, the time and human resources required for manual troubleshooting are reduced, and the maintenance cost is reduced. The fault is quickly responded to and repaired, the equipment downtime is reduced, thereby improving the stability of the power communication network, and the historical data in the big data and database are used for intelligent analysis to optimize the fault processing process.

[0034] Specifically, the time stamp based on the alarm information establishes a time correlation sequence, and a plurality of first candidate fault correlation combinations are obtained from the database by using the time correlation sequence, including the following steps: extracting the time stamp in the power communication network fault code, arranging the fault equipment according to the order of the time stamp, and constructing a time correlation sequence, which ensures that the time sequence of the fault event is accurately captured; the season in which the time stamp is located is compared with the season stored in the database, which can help identify seasonal failure patterns and improve the accuracy of fault location, and determine the corresponding fault correlation combination set of the season in which the time stamp is located in the database, which includes a plurality of candidate fault correlation combinations.

[0035] According to the sequence of the fault equipment and the number of the fault equipment, a plurality of preselected fault correlation combinations are selected from the fault correlation combination set to further narrow down the fault range; the time difference between any two adjacent fault equipment in the time correlation sequence is obtained, and a time difference combination is created, which can reveal the interval time of the fault occurrence and provide important information for subsequent analysis; a preselected time difference combination of each preselected fault correlation combination is obtained, and any analysis of the time difference combination and the preselected time difference combination is performed to determine a plurality of first candidate fault correlation combinations, which helps to find the most possible fault cause and location.

[0036] In the embodiment, through time series and seasonal analysis, the fault equipment and its correlation can be more accurately identified, and the accuracy of fault location is improved. A plurality of first candidate fault correlation combinations are quickly screened out, the time of fault analysis and response is reduced, the overall operation and maintenance efficiency is improved, the seasonal comparison can help identify the fault patterns in a specific season, thereby providing guidance for prevention and maintenance. Historical data and time difference analysis are used to form data-based decision support, reducing the blindness of relying on experience.

[0037] Specifically, the time difference combination and the preselected time difference combination are analyzed in any way to determine a plurality of first candidate fault correlation combinations, including the following steps: comparing the time difference combination with each preselected time difference combination one by one to obtain a time comparison coefficient, wherein the calculation formula of the time comparison coefficient is:

[0038]

[0039] In the formula, is the number of the preselected time difference combination, , total number of preselected time difference combinations, number of time difference in time difference combination, also number of time difference in each preselected time difference combination, , total number of time difference in time difference combination, also total number of time difference in each preselected time difference combination, time difference in time difference combination, time difference in time difference combination, time difference in preselected time difference combination, time difference in preselected time difference combination, time difference in preselected time difference combination, time difference in preselected time difference combination, time difference in preselected time difference combination; the preselected time difference combination corresponding to the time difference comparison coefficient less than the time difference comparison threshold stored in the database is determined as the first candidate fault correlation combination.

[0040] In the embodiment, for each preselected time difference combination, it is compared with each time difference in the time difference combination one by one to evaluate the matching degree between them, and the calculated time difference comparison coefficient is compared with the time difference comparison threshold stored in the database. If a certain time difference comparison coefficient is less than the threshold, it means that the preselected time difference combination has a higher matching degree with the time difference combination. The fault correlation combination corresponding to the preselected time difference combination meeting the condition is determined as the first candidate fault correlation combination.

[0041] By quantifying the similarity of time difference, the fault event can be more accurately matched with the known fault mode, thereby reducing the possibility of misjudgment. By setting the threshold, the preselected time difference combinations that do not meet the conditions can be quickly excluded, the possible fault sources can be concentrated for analysis, and the speed and efficiency of fault troubleshooting can be improved. It is not only suitable for a specific type of fault, but also can be self-adapted and optimized for different seasons or different equipment types, and has great flexibility and expansibility.

[0042] Specifically, the relative position map of the fault equipment is established based on the power communication network fault code, and the relative position map of the fault equipment is compared with the matching position map stored in the database in the first candidate fault correlation combination, including the following steps: taking the coordinates of the first fault equipment in the time correlation sequence as the origin and establishing a rectangular coordinate system, which can provide a unified reference frame for subsequent fault equipment relative position calibration, and marking other fault equipments in the time correlation sequence in the coordinate system to form the relative position map of the fault equipment, which can intuitively display the spatial relationship between the fault equipments.

[0043] The coordinates of each faulty device in the faulty device relative position map are fused and analyzed with the coordinates of each faulty device in each matching position map to obtain the position deviation coefficient. The position deviation coefficient reflects the degree of difference between the faulty device relative position map and the matching position map. The first alternative fault association combination corresponding to the matching position map with the smallest position deviation coefficient is recorded as the second alternative fault association combination, indicating that this is the most likely fault association.

[0044] By establishing a relative position map, the spatial relationship between faulty devices can be more accurately identified, thereby improving the accuracy of fault location. By comparing the relative positions of faulty devices with predefined matching position maps, the most likely fault associations can be quickly screened, optimizing the fault diagnosis process. Accurate coordinate comparison can effectively reduce misdiagnosis and missed diagnosis caused by unclear position relationships, improving the reliability of fault diagnosis. A clear relative position map of faulty devices can help maintenance personnel quickly identify the source of the fault, reduce troubleshooting time, and improve system maintainability.

[0045] The calculation formula of the position deviation coefficient is as follows:

[0046]

[0047] Where, To match the location map number, , is the total number of matching position graphs, The number of the faulty device in the relative position diagram of the faulty device, and also the number of the faulty device in each matching position diagram. , is the total number of faulty devices in the faulty device relative position diagram, and also the total number of faulty devices in each matching position diagram. The faulty device in the relative position diagram is The horizontal coordinate of the faulty device, For the The first matching position in the map The horizontal coordinate of the faulty device, The faulty device in the relative position diagram is The vertical coordinate of the faulty device, For the The first matching position in the map The vertical coordinate of the faulty device, For the Position deviation coefficient.

[0048] In this embodiment, the relative positions of the faulty devices are utilized, and the positions of each faulty device in space are determined by their horizontal coordinates. By taking the known faulty device coordinates of the matching position map as a reference, a comparison with the actual relative positions of the faulty devices is allowed. The coordinate deviation of each faulty device in each matching position map is calculated. Specifically, for each matching position map, the error of each faulty device of the position match is calculated by the square difference, forming an error matrix. The sum of the error squares of all faulty devices is accumulated using the double summation formula, and the square root is taken to obtain the overall deviation coefficient of each matching position map. The smaller this coefficient, the closer the matching position map is to the actual position of the faulty device, and the better the matching effect. By calculating the deviation coefficients of all matching position maps, the matching position map with the smallest deviation coefficient is then selected as the optimal correlation combination.

[0049] Through specific numerical calculation, the adaptability of the relative positions of the faulty devices to the matching positions can be objectively evaluated, providing an intuitive comparison of advantages and disadvantages. Accurate deviation coefficient calculation can help to screen the optimal matching position map, thereby optimizing the accuracy of fault positioning and reducing the impact of errors.

[0050] Specifically, if the second alternative fault correlation combination is multiple, a second alternative fault correlation combination is determined as follows: obtaining the environmental feature data and fault feature data of each faulty device in the time correlation sequence; obtaining the environmental feature matching data and fault feature matching data of each faulty device in the second alternative fault correlation combination; determining a fault matching coefficient based on the environmental feature data and fault feature data of each faulty device and the environmental feature matching data and fault feature matching data of each faulty device in the second alternative fault correlation combination. Various algorithms (such as Euclidean distance, cosine similarity, etc.) can be used to calculate the fault matching coefficient; the second alternative fault correlation combination corresponding to the smallest fault matching coefficient is determined as the final second alternative fault correlation combination.

[0051] Through systematic data comparison and matching coefficient calculation, the most similar alternative combination to the actual faulty device can be more accurately identified, improving the accuracy of fault positioning. The decision-making process based on data analysis reduces the influence of subjective judgment, making the selection of fault correlation combination more scientific and reasonable. Through explicit matching coefficient calculation, the optimal fault correlation combination can be quickly screened, thereby accelerating the response speed of fault handling, improving the operation and maintenance efficiency. By analyzing the matching of environmental features and fault features, potential fault patterns can be found, enhancing the prediction ability for future faults and taking preventive measures in advance.

[0052] The environment characteristic data includes device environment temperature standard deviation, device environment humidity standard deviation and device environment wind power standard deviation, the environment characteristic matching data includes device environment temperature matching standard deviation, device environment humidity matching standard deviation and device environment wind power matching standard deviation; the fault characteristic data includes voltage peak value at fault and temperature peak value at fault, and the fault characteristic matching data includes matching voltage peak value at fault and matching temperature peak value at fault; the determination process of the fault matching coefficient is as follows: the environment characteristic data and the environment characteristic matching data are fused to obtain the environment characteristic matching coefficient; the fault characteristic data and the fault characteristic matching data are fused to obtain the fault characteristic matching coefficient; and the environment characteristic matching coefficient and the fault characteristic matching coefficient are weighted to obtain the fault matching coefficient, wherein the calculation formula of the fault matching coefficient is as follows:

[0053]

[0054] In the formula, is the number of the second alternative fault association combination, is the first fault matching coefficient, is the first environment characteristic matching coefficient, is the first fault characteristic matching coefficient, is the environment characteristic matching coefficient weight factor stored in the database, is the fault characteristic matching coefficient weight factor stored in the database. The environment characteristic data (standard deviation of environment temperature, humidity and wind power) and the fault characteristic data (voltage peak value and temperature peak value at fault) of the device are collected. Meanwhile, the corresponding matching data is prepared. The environment characteristic data and the environment characteristic matching data are fused to obtain the environment characteristic matching coefficient. This process can adopt weighted average, normalization and other methods to ensure that a representative matching coefficient is obtained. Similarly, the fault characteristic data and the fault characteristic matching data are fused to calculate the fault characteristic matching coefficient. The fault matching coefficient is calculated by weighted combination of the environment characteristic matching coefficient and the fault characteristic matching coefficient. The set weight factor can reflect the relative importance of each feature in fault matching. The minimum value is found in all fault matching coefficients to determine the corresponding second alternative fault association combination as the final recommended fault processing scheme. By fusing the environment characteristic and fault characteristic data, the fault matching can be more comprehensively evaluated, the fault identification is more accurate, the fusion processing and weighted calculation reduce the misjudgment possibly caused by a single feature, and the total reliability of the fault matching is improved.

[0055]

[0056]

[0057] ​​​Specifically, the calculation formula of the environmental feature matching coefficient is as follows:

[0058]

[0059] Where, For the Environmental feature matching coefficient, is the standard deviation of the equipment ambient temperature, is the standard deviation of the equipment environment humidity, is the standard deviation of the equipment environment wind force, For the The standard deviation of the device ambient temperature matching, For the The standard deviation of the device's ambient humidity matching, For the The standard deviation of the wind force matching of each device environment, is a natural constant.

[0060] In this implementation, absolute value calculations can be used to clearly evaluate the gap between the actual environmental characteristics of the device and the ideal standard. This design effectively reduces the impact of negative values ​​and is more intuitive. Substituting the sum of the differences into square root and exponential functions emphasizes that small differences are more influential than large differences. This sensitivity is of great significance for fault prediction, early warning, and other aspects. By directly comparing the underlying environmental characteristic data with its matching standard, the operating status of the device can be accurately assessed, improving the accuracy of fault prediction. The introduction of the characteristic matching coefficient quantifies the environmental characteristic differences between each device into numbers, facilitating comparison and analysis, and supporting decision-making.

[0061] Specifically, the calculation formula of the fault feature matching coefficient is as follows:

[0062]

[0063] Where, For the Environmental feature matching coefficient, is the peak voltage during fault, is the peak temperature at the time of failure, For the Matching voltage peak value during a fault, For the Matching temperature peaks at each fault.

[0064] In this implementation, the formula quantifies the difference between actual fault signatures (voltage and temperature peaks) and pre-set matching criteria. The square of the absolute value calculation ensures that negative values ​​do not affect the final result, achieving a more accurate matching metric. Applying the natural logarithm to the quantified differences emphasizes the relative impact of small differences, making it suitable for modeling nonlinear relationships. The natural logarithm function helps prevent matching coefficients from approaching zero, improving calculation stability. A constant of 1 is added to the calculation to avoid mathematical problems that can arise from logarithmic operations in the case of a perfect match, ensuring the formula's applicability and versatility.

[0065] A power communication network fault location system based on alarm association, used for the above-mentioned power communication network fault location method based on alarm association, such as Figure 2 As shown, it includes a fault device analysis module, a time association establishment module, a fault matching module and a fault source determination module, wherein: the fault device analysis module is used to determine the fault device based on the acquired power communication network fault code; the time association establishment module is used to establish a time association sequence based on the timestamp of the alarm information, and use the time association sequence to obtain multiple first alternative fault association combinations from the database; the fault matching module is used to establish a fault device relative position map based on the power communication network fault code, compare the fault device relative position map with the matching position map of the first alternative fault association combination stored in the database, and select the second alternative fault association combination; the fault source determination module is used to determine the power communication network fault source from the database based on the second alternative fault association combination.

[0066] An electronic device includes: a processor and a memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the above-mentioned power communication network fault location method based on alarm association.

[0067] A computer-readable storage medium is used to store a program, wherein when the program is executed by a processor, the method for locating a fault in a power communication network based on alarm association as described above is implemented.

[0068] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0070] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0071] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0072] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims intend to cover all such modifications and variations as fall within the true spirit and scope of the application.

[0073] It is apparent that a number of modifications and changes can be made to the present application without departing from the true spirit and scope of the application. Accordingly, it is intended that all the apparatus and methods that fall within the true spirit and scope of the present application should be embraced by the appended claims.

Claims

1. A method for locating faults in a power communication network based on alarm association, characterized in that: The following steps are involved: Determine the faulty device based on the acquired power communication network fault code; Extract the timestamps from the fault codes of the power communication network, arrange the faulty devices in the order of the timestamps, and construct a time-correlated sequence; Comparing the season of the timestamp with the seasons stored in the database, determining a fault association combination set corresponding to the season of the timestamp in the database, wherein the fault association combination set includes a plurality of candidate fault association combinations; Filtering multiple pre-selected fault association combinations from the fault association combination set according to the fault device sequence and the number of fault devices; Obtain the time difference between any two adjacent faulty devices in the time correlation sequence and create a time difference combination of the time correlation sequence; Obtaining a preselected time difference combination for each preselected fault correlation combination, fusing and analyzing the time difference combination of the time correlation sequence with the preselected time difference combination, and determining a plurality of first candidate fault correlation combinations; Take the coordinates of the first faulty device in the time-related sequence as the origin and establish a rectangular coordinate system. Mark other faulty devices in the time-related sequence in the coordinate system to form a relative position diagram of the faulty devices. The coordinates of each faulty device in the faulty device relative position diagram are combined with the coordinates of each faulty device in each matching position diagram stored in the database for fusion analysis to obtain a position deviation coefficient; The first candidate fault association combination corresponding to the matching position graph corresponding to the minimum position deviation coefficient is recorded as the second candidate fault association combination; If there are multiple second candidate fault association combinations, then one second candidate fault association combination is determined, and the process is as follows: Obtain environmental characteristic data and fault characteristic data of each faulty device in a time-correlated sequence; Obtaining environmental feature matching data and fault feature matching data of each faulty device in the second candidate fault association combination; Determine a fault matching coefficient based on the environmental feature data and fault feature data of each faulty device in the time correlation sequence and the environmental feature matching data and fault feature matching data of each faulty device in the second alternative fault correlation combination; Determine the second candidate fault association combination corresponding to the minimum fault matching coefficient as the final second candidate fault association combination; The power communication network fault source is determined from the database based on the second candidate fault association combination.

2. A method for locating faults in a power communication network based on alarm association according to claim 1, characterized in that: The step of fusing and analyzing the time difference combination with the preselected time difference combination to determine a plurality of first candidate fault association combinations includes the following steps: The time difference combination is compared with each pre-selected time difference combination one by one to obtain a time comparison coefficient, wherein the calculation formula of the time comparison coefficient is: Where, is the number of the pre-selected time difference combination, , is the total number of preselected time difference combinations, It is the number of the time difference in the time difference combination, and also the number of the time difference in each pre-selected time difference combination. , is the total number of time differences in the time difference combination, and is also the total number of time differences in each pre-time difference combination. The first A time difference, For the The first of the pre-selected time difference combinations time difference, For the Time comparison coefficient; A preselected fault association combination corresponding to a preselected time difference combination corresponding to a time comparison coefficient smaller than a time comparison threshold stored in the database is determined as a first candidate fault association combination.

3. The method for locating faults in a power communication network based on alarm association according to claim 1, characterized in that: The calculation formula of the position deviation coefficient is as follows: Where, To match the location map number, , is the total number of matching position graphs, The number of the faulty device in the relative position diagram of the faulty device, and also the number of the faulty device in each matching position diagram. , is the total number of faulty devices in the faulty device relative position diagram, and also the total number of faulty devices in each matching position diagram. The faulty device in the relative position diagram is The horizontal coordinate of the faulty device, For the The first matching position in the map The horizontal coordinate of the faulty device, The faulty device in the relative position diagram is The vertical coordinate of the faulty device, For the The first matching position in the map The vertical coordinate of the faulty device, For the Position deviation coefficient.

4. The method for locating faults in a power communication network based on alarm association according to claim 1, characterized in that: The environmental feature data includes the standard deviation of the device environment temperature, the standard deviation of the device environment humidity and the standard deviation of the device environment wind speed, and the environmental feature matching data includes the standard deviation of the device environment temperature matching, the standard deviation of the device environment humidity matching and the standard deviation of the device environment wind speed matching; The fault characteristic data includes a voltage peak value and a temperature peak value at the time of the fault, and the fault characteristic matching data includes a matching voltage peak value and a matching temperature peak value at the time of the fault; The process of determining the fault matching coefficient is as follows: The environmental feature data and the environmental feature matching data are fused and processed to obtain the environmental feature matching coefficient; Fusing the fault feature data and the fault feature matching data to obtain a fault feature matching coefficient; The environmental feature matching coefficient and the fault feature matching coefficient are weighted to obtain the fault matching coefficient. The calculation formula of the fault matching coefficient is as follows: Where, is the number of the second alternative fault association combination, For the Fault matching coefficient, For the Environmental feature matching coefficient, For the Fault feature matching coefficient, is the environmental feature matching coefficient weight factor stored in the database, is the fault signature matching coefficient weight factor stored in the database.

5. The method for locating faults in a power communication network based on alarm association according to claim 4, characterized in that: The calculation formula of the environmental feature matching coefficient is as follows: Where, For the Environmental feature matching coefficient, is the standard deviation of the equipment ambient temperature, is the standard deviation of the equipment environment humidity, is the standard deviation of the equipment environment wind force, For the The standard deviation of the device ambient temperature matching, is the standard deviation of the environmental humidity matching of the th device, For the The standard deviation of the wind force matching of each device environment, is a natural constant.

6. A method for locating faults in a power communication network based on alarm association according to claim 4, characterized in that ,The calculation formula of the fault feature matching coefficient is as follows: Where, For the Fault feature matching coefficient, is the peak voltage during fault, is the peak temperature at the time of failure, For the Matching voltage peak value during a fault, For the Matching temperature peaks at each fault.

7. A power communication network fault location system based on alarm association, used in the power communication network fault location method based on alarm association according to any one of claims 1 to 6, characterized in that: It includes a fault device analysis module, a time correlation establishment module, a fault matching module and a fault source determination module, wherein: The faulty device analysis module is used to determine the faulty device based on the acquired power communication network fault code; The time association establishing module is used to establish a time association sequence based on the timestamp of the alarm information, and obtain multiple first candidate fault association combinations from the database using the time association sequence; The fault matching module is used to establish a relative position diagram of the faulty equipment based on the fault code of the power communication network, compare the relative position diagram of the faulty equipment with the matching position diagram of the first candidate fault association combination stored in the database, and select a second candidate fault association combination; The fault source determination module is used to determine the power communication network fault source from the database based on the second candidate fault association combination.

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