A system and method for detecting potential safety hazards in the operation of enterprise power distribution equipment

Abstract

The present invention discloses a system and method for troubleshooting safety hazards in the operation of enterprise power distribution equipment, and belongs to the technical field of equipment fault troubleshooting. The coding information of the power distribution equipment and the fault identification are compiled respectively, and the power distribution equipment archive and the fault identification information library are generated accordingly; the back-end log records the behavior information of the fault identification triggered when the power distribution equipment fails in real time, and constructs the hidden danger fuzzy subclass; by identifying the synchronous fault behavior when the power distribution equipment fails, a hidden danger fuzzy set is generated; an index is constructed to capture the hidden danger fuzziness and generate an index set; the hidden danger synchronization between the power distribution equipment is analyzed, and a safety hidden danger troubleshooting strategy is output; the triggered behavior of the fault identification can be used as a unified quantitative correlation dimension to coordinate the fault generation behavior of each power distribution equipment, and at the same time, on the basis of fuzzy representation, the concrete correlation between the hidden danger and fault behaviors of the power distribution equipment under the long-term synchronization is reflected to realize the synchronous troubleshooting of the potential hidden dangers of the power distribution equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment fault troubleshooting, and particularly to a system and method for detecting potential safety hazards in the operation of enterprise power distribution equipment. Background Technique

[0002] In enterprise operation, as a core component of the power system, the stability and safety of power distribution equipment are directly related to the normal production and operation of the enterprise; the technology for detecting potential faults in power distribution equipment mainly relies on comprehensive monitoring and evaluation of the equipment status, including but not limited to infrared thermal imaging, ultrasonic detection, insulation resistance testing, etc., which can accurately detect key parameters such as the temperature, vibration, and insulation performance of the equipment, so as to discover potential fault hazards;

[0003] In the prior art, generally, a troubleshooting plan is formulated to monitor potential faults in power distribution equipment. In particular, for some power distribution equipment with special structures or functions, it is also necessary to execute according to a standardized troubleshooting process. Although some intelligent monitoring devices, such as sensors, can be used to achieve unmanned fault alarms, the intelligent monitoring devices can only monitor the faults that have occurred. For potential fault hazards, this process often still requires manual intervention and a large amount of manpower and material resources, which is not intelligent and scientific enough. Summary of the Invention

[0004] The purpose of the present invention is to provide a system and method for detecting potential safety hazards in the operation of enterprise power distribution equipment, so as to solve the problems raised in the above background technique.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A system for detecting potential safety hazards in the operation of enterprise power distribution equipment, the system includes: an archive information database module, a fault behavior information processing module, a potential hazard fuzzy analysis module, and a synchronous fault processing module;

[0007] The archive information database module is used to respectively compile the coding information of power distribution equipment and fault identifiers, and correspondingly generate a power distribution equipment archive database and a fault identifier information database;

[0008] The fault behavior information processing module records in real time through backend logs the behavior information of triggering fault identifiers when the power distribution equipment fails. The behavior information includes the number of times the fault identifier is triggered, and the node time reported when the power distribution equipment fails. Based on the behavior information, time segments are divided, and potential hazard fuzzy subclasses are constructed;

[0009] The hidden danger fuzzy analysis module generates a hidden danger fuzzy set by identifying synchronous fault behaviors when power distribution equipment fails. The hidden danger fuzzy set includes hidden danger fuzzy items, and each hidden danger fuzzy item consists of a power distribution equipment and the hidden danger fuzzy degree to which the power distribution equipment belongs to the hidden danger fuzzy set;

[0010] The synchronous fault processing module constructs an index based on the hidden danger fuzzy set, captures the hidden danger fuzzy degree, and generates an index set; based on the index set, analyzes the hidden danger synchronization degree between power distribution equipment, and outputs a safety hidden danger investigation strategy.

[0011] Furthermore, the file information library module includes a power distribution equipment file library unit and a fault identification information library unit;

[0012] The power distribution equipment file library unit is used to establish a power distribution equipment file library, and the power distribution equipment file library stores the coding information of several power distribution equipment. Among them, one power distribution equipment corresponds to one prepared power distribution equipment code, and a power distribution equipment sample set is generated, denoted as ES = {dp i |i ∈ [1, I]}, where dp i represents the i-th power distribution equipment, and I represents the total number of power distribution equipment;

[0013] The fault identification information library unit is used to establish a fault identification information library, and the fault identification information library stores several fault identification information. Among them, one type of fault identification corresponds to one type of fault, and one type of fault identification corresponds to one prepared fault identification code, and a fault identification sample set is generated, denoted as FS = {fs j |j ∈ [1, J]}, where fs j represents the j-th fault identification, and J represents the total number of fault identifications.

[0014] Furthermore, the fault behavior information processing module includes a node time reporting unit and a hidden danger fuzzy subclass generation unit;

[0015] The node time reporting unit retrieves the reported node time when the power distribution equipment dp j fails when the y-th trigger of the fault identification fs i is recorded as t y (i, j); respectively select the maximum and minimum values of the node time to form the time range when the fault identification fs j is triggered for the y-th time, and micro-quantize the time range into x time segments with the same scale. Any one time segment is denoted as T x (y, j);

[0016] The hidden danger fuzzy subclass generation unit, based on the node time and the time segment, if then the node time t y(i, j) records the hidden danger fuzzy subclass FSS[T x (y, j)] = {t y (i, j)|dp i ∈ES, i ∈ [1, I]}, where, represents the existential symbol, represents the node time t y (i, j) exists in the time segment T x (y, j).

[0017] Furthermore, the hidden danger fuzzy analysis module includes a hidden danger fuzzy set generation unit and a hidden danger fuzzy degree analysis unit;

[0018] The hidden danger fuzzy set generation unit is used to capture the synchronous fault behavior when the power distribution equipment fails. The synchronous fault behavior refers to the behavior of triggering the same fault identifier when the power distribution equipment fails; taking the fault identifier fs j as the data statistics dimension, generating a hidden danger fuzzy set FS(fs j |y), taking the power distribution equipment as the hidden danger fuzzy object, generating a hidden danger fuzzy item [dp i , fd y (i, j)], where, FS(fs j |y) represents the hidden danger fuzzy set composed of the hidden danger fuzzy items generated by the power distribution equipment where the fault occurs when the fault identifier fs j is triggered for the yth time, fd y (i, j) represents the hidden danger fuzzy degree to which the power distribution equipment dp j belongs to the hidden danger fuzzy set FS(fs i |y) when the fault identifier fs j is triggered for the yth time, and [dp i , fd y (i, j)] ∈ FS(fs j |y);

[0019] The hidden danger fuzzy degree analysis unit calculates the hidden danger fuzzy degree fd y (i, j) based on the hidden danger fuzzy subclass, and the formula is as follows:

[0020]

[0021] In the formula, if then count the number of node times included in the hidden danger fuzzy subclass FSS[T x (y, j)], denoted as If then let NUM{FSS[T x (y, j)]} represents the hidden danger fuzzy subclass FSS[T xThe number of node times included in (y, j).

[0022] Furthermore, the synchronization fault handling module includes an index creation unit and a hidden danger synchronization troubleshooting and analysis unit;

[0023] The index creation unit, based on the hidden danger fuzzy set FS(fs j |y), uses the power distribution equipment dp i as an index, and through the hidden danger fuzzy item [dp i , fd y (i, j), captures the hidden danger fuzzy degree fd y (i, j), and the capture range is the entire domain of the hidden danger fuzzy set. The range of the entire domain depends on the number and trigger times of the fault identifiers, that is, J×Y, where Y represents the current trigger times; using the power distribution equipment dp i as an index, statistically analyzes the capture results, and generates an index set, denoted as IS(dp i )={fd y (i, j)|y∈[1, Y], j∈[1, J]};

[0024] The hidden danger synchronization troubleshooting and analysis unit, based on the index set, analyzes and calculates the hidden danger synchronization degree between power distribution equipment. The formula is as follows:

[0025]

[0026] In the formula, HDS(i, i + 1) represents the hidden danger synchronization degree between the power distribution equipment dp i and the power distribution equipment dp i+1 , fd y (i + 1, j) represents the hidden danger fuzzy degree when the y-th trigger of the fault identifier fs j occurs, and the power distribution equipment dp i+1 belongs to the hidden danger fuzzy set FS(fs j |y);

[0027] A preset hidden danger synchronization degree threshold. If the hidden danger synchronization degree HDS(i, i + 1) is greater than or equal to the hidden danger synchronization degree threshold, then when the power distribution equipment dp i triggers any one of the fault identifiers for the (Y + 1)-th time, a safety hidden danger check is performed on the power distribution equipment dp i+1 for the same fault identifier.

[0028] A method for troubleshooting safety hidden dangers in the operation of enterprise power distribution equipment. This method includes the following steps:

[0029] Step S100: Compile the coding information of the power distribution equipment and the fault identifiers respectively, and correspondingly generate a power distribution equipment archive and a fault identifier information library;

[0030] Step S200: The back-end log records in real time the behavior information of triggering a fault identifier when a power distribution device fails. The behavior information includes the number of times the fault identifier is triggered and the node time reported when the power distribution device fails. Based on the behavior information, time segments are divided, and a hidden danger fuzzy subclass is constructed.

[0031] Step S300: By identifying the synchronous fault behavior when a power distribution device fails, a hidden danger fuzzy set is generated. The hidden danger fuzzy set includes hidden danger fuzzy items, and each hidden danger fuzzy item is composed of a power distribution device and the hidden danger fuzzy degree to which the power distribution device belongs to the hidden danger fuzzy set.

[0032] Step S400: Based on the hidden danger fuzzy set, an index is constructed to capture the hidden danger fuzzy degree, and an index set is generated. Based on the index set, the hidden danger synchronization degree between power distribution devices is analyzed, and a safety hidden danger investigation strategy is output.

[0033] Further, the specific implementation process of step S100 includes:

[0034] Step S101: Establish a power distribution device archive. The power distribution device archive stores the coding information of several power distribution devices. Among them, one power distribution device corresponds to one power distribution device code, and a power distribution device sample set is generated, denoted as ES = {dp i |i ∈ [1, I]}, where dp i represents the i-th power distribution device, and I represents the total number of power distribution devices.

[0035] Step S102: Establish a fault identifier information library. The fault identifier information library stores several fault identifier information. Among them, one type of fault identifier corresponds to one type of fault, and one type of fault identifier corresponds to one fault identifier code, and a fault identifier sample set is generated, denoted as FS = {fs j |j ∈ [1, J]}, where fs j represents the j-th fault identifier, and J represents the total number of fault identifiers.

[0036] Further, the specific implementation process of step S200 includes:

[0037] Step S201: When the y-th trigger of the fault identifier fs j is retrieved through the back-end log, the node time reported when the power distribution device dp i fails is recorded as t y (i, j). The maximum value and the minimum value of the node time are respectively selected to form the time range when the fault identifier fs j is triggered for the y-th time. The time range is micro-quantized into x time segments with the same scale, and any one time segment is denoted as T x (y, j);

[0038] Step S202: Based on the node time and time segment, if then the node time t y (i, j) is recorded in the hidden danger fuzzy subclass FSS[T x (y, j)] = {t y (i, j)|dp i ∈ES, i ∈ [1, I]}, where represents the existence symbol, represents that the node time t y (i, j) exists within the time segment T x (y, j).

[0039] Furthermore, the specific implementation process of the said step S300 includes:

[0040] Step S301: Capture the synchronous fault behavior when the power distribution equipment fails. The synchronous fault behavior refers to the behavior of triggering the same fault identifier when the power distribution equipment fails; taking the fault identifier fs j as the data statistics dimension, generate the hidden danger fuzzy set FS(Fs j |y), taking the power distribution equipment as the hidden danger fuzzy object, generate the hidden danger fuzzy item [dp i , fd y (i, j)], where FD(fs j |y) represents the hidden danger fuzzy set composed of the hidden danger fuzzy items generated by the power distribution equipment where the fault occurs when the fault identifier fs j is triggered for the yth time, fd y (i, j) represents the hidden danger degree to which the power distribution equipment dp j belongs to the hidden danger fuzzy set FS(fs i |y) when the fault identifier fs j is triggered for the yth time, and [dp i , fd y (i, j)] ∈ FS(fs j |y);

[0041] Step S302: Calculate the hidden danger degree fd y (i, j) based on the hidden danger fuzzy subclass. The formula is as follows:

[0042]

[0043] In the formula, if then count the number of node times included in the hidden danger fuzzy subclass FSS[T x (y, j)], denoted as If then let NUM{FSS[Tx (y, j)]} represents the hidden danger fuzzy subclass FSS[T x the number of node times contained in [(y, j)];

[0044] According to the above method, the present invention uses the triggered behavior of the fault identification as a unified quantitative correlation dimension to coordinate the fault generation behavior of each distribution equipment, that is, to quantitatively characterize the fault fuzzy correlation behavior between the distribution equipment through the node time reported when the distribution equipment fails; in particular, in the hidden danger fuzziness calculation formula, the numerator can characterize the distribution equipment fault behavior that generates hidden danger association in different hidden danger fuzzy subclasses. It means that the failure of the i-th distribution device occurs in the x-th time segment. At the same time, the distribution device that fails in the x-th time segment is not only the i-th distribution device, that is, these distribution devices have synchronous failure behavior in the x-th time segment, and then the statistics are obtained. In view of this, hidden danger fuzziness is to characterize the membership fuzziness between distribution equipment by synchronous fault behavior, and the membership fuzziness of different distribution equipment when the same fault mark is triggered the same number of times has the same fuzzy representation.

[0045] Furthermore, the specific implementation process of step S400 includes:

[0046] Step S401: Based on the hidden danger fuzzy set FS (fs j |y), with power distribution equipment dp i For index, through hidden danger fuzzy item [dp i , fd y (i, j)], capturing the hidden danger ambiguity fd y (i, j), the capture range is the entire domain of hidden danger fuzzy sets, the scope of the entire domain depends on the fault identification number and the triggering times, that is, J×Y, Y represents the current triggering times; the distribution equipment dp i For indexing, statistics capture results, and generate index sets, denoted as

[0047] IS(dp i )={fd y (i, j) | y∈[1, Y], j∈[1, J]};

[0048] Step S402: Based on the index set, analyze and calculate the synchronization degree of hidden dangers between the power distribution devices, the formula is as follows:

[0049]

[0050] In the formula, HDS(i, i+1) represents the distribution equipment dp i and power distribution equipment dp i+1 The synchronization degree of hidden dangers between fdy (i + 1, j) represents the power distribution equipment dp where the fault identification fs is triggered for the y-th time j when a fault occurs i+1 and belongs to the hidden danger fuzzy set FS(fs j |y);

[0051] According to the above method, taking the power distribution equipment as the index, on the basis of the fuzzy representation, the long-term synchrony of the membership fuzzy degrees of different power distribution equipment under different numbers of times when different fault identifications are triggered is concretely analyzed, so that the fuzzy representation can be concretely correlated. The hidden danger synchrony degree between power distribution equipment reflects this concrete correlation under the long-term synchrony; in view of this, in the calculation formula of the hidden danger synchrony degree, the smaller the membership fuzzy degree error of different power distribution equipment when the same fault identification is triggered the same number of times, and at the same time, the smaller the possible cumulative error under the long-term synchrony. The smaller this cumulative error, the larger the value in the inverse exponential function, that is, the smaller the cumulative error, the greater the concrete correlation under the long-term synchrony, and thus synchronous hidden danger investigation needs to be carried out;

[0052] Preset a hidden danger synchrony degree threshold. If the hidden danger synchrony degree HDS(i, i + 1) is greater than or equal to the hidden danger synchrony degree threshold, then when the power distribution equipment dp i triggers any one fault identification for the (Y + 1)-th time, conduct a safety hidden danger investigation on the power distribution equipment dp i+1 for the same fault identification.

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In a system and method for investigating safety hidden dangers in the operation of enterprise power distribution equipment provided by the present invention, the coding information of the power distribution equipment and the fault identification are respectively compiled, and a power distribution equipment archive library and a fault identification information library are correspondingly generated; the back-end log records in real time the behavior information of triggering the fault identification when the power distribution equipment fails, and constructs a hidden danger fuzzy subclass; by identifying the synchronous fault behavior when the power distribution equipment fails, a hidden danger fuzzy set is generated; an index is constructed to capture the hidden danger fuzzy degree and an index set is generated; the hidden danger synchrony degree between power distribution equipment is analyzed, and a safety hidden danger investigation strategy is output; it can take the triggered behavior of the fault identification as a unified quantitative correlation dimension to overall plan the fault generation behaviors of each power distribution equipment. At the same time, on the basis of the fuzzy representation, it reflects the concrete correlation under the long-term synchrony of the hidden danger fault behaviors of the power distribution equipment, and realizes the synchronous investigation of potential fault hidden dangers of the power distribution equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings are used to provide further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0055] Figure 1It is a schematic structural diagram of a system for detecting potential safety hazards in the operation of enterprise power distribution equipment according to the present invention;

[0056] Figure 2 It is a schematic diagram of the steps of a method for detecting potential safety hazards in the operation of enterprise power distribution equipment according to the present invention. Specific embodiments

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Please refer to Figure 1 , in the first embodiment: A system for detecting potential safety hazards in the operation of enterprise power distribution equipment is provided. The system includes: an archive information library module, a fault behavior information processing module, a potential hazard fuzzy analysis module, and a synchronous fault processing module;

[0059] The archive information library module is used to respectively compile the coding information of power distribution equipment and fault identifiers, and correspondingly generate a power distribution equipment archive library and a fault identifier information library;

[0060] Preferably, the archive information library module includes a power distribution equipment archive library unit and a fault identifier information library unit;

[0061] The power distribution equipment archive library unit is used to establish a power distribution equipment archive library. The power distribution equipment archive library stores the coding information of several power distribution equipment. Among them, a power distribution equipment is correspondingly compiled with a power distribution equipment code, and a power distribution equipment sample set is generated, denoted as ES = {dp i |i ∈ [1, I]}, where dp i represents the i-th power distribution equipment, and I represents the total number of power distribution equipment;

[0062] The fault identifier information library unit is used to establish a fault identifier information library. The fault identifier information library stores several fault identifier information. Among them, one type of fault identifier corresponds to one type of fault, and one type of fault identifier is correspondingly compiled with a fault identifier code, and a fault identifier sample set is generated, denoted as FS = {fs j |j ∈ [1, J]}, where fs j represents the j-th fault identifier, and J represents the total number of fault identifiers;

[0063] The fault behavior information processing module records in real time through backend logs the behavior information that triggers the fault identifier when a power distribution device fails. The behavior information includes the number of times the fault identifier is triggered and the node time reported when the power distribution device fails. Based on the behavior information, time segments are divided, and a hidden danger fuzzy subclass is constructed.

[0064] Preferably, the fault behavior information processing module includes a node time reporting unit and a hidden danger fuzzy subclass generating unit.

[0065] The node time reporting unit retrieves through backend logs the node time reported when the power distribution device dp fails at the y-th trigger of the fault identifier fs j and records it as t i (i, j); respectively select the maximum and minimum values of the node time to form the time range when the fault identifier fs y is triggered for the y-th time, and micro-quantize the time range into x time segments with the same scale. Denote any one of the time segments as T j (y, j); x

[0066] The hidden danger fuzzy subclass generating unit, based on the node time and the time segment, if then record the node time t y (i, j) into the hidden danger fuzzy subclass FSS[T x (y, j)] = {t y (i, j)|dp i ∈ES, i ∈ [1, I]}, where represents the existence symbol, represents that the node time t y (i, j) exists in the time segment T x (y, j);

[0067] The hidden danger fuzzy analysis module generates a hidden danger fuzzy set by identifying synchronous fault behaviors when a power distribution device fails. The hidden danger fuzzy set includes hidden danger fuzzy items, and each hidden danger fuzzy item consists of a power distribution device and the hidden danger fuzzy degree to which the power distribution device belongs to the hidden danger fuzzy set.

[0068] Preferably, the hidden danger fuzzy analysis module includes a hidden danger fuzzy set generating unit and a hidden danger fuzzy degree analysis unit.

[0069] The hidden danger fuzzy set generating unit is used to capture synchronous fault behaviors when a power distribution device fails. The synchronous fault behavior refers to the behavior of triggering the same fault identifier when the power distribution device fails; taking the fault identifier fs j as the data statistical dimension, generate a hidden danger fuzzy set FS(fs j |y), and taking the power distribution device as the hidden danger fuzzy object, generate a hidden danger fuzzy item [dp i , fdy (i, j)], where FS(fs j |y) represents the fault identifier fs j When it is triggered for the yth time, the hidden danger fuzzy set composed of the hidden danger fuzzy items corresponding to the power distribution equipment with a fault is generated, fd y (i, j) represents the yth trigger of the fault identifier fs j When, the power distribution equipment dp with a fault i Belongs to the hidden danger fuzzy degree of the hidden danger fuzzy set FS(fs j |y), and [dp i , fd y (i, j)] ∈ FS(fs j |y);

[0070] The hidden danger fuzzy degree analysis unit calculates the hidden danger fuzzy degree fd y (i, j) based on the hidden danger fuzzy subclasses. The formula is as follows:

[0071]

[0072] In the formula, if Then count the number of node times included in the hidden danger fuzzy subclass FSS[T x (y, j)], denoted as If Then let NUM{FSS[T x (y, j)]} represents the number of node times included in the hidden danger fuzzy subclass FSS[T x (y, j)];

[0073] The synchronous fault processing module constructs an index based on the hidden danger fuzzy set, captures the hidden danger fuzzy degree, and generates an index set; based on the index set, analyzes the hidden danger synchronization degree between power distribution equipment, and outputs a safety hidden danger investigation strategy;

[0074] Preferably, the synchronous fault processing module includes an index creation unit and a hidden danger synchronization investigation and analysis unit;

[0075] The index creation unit, based on the hidden danger fuzzy set FS(fs j |y), uses the power distribution equipment dp i as an index, and through the hidden danger fuzzy item [dp i , fd y (i, j)], captures the hidden danger fuzzy degree fd y (i, j). The capture range is the entire domain of the hidden danger fuzzy set, and the range of the entire domain depends on the number of the fault identifier and the number of trigger times, that is, J×Y, where Y represents the current number of trigger times; using the power distribution equipment dp i as an index, counts the capture results, and generates an index set, denoted as IS(dpi ) = {fd y (i, j) | y ∈ [1, Y], j ∈ [1, J]};

[0076] The hidden danger synchronous investigation and analysis unit analyzes and calculates the hidden danger synchronization degree between distribution equipment based on the index set. The formula is as follows:

[0077]

[0078] In the formula, HDS(i, i + 1) represents the hidden danger synchronization degree between distribution equipment dp i and distribution equipment dp i+1 ; fd y (i + 1, j) represents the hidden danger degree of the distribution equipment dp j where a fault occurs when the y-th trigger of the fault identifier fs i+1 belongs to the hidden danger fuzzy set FS(fs j |y);

[0079] A preset hidden danger synchronization degree threshold. If the hidden danger synchronization degree HDS(i, i + 1) is greater than or equal to the hidden danger synchronization degree threshold, then when any one of the fault identifiers is triggered for the (Y + 1)-th time on the distribution equipment dp i , conduct a safety hidden danger investigation of the same fault identifier on the distribution equipment dp i+1 .

[0080] Please refer to Figure 2 , in the second embodiment: Provide a method for investigating the operation safety hidden dangers of enterprise distribution equipment. The method includes the following steps:

[0081] Step S100: Compile the coding information of distribution equipment and fault identifiers respectively, and generate a distribution equipment archive and a fault identifier information library correspondingly;

[0082] Exemplarily, establish a distribution equipment archive. The distribution equipment archive stores the coding information of several distribution equipment. Among them, one distribution equipment corresponds to one compiled distribution equipment code, and a distribution equipment sample set is generated, denoted as ES = {dp i |i ∈ p1, I}, where dp i represents the i-th distribution equipment, and I represents the total number of distribution equipment;

[0083] Establish a fault identifier information library. The fault identifier information library stores the information of several fault identifiers. Among them, one type of fault identifier corresponds to one type of fault, and one type of fault identifier corresponds to one compiled fault identifier code, and a fault identifier sample set is generated, denoted as FS = {fs j |j ∈ [1, J]}, where fs j represents the j-th fault identifier, and J represents the total number of fault identifiers;

[0084] Step S200: The back-end log records in real time the behavior information of triggering the fault identifier when the power distribution equipment fails. The behavior information includes the number of times the fault identifier is triggered and the node time reported when the power distribution equipment fails. Time segments are divided based on the behavior information, and a hidden danger fuzzy subclass is constructed;

[0085] Exemplarily, the y-th time of triggering the fault identifier fs is retrieved through the back-end log j When the power distribution equipment dp i The node time reported when a failure occurs is denoted as t y (i, j); The maximum and minimum values of the node time are respectively selected to form the time range when the fault identifier fs j Is triggered for the y-th time. The time range is micro-quantized into x time segments with the same scale, and any one time segment is denoted as T x (y, j);

[0086] Based on the node time and the time segment, if Then the node time t y (i, j) is recorded in the hidden danger fuzzy subclass FSS[T x (y, j)] = {t y (i, j)|dp i ∈ES, i∈[1, I]}, where Represents the existence symbol, Represents that the node time t y (i, j) exists in the time segment T x (y, j);

[0087] Step S300: By identifying the synchronous fault behavior when the power distribution equipment fails, a hidden danger fuzzy set is generated. The hidden danger fuzzy set includes hidden danger fuzzy items, and the hidden danger fuzzy items are composed of the power distribution equipment and the hidden danger fuzzy degree to which the power distribution equipment belongs to the hidden danger fuzzy set;

[0088] Exemplarily, capture the synchronous fault behavior when the power distribution equipment fails. The synchronous fault behavior refers to the behavior of triggering the same fault identifier when the power distribution equipment fails; Taking the fault identifier fs j As the data statistics dimension, a hidden danger fuzzy set FS(fs j |y) is generated. Taking the power distribution equipment as the hidden danger fuzzy object, a hidden danger fuzzy item [dp i , fd y (i, j)] is generated, where PS(fs j |y) represents the hidden danger fuzzy set composed of the hidden danger fuzzy items corresponding to the power distribution equipment that fails when the fault identifier fs j Is triggered for the y-th time, and fd y(i, j) represents the fault identification fs triggered for the y-th time j When this occurs, the power distribution equipment dp where the fault occurs i belongs to the hidden danger fuzzy set FS(fs j |y), and [dp i , fd y (i, j)] ∈ FS(fs j |y);

[0089] Based on the hidden danger fuzzy subcategory, calculate the hidden danger fuzzy degree fd y (i, j), and the formula is as follows:

[0090]

[0091] In the formula, if then count the number of node times included in the hidden danger fuzzy subcategory FSS[T x (y, j)], denoted as If then let NUM{FSS[T x (y, j)]} represents the number of node times included in the hidden danger fuzzy subcategory FSS[T x (y, j)];

[0092] Step S400: Based on the hidden danger fuzzy set, construct an index, capture the hidden danger fuzzy degree, and generate an index set; based on the index set, analyze the hidden danger synchronization degree between power distribution equipment, and output a safety hidden danger investigation strategy;

[0093] Exemplarily, based on the hidden danger fuzzy set FS(fs j |y), using the power distribution equipment dp i as an index, through the hidden danger fuzzy item [dp i , df y (i, j)], capture the hidden danger fuzzy degree fd y (i, j), and the capture range is the entire domain of the hidden danger fuzzy set. The range of the entire domain depends on the number of the fault identification and the number of trigger times, that is, J×Y, where Y represents the current number of trigger times; using the power distribution equipment dp i as an index, count the capture results and generate an index set, denoted as IS(dp i ) = {fd y (i, j)|y ∈ [1, Y], j ∈ [1, J]};

[0094] Based on the index set, analyze and calculate the hidden danger synchronization degree between power distribution equipment, and the formula is as follows:

[0095]

[0096] Wherein, HDS(i, i + 1) represents the power distribution equipment dp i and the power distribution equipment dp i+1 The hidden danger synchronization degree between, fd y (i + 1, j) represents the y-th trigger of the fault identifier fs j When a fault occurs, the power distribution equipment dp i+1 Belongs to the hidden danger fuzzy set FS(fs j |y) of the hidden danger fuzzy degree;

[0097] The preset hidden danger synchronization degree threshold. If the hidden danger synchronization degree HDS(i, i + 1) is greater than or equal to the hidden danger synchronization degree threshold, then when the power distribution equipment dp i The (Y + 1)-th triggers any fault identifier, the power distribution equipment dp i+1 Carry out a safety hazard investigation of the same fault identifier.

[0098] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0099] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise, characterized in that: The method comprises the following steps: Step S100: respectively compiling the coding information of the power distribution equipment and the fault identification, and correspondingly generating the power distribution equipment archive and the fault identification information library; Step S200: The backend log records in real time the behavior information of triggering the fault mark when the power distribution equipment fails, the behavior information includes the number of times the fault mark is triggered and the node time reported when the power distribution equipment fails, divides the time segment based on the behavior information, and constructs the hidden danger fuzzy subclass; Step S300: Generate a hidden danger fuzzy set by identifying the synchronous fault behavior when the power distribution equipment fails, wherein the hidden danger fuzzy set includes hidden danger fuzzy items, and the hidden danger fuzzy items are composed of the power distribution equipment and the hidden danger fuzziness of the power distribution equipment belonging to the hidden danger fuzzy set; Step S400: Based on the hidden danger fuzzy set, construct an index, capture the hidden danger fuzziness, and generate an index set; based on the index set, analyze the hidden danger synchronization between the power distribution equipment, and output a safety hidden danger investigation strategy; The specific implementation process of step S400 includes: Step S401: Let dp i represents the ith power distribution equipment, let fs j represents the jth fault identification, and J represents the total number of fault identifications. Based on the hidden danger fuzzy set FS (fs j |y), with power distribution equipment dp i For index, through hidden danger fuzzy item [dp i , fd y (i, j)], capturing the hidden danger ambiguity fd y (i, j), the capture range is the entire domain of hidden danger fuzzy sets, the scope of the entire domain depends on the fault identification number and the triggering times, that is, J×Y, Y represents the current triggering times; the distribution equipment dp i For indexing, statistics capture results, and generate an index set, denoted as IS(dp i )={fd y (i, j) | y∈[1, Y], j∈[1, J]}; Step S402: Based on the index set, analyze and calculate the synchronization degree of hidden dangers between the power distribution devices, the formula is as follows: Where, HDS(i, j+1) represents the power distribution equipment dp i and power distribution equipment dp i+1 The synchronization degree of hidden dangers between fd y (i+1, j) indicates the yth time the fault flag fs is triggered j When the fault occurs, the power distribution equipment dp i+1 Belongs to the hidden danger fuzzy set FS (fs j |y)’s hidden danger ambiguity; The hidden danger synchronization threshold is preset. If the hidden danger synchronization degree HDS(i, i+1) is greater than or equal to the hidden danger synchronization threshold, then when the distribution equipment dp i When any fault indicator is triggered for the Y+1th time, the power distribution equipment dp i+1 Conduct safety hazard inspections for the same fault identifiers.

2. According to claim 1, a method for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise is characterized in that: The specific implementation process of step S100 includes: Step S101: Establish a power distribution equipment archive, which stores the coding information of several power distribution equipment, wherein one power distribution equipment corresponds to one power distribution equipment code, and generates a power distribution equipment sample set, which is recorded as ES={dp i |i∈[1,I]}, where I represents the total number of power distribution equipment; Step S102: Establish a fault identification information database, wherein the fault identification information database stores a plurality of fault identification information, wherein one fault identification corresponds to one fault type, and one fault identification corresponds to one fault identification code, and generates a fault identification sample set, which is recorded as FS = {fs j |j∈[1,J]}.

3. A method for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise according to claim 2, characterized in that: The specific implementation process of step S200 includes: Step S201: retrieve the yth trigger fault flag fs through the backend log j When the power distribution equipment dp i The node time reported when a failure occurs is denoted as t y (i, j); select the maximum and minimum values ​​of the node time respectively to form the fault mark fs j The time range of the yth trigger is micro-quantized into x time segments of the same scale, and any time segment is recorded as T x (y, j); Step S202: Based on the node time and time segment, if Then the node time t y (i, j) is recorded in the hidden danger fuzzy subclass FSS[T x (y, j)] = {t y (i, j)|dp i ∈ES,i∈[[1,I]}, where Indicates the presence of a symbol, Represents the node time t y (i, j) exists in time segment T x Within (y, j).

4. A method for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise according to claim 3, characterized in that: The specific implementation process of step S300 includes: Step S301: Capture the synchronous fault behavior when the power distribution equipment fails. The synchronous fault behavior refers to the behavior that triggers the same fault identifier when the power distribution equipment fails. j As the data statistical dimension, generate the hidden danger fuzzy set FS (fs j |y), taking the distribution equipment as the hidden danger fuzzy object, generating the hidden danger fuzzy item [dp i , fd y (i, j)], where FS(fs j |y) indicates the fault flag fs j When the yth time is triggered, the hidden danger fuzzy set composed of the hidden danger fuzzy items corresponding to the faulty distribution equipment is generated, fd y (i, j) indicates the yth time the fault flag fs is triggered j When the fault occurs, the power distribution equipment dp i Belongs to the hidden danger fuzzy set FS (fs j |y) of hidden danger ambiguity, and [dp i , fd y (i, j)]∈FS(fs j |y); Step S302: Calculate the hidden danger fuzziness fd based on the hidden danger fuzzy subclass y (i, j), the formula is as follows: In the formula, if Then the statistical hidden danger fuzzy subclass FSS[T x The number of node times contained in [(y, j)] is denoted as like Then NUM{FSS[T x (y, j)]} represents the hidden danger fuzzy subclass FSS[T x The number of node times contained in [(y, j)].

5. A system for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise, characterized in that: The system comprises: an archive information base module, a fault behavior information processing module, a hidden danger fuzzy analysis module and a synchronous fault processing module; The archive information library module is used to compile the coding information of the power distribution equipment and the fault identification respectively, and to generate the power distribution equipment archive library and the fault identification information library accordingly; The fault behavior information processing module records in real time through the backend log the behavior information of the fault mark triggered when the power distribution equipment fails, the behavior information includes the number of times the fault mark is triggered and the node time reported when the power distribution equipment fails, divides the time segment based on the behavior information, and constructs the hidden danger fuzzy subclass; The hidden danger fuzzy analysis module generates a hidden danger fuzzy set by identifying the synchronous fault behavior when the power distribution equipment fails. The hidden danger fuzzy set includes hidden danger fuzzy items, and the hidden danger fuzzy items are composed of the power distribution equipment and the hidden danger fuzziness of the power distribution equipment belonging to the hidden danger fuzzy set; The synchronization fault processing module constructs an index based on the hidden danger fuzzy set, captures the hidden danger fuzziness, and generates an index set; based on the index set, analyzes the hidden danger synchronization between the power distribution equipment, and outputs a safety hidden danger troubleshooting strategy; The synchronization fault processing module includes an index creation unit and a hidden danger synchronization troubleshooting and analysis unit; The index creation unit is used to make dp i represents the ith power distribution equipment, let fs j represents the jth fault identification, and J represents the total number of fault identifications. Based on the hidden danger fuzzy set FS (fs j |y), with power distribution equipment dp i For index, through hidden danger fuzzy item [dp i , fd y (i, j)], capturing the hidden danger ambiguity fd y (i, j), the capture range is the entire domain of hidden danger fuzzy sets, the scope of the entire domain depends on the fault identification number and the triggering times, that is, J×Y, Y represents the current triggering times; the distribution equipment dp i For indexing, statistics capture results, and generate index sets, denoted as IS(dp i )={fd y (i,j)|y∈[1,Y]、j∈[1,J]}; The hidden danger synchronization troubleshooting and analysis unit analyzes and calculates the hidden danger synchronization degree between the power distribution devices based on the index set, and the formula is as follows: In the formula, HDS(i, i+1) represents the distribution equipment dp i and power distribution equipment dp i+1 The synchronization degree of hidden dangers between fd y (i+1, j) indicates the yth time the fault flag fs is triggered j When the fault occurs, the power distribution equipment dp i+1 Belongs to the hidden danger fuzzy set FS (fs j |y)’s hidden danger ambiguity; The hidden danger synchronization threshold is preset. If the hidden danger synchronization degree HDS(i, i+1) is greater than or equal to the hidden danger synchronization threshold, then when the distribution equipment dp i When any fault indicator is triggered for the Y+1th time, the power distribution equipment dp i+1 Conduct safety hazard inspections for the same fault identifiers.

6. A system for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise according to claim 5, characterized in that: The archive information library module includes a power distribution equipment archive library unit and a fault identification information library unit; The power distribution equipment archive unit is used to establish a power distribution equipment archive, which stores the coding information of several power distribution equipment, wherein one power distribution equipment corresponds to one power distribution equipment code, and generates a power distribution equipment sample set, which is recorded as ES={dp i |i∈[1,I]}, where I represents the total number of power distribution equipment; The fault identification information base unit is used to establish a fault identification information base, wherein the fault identification information base stores a plurality of fault identification information, wherein one fault identification corresponds to one fault type, and one fault identification corresponds to one fault identification code, and generates a fault identification sample set, which is recorded as FS={fs j |j∈[1,J]}.

7. A system for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise according to claim 6, characterized in that: The fault behavior information processing module includes a node time reporting unit and a hidden danger fuzzy subclass generation unit; The node time reporting unit retrieves the yth trigger fault flag fs through the backend log j When the power distribution equipment dp i The node time reported when a failure occurs is denoted as t y (i, j); select the maximum and minimum values ​​of the node time respectively to form the fault mark fs j The time range of the yth trigger is micro-quantized into x time segments of the same scale, and any time segment is recorded as T x (y, j); The hidden danger fuzzy subclass generation unit is based on the node time and time segment. Then the node time t y (i, j) is recorded in the hidden danger fuzzy subclass FSS[T x (y, j)] = {t y (i, j)|dp i ∈ES, i∈[1,I]}, where, Indicates the presence of a symbol, Represents the node time t y (i, j) exists in time segment T x Within (y, j).

8. The system for troubleshooting safety hazards in the operation of power distribution equipment in an enterprise according to claim 7, characterized in that: The hidden danger fuzzy analysis module includes a hidden danger fuzzy set generation unit and a hidden danger fuzziness analysis unit; The hidden danger fuzzy set generation unit is used to capture the synchronous fault behavior when the power distribution equipment fails, and the synchronous fault behavior refers to the behavior that triggers the same fault mark when the power distribution equipment fails; Fault identification fs j As the data statistical dimension, generate the hidden danger fuzzy set FS (fs j |y), taking the distribution equipment as the hidden danger fuzzy object, generating the hidden danger fuzzy item [dp i , fd y (i, j)], where FS(fs j |y) indicates the fault flag fs j When the yth time is triggered, the hidden danger fuzzy set composed of the hidden danger fuzzy items corresponding to the faulty distribution equipment is generated, fd y (i, j) indicates the yth time the fault flag fs is triggered j When the fault occurs, the power distribution equipment dp i Belongs to the hidden danger fuzzy set FS (fs j |y) of hidden danger ambiguity, and [dp i , fd y (i, j)]∈FS(fs j |y); The hidden danger ambiguity analysis unit calculates the hidden danger ambiguity fd based on the hidden danger fuzzy subclass y (i, j), the formula is as follows: In the formula, if Then the statistical hidden danger fuzzy subclass FSS[T x The number of node times contained in [(y, j)] is denoted as like Then NUM{FSS[T x (y, j)]} represents the hidden danger fuzzy subclass FSS[T x The number of node times contained in [(y, j)].

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