Distribution Substation Safety Perception Method and System Based on Multi-Sensor Information

Abstract

The present invention discloses a safety perception method and system for a distribution substation based on multi-sensor information. The method includes: acquiring device sensing information of a plurality of distribution devices in a target distribution substation area; screening out a plurality of working devices from the plurality of distribution devices based on the device sensing information; determining working circuit information corresponding to any plurality of the working devices based on a preset device working circuit rule; and determining area anomaly parameters corresponding to the target distribution substation area according to the device sensing information based on a prediction neural network corresponding to the working circuit information. It can be seen that the present invention can fully combine the working circuits between the distribution substation devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution substation.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for sensing power distribution room security based on multi-sensor information. Background Art

[0002] With the development of smart grid technology, the power industry is gradually exploring intelligent distribution room sensing technology, such as multi-dimensional sensing through biometric access control, intelligent monitoring, intelligent inspection robots, wireless environmental sensing terminals, ozone sensors and other intelligent sensing devices. However, in the existing distribution room sensing and monitoring solutions, generally only the status of specific distribution room equipment is sensed, and the working circuits between multiple distribution room equipment are not fully considered to accurately analyze the collaborative perception problem between the equipment. Therefore, its perception accuracy is low and the monitoring effect is poor. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distribution room safety perception method and system based on multi-sensor information, which can fully combine the working circuits between the distribution room equipment to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a power distribution room safety perception method based on multi-sensor information, the method comprising:

[0005] Acquire the device sensor information of multiple power distribution devices in the target power distribution room area;

[0006] Based on the device sensing information, screening out a plurality of working devices from the plurality of power distribution devices;

[0007] Based on a preset equipment working circuit rule, determine working circuit information corresponding to any plurality of the working equipment;

[0008] Based on the prediction neural network corresponding to the working loop information and according to the equipment sensor information, the regional abnormality parameters corresponding to the target power distribution room area are determined.

[0009] As an optional embodiment, in the first aspect of the present invention, the device sensing information includes at least one of temperature information, humidity information, radio frequency information, light information, image information, vibration information and sound information.

[0010] As an optional implementation, in the first aspect of the present invention, the selecting a plurality of working devices from the plurality of power distribution devices based on the device sensing information includes:

[0011] For each of the said power distribution devices, determine the device type corresponding to the power distribution device;

[0012] Based on the preset correspondence between the type and the parameter judgment value, determine at least one parameter judgment value corresponding to the device type;

[0013] According to the parameter judgment value, judge the device sensing information corresponding to the power distribution device to obtain the parameter working possibility;

[0014] According to the regular working time corresponding to the device type and the current time point, determine the time working possibility corresponding to the power distribution device;

[0015] Calculate the product of the parameter working possibility and the time working possibility to obtain the working possible parameter corresponding to the power distribution device;

[0016] Screen out the devices among the multiple power distribution devices whose working possible parameters are greater than a preset first parameter threshold to obtain multiple working devices.

[0017] As an optional implementation manner, in the first aspect of the present invention, the device type is a transformer device, a low-voltage switchgear device, a capacitor cabinet device, a medium-voltage incoming line device, a low-voltage outgoing line device, or a basic support device.

[0018] As an optional implementation manner, in the first aspect of the present invention, the step of judging the device sensing information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility includes:

[0019] For each sensing information in the device sensing information, determine the parameter judgment value corresponding to the sensing information;

[0020] Perform a difference calculation between the information value of the sensing information and the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; the parameter difference includes positive and negative signs;

[0021] Calculate the weighted sum average of the parameter differences of all the sensing information in the device sensing information to obtain the parameter working possibility corresponding to the power distribution device.

[0022] As an optional implementation manner, in the first aspect of the present invention, the step of determining the time working possibility corresponding to the power distribution device according to the regular working time corresponding to the device type and the current time point includes:

[0023] According to the preset correspondence between the device type and the working time interval, and the device type, determine at least one working time period corresponding to the power distribution device;

[0024] Determine whether the current time point is within any of the working time periods. If so, determine that the time working possibility corresponding to the power distribution device is the first positive value;

[0025] If not, calculate the minimum time difference between the current time point and the interval boundary points of any of the working time periods;

[0026] Determine that the time working possibility corresponding to the power distribution device is the product of the first positive value and the time weight; the time weight is less than 1, and the time weight is inversely proportional to the minimum time difference.

[0027] As an optional implementation manner, in the first aspect of the present invention, the determining the working loop information corresponding to any plurality of the working devices based on the preset device working loop rules includes:

[0028] According to the preset work task table, determine the multiple work tasks corresponding to the current time point;

[0029] For each work task, determine the task working device set and the working loop corresponding to the work task;

[0030] Judge whether the set formed by all the working devices includes the task working device set. If so, determine that the working loop corresponding to the work task is the target working loop; the target working loop includes the device information of multiple working devices that form a continuous relationship in the work process;

[0031] Determine all the target working loops as the working loop information.

[0032] As an optional implementation manner, in the first aspect of the present invention, the determining the area abnormal parameter corresponding to the target substation area based on the prediction neural network corresponding to the working loop information and according to the device sensing information includes:

[0033] For each of the target working loops, determine the prediction neural network corresponding to the target working loop; the prediction neural network is trained by a training data set including the training device sensing information and work anomaly annotations corresponding to multiple corresponding working loops;

[0034] Input the sensing information in all the device sensing information that belongs to the device information corresponding to the target working loop into the prediction neural network to obtain the abnormal parameter corresponding to the target working loop;

[0035] Calculate the weighted sum average of the abnormal parameters corresponding to all the target working circuits to obtain the regional abnormal parameter corresponding to the target substation area; wherein, the weighted calculation weight of the abnormal parameter corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the number of device information in the corresponding target working circuit; the second weight is proportional to the corresponding historical abnormal rate of the target working circuit;

[0036] When the regional abnormal parameter is greater than a preset second parameter threshold, an abnormal warning is issued for the target substation area.

[0037] A second aspect of the embodiments of the present invention discloses a substation safety perception system based on multi-sensor information, and the system includes:

[0038] An acquisition module, configured to acquire device sensing information of a plurality of power distribution devices in a target substation area;

[0039] A screening module, configured to screen out a plurality of working devices from the plurality of power distribution devices based on the device sensing information;

[0040] A determination module, configured to determine the working circuit information corresponding to any plurality of the working devices based on a preset device working circuit rule;

[0041] A prediction module, configured to determine the regional abnormal parameter corresponding to the target substation area according to the device sensing information based on a prediction neural network corresponding to the working circuit information.

[0042] As an optional implementation manner, in the second aspect of the present invention, the device sensing information includes at least one of temperature information, humidity information, radio frequency information, light information, image information, vibration information, and sound information.

[0043] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the screening module screens out a plurality of working devices from the plurality of power distribution devices based on the device sensing information includes:

[0044] For each power distribution device, determine the device type corresponding to the power distribution device;

[0045] Based on a preset correspondence between the type and the parameter judgment value, determine at least one parameter judgment value corresponding to the device type;

[0046] Judge the device sensing information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility;

[0047] Determine the time working possibility corresponding to the power distribution equipment according to the regular working hours corresponding to the equipment type and the current time point;

[0048] Calculate the product of the parameter working possibility and the time working possibility to obtain the working possible parameter corresponding to the power distribution equipment;

[0049] Screen out the equipment among the multiple power distribution equipment whose working possible parameter is greater than a preset first parameter threshold to obtain multiple working equipment.

[0050] As an optional implementation manner, in the second aspect of the present invention, the equipment type is a transformer equipment, a low-voltage switchgear equipment, a capacitor cabinet equipment, a medium-voltage incoming line equipment, a low-voltage outgoing line equipment, or a basic support equipment.

[0051] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the screening module determines the parameter working possibility by judging the equipment sensing information corresponding to the power distribution equipment according to the parameter judgment value includes:

[0052] For each sensing information in the equipment sensing information, determine the parameter judgment value corresponding to the sensing information;

[0053] Perform a difference calculation on the information value of the sensing information and the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; the parameter difference includes positive and negative signs;

[0054] Calculate the weighted sum average of the parameter differences of all the sensing information in the equipment sensing information to obtain the parameter working possibility corresponding to the power distribution equipment.

[0055] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the screening module determines the time working possibility corresponding to the power distribution equipment according to the regular working hours corresponding to the equipment type and the current time point includes:

[0056] According to the corresponding relationship between the preset equipment type and the working time interval, and the equipment type, determine at least one working time period corresponding to the power distribution equipment;

[0057] Judge whether the current time point is within any of the working time periods. If so, determine that the time working possibility corresponding to the power distribution equipment is a first positive value;

[0058] If not, calculate the minimum time difference between the current time point and the interval boundary point of any of the working time periods;

[0059] Determine that the time working possibility corresponding to the power distribution equipment is the product of the first positive value and the time weight; the time weight is less than 1, and the time weight is inversely proportional to the minimum time difference.

[0060] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the determination module determines the working loop information corresponding to any plurality of the working devices based on a preset device working loop rule includes:

[0061] According to a preset work task table, determine a plurality of work tasks corresponding to the current time point;

[0062] For each work task, determine the task working device set and the working loop corresponding to the work task;

[0063] Judge whether the set formed by all the working devices includes the task working device set. If so, determine the working loop corresponding to the work task as the target working loop; the target working loop includes device information of a plurality of working devices that form a continuous relationship in the work process;

[0064] Determine all the target working loops as the working loop information.

[0065] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the prediction module determines the area anomaly parameter corresponding to the target power distribution room area based on the prediction neural network corresponding to the working loop information and according to the device sensing information includes:

[0066] For each of the target working loops, determine the prediction neural network corresponding to the target working loop; the prediction neural network is trained by a training data set including training device sensing information and work anomaly annotations corresponding to a plurality of corresponding working loops;

[0067] Input the sensing information belonging to the device information corresponding to the target working loop among all the device sensing information into the prediction neural network to obtain the anomaly parameter corresponding to the target working loop;

[0068] Calculate the weighted sum average of the anomaly parameters corresponding to all the target working loops to obtain the area anomaly parameter corresponding to the target power distribution room area; wherein, the weighted calculation weight of the anomaly parameter corresponding to each target working loop is the product of the first weight and the second weight; the first weight is proportional to the number of device information in the corresponding target working loop; the second weight is proportional to the corresponding historical anomaly rate of the target working loop;

[0069] When the area anomaly parameter is greater than a preset second parameter threshold, issue an anomaly warning for the target power distribution room area.

[0070] The third aspect of the present invention discloses another power distribution room safety perception system based on multi-sensor information, and the system includes:

[0071] A memory storing executable program code;

[0072] A processor coupled to the memory;

[0073] The processor calls the executable program code stored in the memory and executes some or all of the steps in the power distribution room safety perception method based on multi-sensor information disclosed in the first aspect of the present invention.

[0074] The fourth aspect of the present invention discloses a computer storage medium, and the computer storage medium stores computer instructions, which are used to execute some or all of the steps in the power distribution room safety perception method based on multi-sensor information disclosed in the first aspect of the present invention when being called.

[0075] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0076] The present invention can screen out multiple working devices from multiple power distribution devices based on device sensing information, then determine the working circuit information corresponding to any multiple working devices according to the preset device working circuit rules, and predict the regional abnormal parameters based on the device sensing information according to the prediction neural network corresponding to the working circuit information, so as to be able to fully combine the working circuits between the power distribution room devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0078] Figure 1 is a flowchart of a power distribution room safety perception method based on multi-sensor information disclosed in an embodiment of the present invention.

[0079] Figure 2 is a structural diagram of a power distribution room safety perception system based on multi-sensor information disclosed in an embodiment of the present invention.

[0080] Figure 3 is a structural diagram of another power distribution room safety perception system based on multi-sensor information disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below 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 work belong to the scope of protection of the present invention.

[0082] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0083] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0084] The present invention discloses a power distribution room safety perception method and system based on multi-sensor information, which can screen out multiple working devices from multiple power distribution devices based on device sensor information, then determine the working circuit information corresponding to any multiple working devices according to the preset device working circuit rules, and predict the regional abnormal parameters based on the device sensor information according to the prediction neural network corresponding to the working circuit information, so as to be able to fully combine the working circuits between the power distribution room devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room. The following will be described in detail respectively.

[0085] Embodiment 1

[0086] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a power distribution room safety perception method based on multi-sensor information disclosed in an embodiment of the present invention. Among them, Figure 1 the described power distribution room safety perception method based on multi-sensor information can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 1As shown in the figure, the power distribution room safety perception method based on multi-sensor information may include the following operations:

[0087] 101. Obtain the device sensing information of multiple power distribution devices in the target power distribution room area.

[0088] 102. Based on the device sensing information, screen out multiple working devices from the multiple power distribution devices.

[0089] 103. Based on the preset device working circuit rules, determine the working circuit information corresponding to any multiple working devices.

[0090] 104. Based on the prediction neural network corresponding to the working circuit information, determine the area anomaly parameters corresponding to the target power distribution room area according to the device sensing information.

[0091] It can be seen that the above-mentioned invention embodiments can screen out multiple working devices from multiple power distribution devices based on the device sensing information, then determine the working circuit information corresponding to any multiple working devices according to the preset device working circuit rules, and predict the area anomaly parameters based on the device sensing information according to the prediction neural network corresponding to the working circuit information, so as to be able to fully combine the working circuits between the power distribution room devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room.

[0092] As an optional embodiment, in the above steps, the device sensing information includes at least one of temperature information, humidity information, radio frequency information, light information, image information, vibration information, and sound information.

[0093] It can be seen that through the above optional embodiment, the content of the device sensing information is defined to comprehensively characterize the state characteristics of the power distribution devices, and assist in achieving more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room devices, and improving the monitoring effect and safety level of the power distribution room.

[0094] As an optional embodiment, in the above steps, screening out multiple working devices from multiple power distribution devices based on the device sensing information includes:

[0095] For each power distribution device, determine the device type corresponding to the power distribution device;

[0096] Based on the preset correspondence between the type and the parameter judgment value, determine at least one parameter judgment value corresponding to the device type;

[0097] According to the parameter judgment value, judge the device sensing information corresponding to the power distribution device to obtain the parameter working possibility;

[0098] According to the regular working time corresponding to the device type and the current time point, determine the time working possibility corresponding to the power distribution device;

[0099] Calculate the product of the parameter working possibility and the time working possibility to obtain the working possible parameter corresponding to the power distribution equipment;

[0100] Screen out the equipment among multiple power distribution equipment whose working possible parameters are greater than a preset first parameter threshold to obtain multiple working equipment.

[0101] It can be seen that through the above optional embodiments, it is possible to determine the working possibility of the power distribution equipment based on the parameter judgment value corresponding to the equipment type of the power distribution equipment and the regular working hours, so as to screen out more accurate working equipment, assist in realizing a more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room equipment, and improve the monitoring effect and safety level of the power distribution room.

[0102] As an optional embodiment, in the above steps, the equipment type is a transformer equipment, a low-voltage switchgear equipment, a capacitor cabinet equipment, a medium-voltage incoming line equipment, a low-voltage outgoing line equipment or a basic support equipment.

[0103] It can be seen that through the above optional embodiments, the equipment type of the power distribution equipment is defined to comprehensively characterize the type characteristics of the power distribution equipment, assist in realizing a more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room equipment, and improve the monitoring effect and safety level of the power distribution room.

[0104] As an optional embodiment, in the above steps, according to the parameter judgment value, judge the equipment sensing information corresponding to the power distribution equipment to obtain the parameter working possibility, including:

[0105] For each sensing information in the equipment sensing information, determine the parameter judgment value corresponding to the sensing information;

[0106] Perform a difference calculation on the information value of the sensing information and the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; optionally, the parameter difference includes positive and negative signs;

[0107] Calculate the weighted sum average of the parameter differences of all the sensing information in the equipment sensing information to obtain the parameter working possibility corresponding to the power distribution equipment.

[0108] It can be seen that through the above optional embodiments, it is possible to perform a difference judgment on the information value of each sensing information based on the parameter judgment value corresponding to the equipment type of the power distribution equipment and then perform a weighted sum average to obtain the working possibility in terms of parameters corresponding to the power distribution equipment, so as to facilitate subsequent accurate safety prediction, assist in realizing a more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room equipment, and improve the monitoring effect and safety level of the power distribution room.

[0109] As an alternative embodiment, in the above steps, determining the time working possibility corresponding to the power distribution equipment according to the regular working hours corresponding to the equipment type and the current time point includes:

[0110] According to the corresponding relationship between the preset equipment type and the working time interval, and the equipment type, determine at least one working time period corresponding to the power distribution equipment;

[0111] Judge whether the current time point is within any of the working time periods. If so, determine that the time working possibility corresponding to the power distribution equipment is the first positive value;

[0112] If not, calculate the minimum time difference between the current time point and the interval boundary point of any of the working time periods;

[0113] Determine that the time working possibility corresponding to the power distribution equipment is the product of the first positive value and the time weight; the time weight is less than 1, and the time weight is inversely proportional to the minimum time difference.

[0114] It can be seen that through the above alternative embodiments, it is possible to perform difference judgment and weighted sum averaging on the information value of each sensing information based on the parameter judgment value corresponding to the equipment type of the power distribution equipment, so as to obtain the working possibility in terms of parameters corresponding to the power distribution equipment, which is convenient for subsequent accurate safety prediction, and assist in realizing more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room equipment, improving the monitoring effect and safety level of the power distribution room.

[0115] As an alternative embodiment, in the above steps, determining the working circuit information corresponding to any number of working devices based on the preset equipment working circuit rules includes:

[0116] According to the preset work task list, determine the multiple work tasks corresponding to the current time point;

[0117] For each work task, determine the task working device set and the working circuit corresponding to the work task;

[0118] Judge whether the set formed by all the working devices includes the task working device set. If so, determine the working circuit corresponding to the work task as the target working circuit; optionally, the target working circuit includes the device information of multiple working devices that form a continuous relationship in the work process;

[0119] Determine all the target working circuits as the working circuit information.

[0120] It can be seen that through the above optional embodiments, the working circuit of the ongoing work task can be determined based on the device set of the work task and the current working device information, so as to facilitate subsequent accurate safety prediction, assist in realizing more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room devices, and improve the monitoring effect and safety level of the power distribution room.

[0121] As an optional embodiment, in the above steps, based on the prediction neural network corresponding to the working circuit information, according to the device sensing information, determine the regional abnormal parameter corresponding to the target power distribution room area, including:

[0122] For each target working circuit, determine the prediction neural network corresponding to the target working circuit; optionally, the prediction neural network is trained by a training data set including the training device sensing information and working anomaly annotations corresponding to multiple corresponding working circuits;

[0123] Input the sensing information of the device information belonging to the target working circuit in all device sensing information into the prediction neural network to obtain the abnormal parameter corresponding to the target working circuit;

[0124] Calculate the weighted summation average value of the abnormal parameters corresponding to all target working circuits to obtain the regional abnormal parameter corresponding to the target power distribution room area; optionally, among them, the weighted calculation weight of the abnormal parameter corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the number of device information in the corresponding target working circuit; the second weight is proportional to the historical abnormal rate corresponding to the corresponding target working circuit;

[0125] When the regional abnormal parameter is greater than the preset second parameter threshold, an abnormal warning is issued to the target power distribution room area.

[0126] It can be seen that through the above optional embodiments, the abnormal prediction can be carried out according to the prediction neural network corresponding to the target working circuit based on the device sensing information of the devices in the circuit, and the weighted calculation is carried out based on the corresponding weight rule to obtain a more accurate abnormal characterization parameter, realizing more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room devices, and improving the monitoring effect and safety level of the power distribution room.

[0127] Embodiment 2

[0128] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a power distribution room safety perception system based on multi-sensing information disclosed in an embodiment of the present invention. Among them, Figure 2The described power distribution room safety perception system based on multi-sensor information can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 2 shown, the power distribution room safety perception system based on multi-sensor information may include:

[0129] An acquisition module 201, configured to acquire device sensing information of multiple power distribution devices in a target power distribution room area.

[0130] A screening module 202, configured to screen out multiple working devices from the multiple power distribution devices based on the device sensing information.

[0131] A determination module 203, configured to determine working loop information corresponding to any multiple working devices based on a preset device working loop rule.

[0132] A prediction module 204, configured to determine a region anomaly parameter corresponding to the target power distribution room area according to the device sensing information based on a prediction neural network corresponding to the working loop information.

[0133] It can be seen that the above-mentioned invention embodiments can screen out multiple working devices from multiple power distribution devices based on the device sensing information, then determine the working loop information corresponding to any multiple working devices according to the preset device working loop rule, and predict the region anomaly parameter according to the device sensing information based on the prediction neural network corresponding to the working loop information, so as to be able to fully combine the working loops between the power distribution room devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room.

[0134] As an optional embodiment, the device sensing information includes at least one of temperature information, humidity information, radio frequency information, light information, image information, vibration information, and sound information.

[0135] It can be seen that through the above optional embodiment, the content of the device sensing information is defined to comprehensively characterize the state characteristics of the power distribution devices, and to assist in achieving more accurate and precise safety perception prediction by fully combining the working loops between the power distribution room devices, and improving the monitoring effect and safety level of the power distribution room.

[0136] As an optional embodiment, the specific manner in which the screening module screens out multiple working devices from the multiple power distribution devices based on the device sensing information includes:

[0137] For each power distribution device, determine the device type corresponding to the power distribution device;

[0138] Based on a preset correspondence between the type and the parameter judgment value, determine at least one parameter judgment value corresponding to the device type;

[0139] Judge the device sensing information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility;

[0140] Determine the time working possibility corresponding to the power distribution device according to the regular working time corresponding to the device type and the current time point;

[0141] Calculate the product of the parameter working possibility and the time working possibility to obtain the working possible parameter corresponding to the power distribution device;

[0142] Screen out the devices among multiple power distribution devices whose working possible parameters are greater than a preset first parameter threshold to obtain multiple working devices.

[0143] It can be seen that through the above optional embodiments, it is possible to determine the working possibility based on the parameter judgment value and the regular working time corresponding to the device type of the power distribution device, so as to screen out more accurate working devices, assist in realizing a more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room devices, and improve the monitoring effect and safety level of the power distribution room.

[0144] As an optional embodiment, the device type is a transformer device, a low-voltage switchgear device, a capacitor cabinet device, a medium-voltage incoming line device, a low-voltage outgoing line device or a basic support device.

[0145] It can be seen that through the above optional embodiments, the device type of the power distribution device is defined to comprehensively characterize the type characteristics of the power distribution device, assist in realizing a more accurate and precise safety perception prediction by fully combining the working circuits between the power distribution room devices, and improve the monitoring effect and safety level of the power distribution room.

[0146] As an optional embodiment, the specific manner in which the screening module judges the device sensing information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility includes:

[0147] For each sensing information in the device sensing information, determine the parameter judgment value corresponding to the sensing information;

[0148] Perform a difference calculation on the information value of the sensing information and the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; optionally, the parameter difference includes positive and negative signs;

[0149] Calculate the weighted sum average of the parameter differences of all sensing information in the device sensing information to obtain the parameter working possibility corresponding to the power distribution device.

[0150] It can be seen that through the above optional embodiments, it is possible to perform difference judgment and weighted summation averaging on the information value of each sensing information based on the parameter judgment value corresponding to the device type of the power distribution device, so as to obtain the working possibility in terms of the parameters corresponding to the power distribution device, facilitate subsequent accurate safety prediction, assist in fully combining the working circuits between the power distribution room devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room.

[0151] As an optional embodiment, the specific manner in which the screening module determines the time working possibility corresponding to the power distribution device according to the regular working time corresponding to the device type and the current time point includes:

[0152] According to the corresponding relationship between the preset device type and the working time interval, and the device type, determine at least one working time period corresponding to the power distribution device;

[0153] Judge whether the current time point is within any working time period. If so, determine that the time working possibility corresponding to the power distribution device is the first positive value;

[0154] If not, calculate the minimum time difference between the current time point and the interval boundary point of any working time period;

[0155] Determine that the time working possibility corresponding to the power distribution device is the product of the first positive value and the time weight; the time weight is less than 1, and the time weight is inversely proportional to the minimum time difference.

[0156] It can be seen that through the above optional embodiments, it is possible to perform difference judgment and weighted summation averaging on the information value of each sensing information based on the parameter judgment value corresponding to the device type of the power distribution device, so as to obtain the working possibility in terms of the parameters corresponding to the power distribution device, facilitate subsequent accurate safety prediction, assist in fully combining the working circuits between the power distribution room devices to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room.

[0157] As an optional embodiment, the specific manner in which the determination module determines the working circuit information corresponding to any number of working devices based on the preset device working circuit rule includes:

[0158] According to the preset work task table, determine the multiple work tasks corresponding to the current time point;

[0159] For each work task, determine the task working device set and the working circuit corresponding to the work task;

[0160] Determine whether the set formed by all working devices includes the set of task working devices. If so, determine the working circuit corresponding to the work task as the target working circuit; optionally, the device information of multiple working devices that form a continuous relationship in the work process is included in the target working circuit;

[0161] Determine all target working circuits as working circuit information.

[0162] It can be seen that through the above optional embodiments, the working circuit of the work task being executed can be determined based on the device set of the work task and the current working device information, so as to facilitate subsequent accurate safety prediction, and assist in realizing more accurate and precise safety perception prediction by fully combining the working circuits between the distribution room devices, improving the monitoring effect and safety level of the distribution room.

[0163] As an optional embodiment, the specific manner in which the prediction module determines the area anomaly parameter corresponding to the target distribution room area based on the prediction neural network corresponding to the working circuit information and according to the device sensing information includes:

[0164] For each target working circuit, determine the prediction neural network corresponding to the target working circuit; optionally, the prediction neural network is trained by a training data set including the training device sensing information and work anomaly annotations corresponding to multiple corresponding working circuits;

[0165] Input the sensing information of the device information belonging to the target working circuit among all device sensing information into the prediction neural network to obtain the anomaly parameter corresponding to the target working circuit;

[0166] Calculate the weighted sum average of the anomaly parameters corresponding to all target working circuits to obtain the area anomaly parameter corresponding to the target distribution room area; optionally, among them, the weighted calculation weight of the anomaly parameter corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the number of device information in the corresponding target working circuit; the second weight is proportional to the historical anomaly rate corresponding to the corresponding target working circuit;

[0167] When the area anomaly parameter is greater than the preset second parameter threshold, an anomaly warning is issued for the target distribution room area.

[0168] It can be seen that through the above optional embodiments, the anomaly prediction can be performed according to the device sensing information of the devices in the circuit by the prediction neural network corresponding to the target working circuit, and the weighted calculation is performed based on the corresponding weight rule to obtain a more accurate anomaly characterization parameter, realizing more accurate and precise safety perception prediction by fully combining the working circuits between the distribution room devices, and improving the monitoring effect and safety level of the distribution room.

[0169] Embodiment III

[0170] Please refer to Figure 3 , Figure 3 which is another power distribution room safety perception system based on multi-sensor information disclosed in the embodiments of the present invention. Figure 3 The described power distribution room safety perception system based on multi-sensor information is applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 3 shown, the power distribution room safety perception system based on multi-sensor information may include:

[0171] A memory 301 storing executable program code;

[0172] A processor 302 coupled to the memory 301;

[0173] Wherein, the processor 302 calls the executable program code stored in the memory 301 to execute the steps of the power distribution room safety perception method described in Embodiment 1.

[0174] Embodiment 4

[0175] The embodiments of the present invention disclose a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the power distribution room safety perception method described in Embodiment 1.

[0176] Embodiment 5

[0177] The embodiments of the present invention disclose a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the power distribution room safety perception method described in Embodiment 1.

[0178] The above describes specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0179] The systems, devices, modules, or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0180] For the convenience of description, when describing the above devices, they are divided into various units according to their functions and described separately. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0181] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0182] This specification is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0183] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps for the functions specified in one block or multiple blocks.

[0185] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0186] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0187] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0188] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0189] This specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0190] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0191] Finally, it should be noted that the disclosed method and system for power distribution room safety perception based on multi-sensor information according to the embodiments of the present invention are only the preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A safety perception method for a distribution room based on multi-sensor information, characterized in that The method includes: Obtaining device sensing information of multiple power distribution devices in a target substation area; Based on the device sensing information, screening out multiple working devices from the multiple power distribution devices, including: For each of the power distribution devices, determining the device type corresponding to the power distribution device; Based on a preset correspondence between types and parameter judgment values, determining at least one parameter judgment value corresponding to the device type; According to the parameter judgment value, judging the device sensing information corresponding to the power distribution device to obtain a parameter working possibility; According to the regular working time corresponding to the device type and the current time point, determining the time working possibility corresponding to the power distribution device; Calculating the product of the parameter working possibility and the time working possibility to obtain a working possible parameter corresponding to the power distribution device; Screening out the devices among the multiple power distribution devices whose working possible parameters are greater than a preset first parameter threshold to obtain multiple working devices; Based on a preset device working loop rule, determining the working loop information corresponding to any multiple of the working devices, including: According to a preset work task table, determining multiple work tasks corresponding to the current time point; For each work task, determining the task working device set and the working loop corresponding to the work task; Judging whether the set formed by all the working devices includes the task working device set, and if so, determining the working loop corresponding to the work task as the target working loop; the target working loop includes device information of multiple working devices that form a continuous relationship in the work process; Determining all the target working loops as the working loop information; Based on the prediction neural network corresponding to the working loop information, according to the device sensing information, determining the area anomaly parameter corresponding to the target substation area, including: For each of the target working loops, determining the prediction neural network corresponding to the target working loop; the prediction neural network is trained by a training data set including training device sensing information and work anomaly annotations corresponding to multiple corresponding working loops; Inputting the sensing information in all the device sensing information that belongs to the device information corresponding to the target working loop into the prediction neural network to obtain the anomaly parameter corresponding to the target working loop; Calculating the weighted sum average of the anomaly parameters corresponding to all the target working loops to obtain the area anomaly parameter corresponding to the target substation area; wherein, the weighted calculation weight of the anomaly parameter corresponding to each target working loop is the product of a first weight and a second weight; the first weight is proportional to the number of device information in the corresponding target working loop; the second weight is proportional to the historical anomaly rate corresponding to the corresponding target working loop; When the area anomaly parameter is greater than a preset second parameter threshold, sending an anomaly warning to the target substation area.

2. The safety perception method for a power distribution room based on multi-sensor information according to claim 1, characterized in that The device sensing information includes at least one of temperature information, humidity information, radio frequency information, light information, image information, vibration information, and sound information.

3. The safety perception method for a power distribution room based on multi-sensor information according to claim 1, wherein The device types are transformer equipment, low-voltage switchgear equipment, capacitor bank equipment, medium-voltage incoming line equipment, low-voltage outgoing line equipment, or basic support equipment.

4. The safety perception method for a power distribution room based on multi-sensor information according to claim 1, characterized in that Judging the device sensing information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility, including: For each sensing information in the device sensing information, determining the parameter judgment value corresponding to the sensing information; Calculating the difference between the information value of the sensing information and the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; the parameter difference includes positive and negative signs; Calculating the weighted sum average of the parameter differences of all the sensing information in the device sensing information to obtain the parameter working possibility corresponding to the power distribution device.

5. The safety perception method for a power distribution room based on multi-sensor information according to claim 1, characterized in that, Determining the time working possibility corresponding to the power distribution device according to the regular working time corresponding to the device type and the current time point, including: Determining at least one working time period corresponding to the power distribution device according to the preset correspondence between the device type and the working time interval and the device type; Judging whether the current time point is within any of the working time periods. If so, determining the time working possibility corresponding to the power distribution device as a first positive value; If not, calculating the minimum time difference between the current time point and the interval boundary point of any of the working time periods; Determining the time working possibility corresponding to the power distribution device as the product of the first positive value and the time weight; the time weight is less than 1, and the time weight is inversely proportional to the minimum time difference.

6. A power distribution room safety perception system based on multi-sensor information, characterized in that, The system includes: An acquisition module for acquiring the device sensing information of multiple power distribution devices in the target substation area; A screening module for screening out multiple working devices from the multiple power distribution devices based on the device sensing information, including: For each power distribution device, determining the device type corresponding to the power distribution device; Determining at least one parameter judgment value corresponding to the device type based on the preset correspondence between the type and the parameter judgment value; Judging the device sensing information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility; Determining the time working possibility corresponding to the power distribution device according to the regular working time corresponding to the device type and the current time point; Calculating the product of the parameter working possibility and the time working possibility to obtain the working possible parameter corresponding to the power distribution device; Screening out the devices in the multiple power distribution devices whose working possible parameters are greater than a preset first parameter threshold to obtain multiple working devices; A determination module for determining the working loop information corresponding to any multiple of the working devices based on the preset device working loop rule, including: Determining multiple working tasks corresponding to the current time point according to the preset working task list; For each working task, determining the task working device set and the working loop corresponding to the working task; Determine whether the set formed by all the described working devices includes the set of the task working devices. If so, determine the working circuit corresponding to the working task as the target working circuit; the device information of multiple working devices that form a continuous relationship in the working process is included in the target working circuit; Determine all the target working circuits as the working circuit information; A prediction module, configured to determine the area anomaly parameter corresponding to the target substation area based on the prediction neural network corresponding to the working circuit information and according to the device sensing information, including: For each of the target working circuits, determine the prediction neural network corresponding to the target working circuit; the prediction neural network is trained by a training data set including the training device sensing information and working anomaly annotations corresponding to multiple corresponding working circuits; Input the sensing information belonging to the device information corresponding to the target working circuit among all the device sensing information into the prediction neural network to obtain the anomaly parameter corresponding to the target working circuit; Calculate the weighted sum average of the anomaly parameters corresponding to all the target working circuits to obtain the area anomaly parameter corresponding to the target substation area; wherein, the weighted calculation weight of the anomaly parameter corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the number of device information in the corresponding target working circuit; the second weight is proportional to the historical anomaly rate corresponding to the corresponding target working circuit; When the area anomaly parameter is greater than a preset second parameter threshold, issue an anomaly warning for the target substation area.

7. A power distribution room safety perception system based on multi-sensor information, characterized in that, The system includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory and executes the substation safety perception method based on multi-sensing information according to any one of claims 1-5.

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