Power distribution room safety sensing method and system based on multi-sensing information

Abstract

The invention discloses a power distribution room safety sensing method and system based on multi-sensing information. The method comprises the following steps: acquiring equipment sensing information of a plurality of power distribution equipment in a target power distribution room area; screening out a plurality of working devices from the plurality of power distribution devices based on the device sensing information; based on a preset device working loop rule, determining working loop information corresponding to any multiple working devices; and based on a prediction neural network corresponding to the working loop information, according to the equipment sensing information, determining an area abnormal parameter corresponding to the target power distribution room area. Therefore, more accurate and precise safety perception prediction can be realized by fully combining the working loops among the equipment in the power distribution room, and the monitoring effect and the safety degree of the power distribution room are improved.

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: Acquire the device sensor information of multiple power distribution devices in the target power distribution room area; Based on the device sensing information, screening out a plurality of working devices from the plurality of power distribution devices; Based on a preset equipment working circuit rule, determine working circuit information corresponding to any plurality of the working equipment; 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.

[0005] 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.

[0006] 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: For each of the power distribution devices, determining a device type corresponding to the power distribution device; Determine at least one parameter judgment value corresponding to the device type based on a preset correspondence between the type and the parameter judgment value; According to the parameter judgment value, the device sensor information corresponding to the power distribution device is judged to obtain the parameter working possibility; Determine the time working possibility corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point; Calculate the product of the parameter working possibility and the time working possibility to obtain the working possibility parameter corresponding to the power distribution equipment; Devices whose possible working parameters are greater than a preset first parameter threshold are screened out from the multiple power distribution devices to obtain multiple working devices.

[0007] As an optional implementation, in the first aspect of the present invention, the equipment type is transformer equipment, low-voltage switchgear equipment, capacitor cabinet equipment, medium-voltage incoming line equipment, low-voltage outgoing line equipment or basic support equipment.

[0008] As an optional implementation, in the first aspect of the present invention, judging the device sensor information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility includes: For each piece of sensor information in the device sensor information, determining the parameter judgment value corresponding to the sensor information; The information value of the sensing information is calculated to be different from the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; the parameter difference includes a positive and negative sign; The weighted sum average of the parameter differences of all the sensor information in the device sensor information is calculated to obtain the parameter working possibility corresponding to the power distribution equipment.

[0009] As an optional implementation, in the first aspect of the present invention, determining the time working possibility corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point includes: Determine at least one working time period corresponding to the power distribution equipment according to the preset correspondence between the equipment type and the working time interval, and the equipment type; Determine whether the current time point is within any of the working time periods, and if so, determine that the time working possibility corresponding to the power distribution equipment is a first positive value; If not, calculate the minimum time difference between the current time point and any boundary point of the working time period; 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.

[0010] As an optional implementation, in the first aspect of the present invention, determining the working circuit information corresponding to any plurality of the working devices based on a preset device working circuit rule includes: According to the preset work task table, determine multiple work tasks corresponding to the current time point; For each work task, determine the task work equipment set and work circuit corresponding to the work task; Determine whether the set formed by all the working devices includes the task working device set, and if so, determine that the working loop corresponding to the working task is the target working loop; the target working loop includes device information of multiple working devices that form a continuous relationship in the work process; All the target working loops are determined as working loop information.

[0011] As an optional implementation, in the first aspect of the present invention, the prediction neural network corresponding to the working loop information determines the regional abnormality parameters corresponding to the target power distribution room area according to the device sensor information, including: For each of the target working loops, determining a prediction neural network corresponding to the target working loop; the prediction neural network is trained by a training data set including sensor information of training devices corresponding to a plurality of corresponding working loops and working abnormality annotations; Inputting the sensor information of the device information corresponding to the target working circuit among all the device sensor information into the prediction neural network to obtain the abnormal parameters corresponding to the target working circuit; Calculate the weighted sum average of the abnormal parameters corresponding to all the target working circuits to obtain the regional abnormal parameters corresponding to the target distribution room area; wherein the weighted calculation weight of the abnormal parameters corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the amount of device information in the corresponding target working circuit; the second weight is proportional to the historical abnormality rate corresponding to the corresponding target working circuit; When the abnormal parameter of the area is greater than a preset second parameter threshold, an abnormal warning is issued to the target power distribution room area.

[0012] A second aspect of an embodiment of the present invention discloses a power distribution room safety perception system based on multi-sensor information, the system comprising: An acquisition module is used to acquire device sensor information of multiple power distribution devices in the target power distribution room area; A screening module, used for screening a plurality of working devices from the plurality of power distribution devices based on the device sensing information; A determination module, used to determine working circuit information corresponding to any plurality of working devices based on a preset device working circuit rule; A prediction module is used to determine the regional abnormality parameters corresponding to the target distribution room area based on the prediction neural network corresponding to the working loop information and according to the device sensor information.

[0013] As an optional embodiment, 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.

[0014] As an optional implementation, 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: For each of the power distribution devices, determining a device type corresponding to the power distribution device; Determine at least one parameter judgment value corresponding to the device type based on a preset correspondence between the type and the parameter judgment value; According to the parameter judgment value, the device sensor information corresponding to the power distribution device is judged to obtain the parameter working possibility; Determine the time working possibility corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point; Calculate the product of the parameter working possibility and the time working possibility to obtain the working possibility parameter corresponding to the power distribution equipment; Devices whose possible working parameters are greater than a preset first parameter threshold are screened out from the multiple power distribution devices to obtain multiple working devices.

[0015] As an optional implementation, in the second aspect of the present invention, the equipment type is transformer equipment, low-voltage switchgear equipment, capacitor cabinet equipment, medium-voltage incoming line equipment, low-voltage outgoing line equipment or basic support equipment.

[0016] As an optional implementation, in the second aspect of the present invention, the screening module judges the device sensor information corresponding to the power distribution device according to the parameter judgment value, and obtains the specific manner of the parameter working possibility, including: For each piece of sensor information in the device sensor information, determining the parameter judgment value corresponding to the sensor information; The information value of the sensing information is calculated to be different from the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; the parameter difference includes a positive and negative sign; The weighted sum average of the parameter differences of all the sensor information in the device sensor information is calculated to obtain the parameter working possibility corresponding to the power distribution equipment.

[0017] As an optional implementation, in the second aspect of the present invention, the screening module determines the specific manner of the time working possibility corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point, including: Determine at least one working time period corresponding to the power distribution equipment according to the preset correspondence between the equipment type and the working time interval, and the equipment type; Determine whether the current time point is within any of the working time periods, and if so, determine that the time working possibility corresponding to the power distribution equipment is a first positive value; If not, calculate the minimum time difference between the current time point and any boundary point of the working time period; 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.

[0018] As an optional implementation, 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: According to the preset work task table, determine multiple work tasks corresponding to the current time point; For each work task, determine the task work equipment set and work circuit corresponding to the work task; Determine whether the set formed by all the working devices includes the task working device set, and if so, determine that the working loop corresponding to the working task is the target working loop; the target working loop includes device information of multiple working devices that form a continuous relationship in the work process; All the target working loops are determined as working loop information.

[0019] As an optional implementation, in the second aspect of the present invention, the prediction module determines the specific manner of the regional abnormality parameter corresponding to the target distribution room area based on the prediction neural network corresponding to the working loop information and according to the device sensor information, including: For each of the target working loops, a prediction neural network corresponding to the target working loop is determined; the prediction neural network is trained by a training data set including sensor information of training devices corresponding to a plurality of corresponding working loops and working abnormality annotations; Inputting the sensor information of the device information corresponding to the target working circuit among all the device sensor information into the prediction neural network to obtain the abnormal parameters corresponding to the target working circuit; Calculate the weighted sum average of the abnormal parameters corresponding to all the target working circuits to obtain the regional abnormal parameters corresponding to the target distribution room area; wherein the weighted calculation weight of the abnormal parameters corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the amount of device information in the corresponding target working circuit; the second weight is proportional to the historical abnormality rate corresponding to the corresponding target working circuit; When the abnormal parameter of the area is greater than a preset second parameter threshold, an abnormal warning is issued to the target power distribution room area.

[0020] The third aspect of the present invention discloses another power distribution room safety perception system based on multi-sensor information, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part 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.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the distribution room safety perception method based on multi-sensor information disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can screen out multiple working devices from multiple power distribution devices based on device sensor information, and then determine the working circuit information corresponding to any multiple working devices according to preset equipment working circuit rules, and predict regional abnormal parameters according to the device sensor information based on the prediction neural network corresponding to the working circuit information, thereby being able to fully combine the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, thereby improving the monitoring effect and safety level of the distribution room. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a method for power distribution room safety perception based on multi-sensor information disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a power distribution room safety perception system based on multi-sensor information disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another power distribution room safety perception system based on multi-sensor information disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] 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 "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] 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, and then determine the working circuit information corresponding to any multiple working devices according to the preset device working circuit rules, and predict regional abnormal parameters according to the device sensor information based on the prediction neural network corresponding to the working circuit information, so as to fully combine the working circuits between the power distribution room equipment to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the power distribution room. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 This is a flow chart of a method for sensing power distribution room safety based on multi-sensor information disclosed in an embodiment of the present invention. 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). Figure 1 As shown, the power distribution room safety perception method based on multi-sensor information may include the following operations: 101. Obtain device sensor information of multiple power distribution devices in a target power distribution room area.

[0032] 102. Based on the device sensor information, multiple working devices are selected from multiple power distribution devices. 103. Based on a preset equipment working circuit rule, determine working circuit information corresponding to any number of working equipment. 104. Based on the prediction neural network corresponding to the working circuit information, the regional abnormal parameters corresponding to the target distribution room area are determined according to the equipment sensor information.

[0033] It can be seen that the above-mentioned embodiments of the invention can screen out multiple working devices from multiple distribution devices based on the equipment sensor information, and then determine the working circuit information corresponding to any multiple working devices according to the preset equipment working circuit rules, and predict the regional abnormal parameters according to the equipment sensor information based on the prediction neural network corresponding to the working circuit information, so as to fully combine the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, thereby improving the monitoring effect and safety level of the distribution room.

[0034] 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.

[0035] It can be seen that through the above optional embodiments, the content of the equipment sensor information is limited to comprehensively characterize the status characteristics of the distribution equipment, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0036] As an optional embodiment, in the above step, selecting multiple working devices from multiple power distribution devices based on device sensor information includes: For each power distribution device, determine the device type corresponding to the power distribution device; Determine at least one parameter judgment value corresponding to the device type based on a preset correspondence between the type and the parameter judgment value; According to the parameter judgment value, the device sensor information corresponding to the power distribution equipment is judged to obtain the parameter working possibility; Determine the time working possibility of the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point; Calculate the product of parameter working possibility and time working possibility to obtain the working possibility parameter corresponding to the power distribution equipment; Devices whose possible working parameters are greater than a preset first parameter threshold are screened out from a plurality of power distribution devices to obtain a plurality of working devices.

[0037] It can be seen that through the above-mentioned optional embodiments, the working possibility of the distribution equipment can be determined based on the parameter judgment value corresponding to the equipment type and the regular working time, so as to screen out more accurate working equipment, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0038] As an optional embodiment, in the above steps, the equipment type is transformer equipment, low-voltage switch cabinet equipment, capacitor cabinet equipment, medium-voltage incoming line equipment, low-voltage outgoing line equipment or basic support equipment.

[0039] It can be seen that through the above optional embodiments, the equipment type of the distribution equipment is limited to comprehensively characterize the type characteristics of the distribution equipment, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0040] As an optional embodiment, in the above steps, judging the device sensor information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility includes: For each piece of sensor information in the device sensor information, determining a parameter judgment value corresponding to the sensor information; Calculate 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; optionally, the parameter difference includes a positive or negative sign; The weighted sum average of the parameter differences of all sensor information in the device sensor information is calculated to obtain the parameter working possibility corresponding to the power distribution equipment.

[0041] It can be seen that through the above-mentioned optional embodiments, the information value of each sensor information can be judged based on the parameter judgment value corresponding to the equipment type of the distribution equipment, and then the weighted sum and average are taken to obtain the working possibility of the parameters corresponding to the distribution equipment, so as to facilitate subsequent accurate safety predictions, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception predictions, and improve the monitoring effect and safety level of the distribution room.

[0042] As an optional embodiment, in the above step, determining the time working possibility corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point includes: Determine at least one working time period corresponding to the power distribution equipment according to the preset correspondence between the equipment type and the working time interval, as well as the equipment type; Determine whether the current time point is within any working time period, and if so, determine that the time working possibility corresponding to the power distribution equipment is a first positive value; If not, calculate the minimum time difference between the current time point and the interval boundary point of any working time period; The time working possibility corresponding to the power distribution equipment is determined to be 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.

[0043] It can be seen that through the above-mentioned optional embodiments, the information value of each sensor information can be judged based on the parameter judgment value corresponding to the equipment type of the distribution equipment, and then the weighted sum and average are taken to obtain the working possibility of the parameters corresponding to the distribution equipment, so as to facilitate subsequent accurate safety predictions, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception predictions, and improve the monitoring effect and safety level of the distribution room.

[0044] As an optional embodiment, in the above steps, determining the working circuit information corresponding to any plurality of working devices based on a preset device working circuit rule includes: According to the preset work task table, determine multiple work tasks corresponding to the current time point; For each work task, determine the task work equipment set and work circuit corresponding to the work task; Determine whether the set formed by all working devices includes the task working device set, and if so, determine that the working loop corresponding to the working task is the target working loop; optionally, the target working loop includes device information of multiple working devices that form a continuous relationship in the work process; All target working loops are determined as working loop information.

[0045] It can be seen that through the above-mentioned optional embodiments, the working circuit of the work task being executed can be determined based on the equipment set of the work task and the current working equipment information, so as to facilitate subsequent accurate safety predictions, assist in fully combining the working circuits between the distribution room equipment to achieve more accurate and precise safety perception predictions, and improve the monitoring effect and safety level of the distribution room.

[0046] As an optional embodiment, in the above steps, based on the prediction neural network corresponding to the working loop information, determining the regional abnormality parameters corresponding to the target distribution room area according to the device sensor information includes: For each target working loop, determining a prediction neural network corresponding to the target working loop; optionally, the prediction neural network is trained by a training data set including sensor information of training devices corresponding to a plurality of corresponding working loops and working abnormality annotations; Inputting the sensor information of the device information corresponding to the target working circuit among all the device sensor information into the prediction neural network to obtain the abnormal parameters corresponding to the target working circuit; Calculate the weighted sum average of the abnormal parameters corresponding to all target working circuits to obtain the regional abnormal parameters corresponding to the target distribution room area; optionally, the weight of the weighted calculation of the abnormal parameters corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the amount of equipment information in the corresponding target working circuit; the second weight is proportional to the historical abnormality rate corresponding to the corresponding target working circuit; When the regional abnormal parameter is greater than the preset second parameter threshold, an abnormal warning is issued to the target distribution room area.

[0047] It can be seen that through the above-mentioned optional embodiments, it is possible to make anomaly predictions based on the device sensor information of the equipment in the target working loop according to the predictive neural network corresponding to the target working loop, and perform weighted calculations based on the corresponding weight rules to obtain more accurate anomaly characterization parameters, thereby fully combining the working loops between the equipment in the distribution room to achieve more accurate and precise safety perception predictions, thereby improving the monitoring effect and safety level of the distribution room.

[0048] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of a power distribution room safety perception system based on multi-sensor information disclosed in an embodiment of the present invention. Figure 2 The 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). Figure 2 As shown, the power distribution room safety perception system based on multi-sensor information may include: The acquisition module 201 is used to acquire device sensor information of multiple power distribution devices in the target power distribution room area.

[0049] The screening module 202 is used to screen out a plurality of working devices from a plurality of power distribution devices based on device sensing information. The determination module 203 is used to determine the working circuit information corresponding to any number of working devices based on a preset device working circuit rule. The prediction module 204 is used to determine the regional abnormality parameters corresponding to the target power distribution room area based on the prediction neural network corresponding to the working circuit information and according to the equipment sensor information.

[0050] It can be seen that the above-mentioned embodiments of the invention can screen out multiple working devices from multiple distribution devices based on the equipment sensor information, and then determine the working circuit information corresponding to any multiple working devices according to the preset equipment working circuit rules, and predict the regional abnormal parameters according to the equipment sensor information based on the prediction neural network corresponding to the working circuit information, so as to fully combine the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, thereby improving the monitoring effect and safety level of the distribution room.

[0051] 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.

[0052] It can be seen that through the above optional embodiments, the content of the equipment sensor information is limited to comprehensively characterize the status characteristics of the distribution equipment, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0053] As an optional embodiment, the specific manner in which the screening module screens out multiple working devices from multiple power distribution devices based on the device sensor information includes: For each power distribution device, determine the device type corresponding to the power distribution device; Determine at least one parameter judgment value corresponding to the device type based on a preset correspondence between the type and the parameter judgment value; According to the parameter judgment value, the device sensor information corresponding to the power distribution equipment is judged to obtain the parameter working possibility; Determine the time working possibility of the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point; Calculate the product of parameter working possibility and time working possibility to obtain the working possibility parameter corresponding to the power distribution equipment; Devices whose possible working parameters are greater than a preset first parameter threshold are screened out from a plurality of power distribution devices to obtain a plurality of working devices.

[0054] It can be seen that through the above-mentioned optional embodiments, the working possibility of the distribution equipment can be determined based on the parameter judgment value corresponding to the equipment type and the regular working time, so as to screen out more accurate working equipment, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0055] As an optional embodiment, the equipment type is transformer equipment, low-voltage switch cabinet equipment, capacitor cabinet equipment, medium-voltage incoming line equipment, low-voltage outgoing line equipment or basic support equipment.

[0056] It can be seen that through the above optional embodiments, the equipment type of the distribution equipment is limited to comprehensively characterize the type characteristics of the distribution equipment, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception prediction, and improve the monitoring effect and safety level of the distribution room.

[0057] As an optional embodiment, the screening module judges the device sensor information corresponding to the power distribution device according to the parameter judgment value, and obtains the specific method of the parameter working possibility, including: For each piece of sensor information in the device sensor information, determining a parameter judgment value corresponding to the sensor information; Calculate 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; optionally, the parameter difference includes a positive or negative sign; The weighted sum average of the parameter differences of all sensor information in the device sensor information is calculated to obtain the parameter working possibility corresponding to the power distribution equipment.

[0058] It can be seen that through the above-mentioned optional embodiments, the information value of each sensor information can be judged based on the parameter judgment value corresponding to the equipment type of the distribution equipment, and then the weighted sum and average are taken to obtain the working possibility of the parameters corresponding to the distribution equipment, so as to facilitate subsequent accurate safety predictions, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception predictions, and improve the monitoring effect and safety level of the distribution room.

[0059] As an optional embodiment, the screening module determines the specific manner of the possibility of the time operation corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point, including: Determine at least one working time period corresponding to the power distribution equipment according to the preset correspondence between the equipment type and the working time interval, as well as the equipment type; Determine whether the current time point is within any working time period, and if so, determine that the time working possibility corresponding to the power distribution equipment is a first positive value; If not, calculate the minimum time difference between the current time point and the interval boundary point of any working time period; The time working possibility corresponding to the power distribution equipment is determined to be 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] It can be seen that through the above-mentioned optional embodiments, the information value of each sensor information can be judged based on the parameter judgment value corresponding to the equipment type of the distribution equipment, and then the weighted sum and average are taken to obtain the working possibility of the parameters corresponding to the distribution equipment, so as to facilitate subsequent accurate safety predictions, assist in fully combining the working circuits between the equipment in the distribution room to achieve more accurate and precise safety perception predictions, and improve the monitoring effect and safety level of the distribution room.

[0061] As an optional embodiment, the specific manner in which the determination module determines the working circuit information corresponding to any plurality of working devices based on a preset device working circuit rule includes: According to the preset work task table, determine multiple work tasks corresponding to the current time point; For each work task, determine the task work equipment set and work circuit corresponding to the work task; Determine whether the set formed by all working devices includes the task working device set, and if so, determine that the working loop corresponding to the working task is the target working loop; optionally, the target working loop includes device information of multiple working devices that form a continuous relationship in the work process; All target working loops are determined as working loop information.

[0062] It can be seen that through the above-mentioned optional embodiments, the working circuit of the work task being executed can be determined based on the equipment set of the work task and the current working equipment information, so as to facilitate subsequent accurate safety predictions, assist in fully combining the working circuits between the distribution room equipment to achieve more accurate and precise safety perception predictions, and improve the monitoring effect and safety level of the distribution room.

[0063] As an optional embodiment, the prediction module determines the specific method of the regional abnormal parameters corresponding to the target power distribution room area based on the prediction neural network corresponding to the working loop information and the device sensor information, including: For each target working loop, determining a prediction neural network corresponding to the target working loop; optionally, the prediction neural network is trained by a training data set including sensor information of training devices corresponding to a plurality of corresponding working loops and working abnormality annotations; Inputting the sensor information of the device information corresponding to the target working circuit among all the device sensor information into the prediction neural network to obtain the abnormal parameters corresponding to the target working circuit; Calculate the weighted sum average of the abnormal parameters corresponding to all target working circuits to obtain the regional abnormal parameters corresponding to the target distribution room area; optionally, the weight of the weighted calculation of the abnormal parameters corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the amount of equipment information in the corresponding target working circuit; the second weight is proportional to the historical abnormality rate corresponding to the corresponding target working circuit; When the regional abnormal parameter is greater than the preset second parameter threshold, an abnormal warning is issued to the target distribution room area.

[0064] It can be seen that through the above-mentioned optional embodiments, it is possible to make anomaly predictions based on the device sensor information of the equipment in the target working loop according to the predictive neural network corresponding to the target working loop, and perform weighted calculations based on the corresponding weight rules to obtain more accurate anomaly characterization parameters, thereby fully combining the working loops between the equipment in the distribution room to achieve more accurate and precise safety perception predictions, thereby improving the monitoring effect and safety level of the distribution room.

[0065] Embodiment 3 See also Figure 3 , Figure 3 This is another power distribution room safety perception system based on multi-sensor information disclosed in an embodiment 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). Figure 3 As shown, the power distribution room safety perception system based on multi-sensor information may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; 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 based on multi-sensor information described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the power distribution room safety perception method based on multi-sensor information described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the distribution room safety perception method based on multi-sensor information described in Example 1.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may 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.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may 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 may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the distribution room safety perception method and system based on multi-sensor information disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, 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 various embodiments of the present invention.

Claims

1. A power distribution room safety perception method based on multi-sensor information, characterized in that: The method comprises: Acquire the device sensor information of multiple power distribution devices in the target power distribution room area; Based on the device sensing information, screening out a plurality of working devices from the plurality of power distribution devices; Based on a preset equipment working circuit rule, determine working circuit information corresponding to any plurality of the working equipment; 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.

2. The method for power distribution room safety perception based on multi-sensor information according to claim 1 is 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 method for power distribution room safety perception based on multi-sensor information according to claim 1 is characterized in that: The step of selecting a plurality of working devices from the plurality of power distribution devices based on the device sensing information comprises: For each of the power distribution devices, determining a device type corresponding to the power distribution device; Determine at least one parameter judgment value corresponding to the device type based on a preset correspondence between the type and the parameter judgment value; According to the parameter judgment value, the device sensor information corresponding to the power distribution device is judged to obtain the parameter working possibility; Determine the time working possibility corresponding to the power distribution equipment according to the regular working time corresponding to the equipment type and the current time point; Calculate the product of the parameter working possibility and the time working possibility to obtain the working possibility parameter corresponding to the power distribution equipment; Devices whose possible working parameters are greater than a preset first parameter threshold are screened out from the multiple power distribution devices to obtain multiple working devices.

4. The method for power distribution room safety perception based on multi-sensor information according to claim 3 is characterized in that: The equipment type is transformer equipment, low-voltage switchgear equipment, capacitor cabinet equipment, medium-voltage incoming line equipment, low-voltage outgoing line equipment or basic support equipment.

5. The method for power distribution room safety perception based on multi-sensor information according to claim 3 is characterized in that: The step of judging the device sensor information corresponding to the power distribution device according to the parameter judgment value to obtain the parameter working possibility includes: For each piece of sensor information in the device sensor information, determining the parameter judgment value corresponding to the sensor information; The information value of the sensing information is calculated to be different from the corresponding parameter judgment value to obtain the parameter difference corresponding to the sensing information; the parameter difference includes a positive and negative sign; The weighted sum average of the parameter differences of all the sensor information in the device sensor information is calculated to obtain the parameter working possibility corresponding to the power distribution equipment.

6. The method for power distribution room safety perception based on multi-sensor information according to claim 3 is characterized in that: The determining, according to the regular working time corresponding to the equipment type and the current time point, the time working possibility corresponding to the power distribution equipment includes: Determine at least one working time period corresponding to the power distribution equipment according to the preset correspondence between the equipment type and the working time interval, and the equipment type; Determine whether the current time point is within any of the working time periods, and if so, determine that the time working possibility corresponding to the power distribution equipment is a first positive value; If not, calculate the minimum time difference between the current time point and any boundary point of the working time period; 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.

7. The method for power distribution room safety perception based on multi-sensor information according to claim 1 is characterized in that: The determining of working circuit information corresponding to any plurality of working devices based on a preset device working circuit rule includes: According to the preset work task table, determine multiple work tasks corresponding to the current time point; For each work task, determine the task work equipment set and work circuit corresponding to the work task; Determine whether the set formed by all the working devices includes the task working device set, and if so, determine that the working loop corresponding to the working task is the target working loop; the target working loop includes device information of multiple working devices that form a continuous relationship in the work process; All the target working loops are determined as working loop information.

8. The method for power distribution room safety perception based on multi-sensor information according to claim 7 is characterized in that: The predictive neural network corresponding to the working circuit information determines the regional abnormality parameters corresponding to the target power distribution room area according to the device sensor information, including: For each of the target working loops, a prediction neural network corresponding to the target working loop is determined; the prediction neural network is trained by a training data set including sensor information of training devices corresponding to a plurality of corresponding working loops and working abnormality annotations; Inputting the sensor information of the device information corresponding to the target working circuit among all the device sensor information into the prediction neural network to obtain the abnormal parameters corresponding to the target working circuit; Calculate the weighted sum average of the abnormal parameters corresponding to all the target working circuits to obtain the regional abnormal parameters corresponding to the target distribution room area; wherein the weighted calculation weight of the abnormal parameters corresponding to each target working circuit is the product of the first weight and the second weight; the first weight is proportional to the amount of device information in the corresponding target working circuit; the second weight is proportional to the historical abnormality rate corresponding to the corresponding target working circuit; When the abnormal parameter of the area is greater than a preset second parameter threshold, an abnormal warning is issued to the target power distribution room area.

9. A power distribution room safety perception system based on multi-sensor information, characterized in that: The system comprises: An acquisition module is used to acquire device sensor information of multiple power distribution devices in the target power distribution room area; A screening module, used for screening a plurality of working devices from the plurality of power distribution devices based on the device sensing information; A determination module, used to determine working circuit information corresponding to any plurality of working devices based on a preset device working circuit rule; A prediction module is used to determine the regional abnormality parameters corresponding to the target distribution room area based on the prediction neural network corresponding to the working loop information and according to the device sensor information.

10. A power distribution room safety perception system based on multi-sensor information, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the power distribution room safety perception method based on multi-sensor information as described in any one of claims 1-8.

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