A monitoring method and system for power distribution room based on artificial intelligence and Internet of Things technology

Abstract

The present invention provides a distribution room monitoring method based on artificial intelligence Internet of Things technology, which belongs to the field of risk monitoring technology, and specifically comprises: based on the analysis results of the Internet of Things monitoring device of the distribution switch equipment in the distribution room, determining the historical load data of different distribution switch equipment, and using the historical load data of the distribution switch equipment to determine the risk switch equipment in the distribution switch equipment; based on the distribution data of the risk switch equipment in the distribution room, when it is determined that the safety risk of the distribution room meets the requirements, different distribution switch equipment operation risk time periods are determined based on the historical load data of different distribution switch equipment; according to the matching results of the operation risk time periods of the distribution switch equipment in different time periods, an artificial intelligence model is used to determine the online monitoring strategy of the fire-fighting equipment in different time periods, thereby improving the reliability of the monitoring and processing of the fire-fighting equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution rooms, and in particular, relates to a power distribution room monitoring method and system based on artificial intelligence Internet of Things technology. Background Art

[0002] In order to realize the safety monitoring of the power distribution room, the invention patent application CN201120551891.1 "Centralized data acquisition terminal and monitoring system" uses a centralized data acquisition terminal and a monitoring computer, which not only reduces the labor intensity of the staff in the power communication room, but also improves the monitoring efficiency and the safety of the monitoring process. However, there are the following technical problems:

[0003] When conducting safety monitoring of the distribution room, there are huge differences in the distribution data of the distribution switchgear in the distribution room. Therefore, if it is not possible to determine differentiated safety monitoring strategies for the fire-fighting equipment in the distribution room based on the distribution of the distribution switchgear in the distribution room, once a safety risk occurs, if there is an abnormality in the operating status of the fire-fighting equipment, the safety of the distribution room cannot be guaranteed.

[0004] In response to the above technical problems, the present application specifically provides a distribution room monitoring method and system based on artificial intelligence Internet of Things technology. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a method for monitoring a power distribution room based on artificial intelligence Internet of Things technology is provided.

[0007] A distribution room monitoring method based on artificial intelligence Internet of Things technology, specifically comprising:

[0008] S1 obtains the power distribution circuit of the fire-fighting equipment in the power distribution room, and proceeds to the next step when it is determined that the power supply reliability of the fire-fighting equipment meets the requirements based on the temperature monitoring data of the power distribution copper busbar of the power distribution circuit;

[0009] S2 determines the historical load data of different distribution switchgear based on the analysis result of the Internet of Things monitoring device of the distribution switchgear in the distribution room, and determines the risky switchgear among the distribution switchgear by using the historical load data of the distribution switchgear;

[0010] S3 determines the operation risk periods of different distribution switchgear based on the distribution data of the risk switchgear in the distribution room and determines that the safety risk of the distribution room meets the requirements based on the historical load data of different distribution switchgear;

[0011] S4 uses artificial intelligence models to determine the online monitoring strategies for fire-fighting equipment in different time periods based on the matching results of the operating risk periods of distribution switchgear in different time periods.

[0012] The beneficial effects of the present invention are:

[0013] The temperature monitoring data of the distribution copper busbars in the distribution circuit is used to determine whether the power supply reliability of the fire-fighting equipment meets the requirements, thereby realizing the evaluation of the power supply reliability of the fire-fighting equipment from the perspective of the temperature monitoring data of the distribution copper busbars in the distribution circuit of the fire-fighting equipment. It also lays the foundation for determining differentiated online monitoring strategies based on differences in power supply reliability, thereby improving the reliability of the monitoring and processing of the fire-fighting equipment.

[0014] According to the matching results of the operation risk periods of the distribution switch equipment in different time periods, the online monitoring strategies of the fire-fighting equipment in different time periods are determined, and the number of distribution switch equipment with operation risks in different time periods is realized, and differentiated online monitoring strategies are determined, which not only ensures the reliability of the monitoring and processing of the fire-fighting equipment, but also avoids the technical problem of excessive difficulty in data processing of the monitoring and processing due to frequent monitoring and processing of the fire-fighting equipment.

[0015] A further technical solution is that the fire-fighting equipment includes smoke monitoring equipment, automatic fire alarm equipment, automatic water sprinkler fire extinguishing equipment, and fire hydrant equipment.

[0016] A further technical solution is that the temperature monitoring data includes temperature monitoring values ​​within a preset time period.

[0017] A further technical solution is to determine whether the power supply reliability of the fire-fighting equipment meets the requirements, specifically including:

[0018] Determine the temperature monitoring value of the distribution copper busbar at different times within a preset time period based on the temperature monitoring data of the distribution copper busbar of the distribution circuit;

[0019] Determine the abnormal moment of temperature monitoring of the distribution copper busbar within a preset time period by using the temperature monitoring quantity;

[0020] The power supply abnormality coefficient of the fire-fighting equipment is determined based on the proportion of the number of abnormal moments in the temperature monitoring, and the power supply abnormality coefficient is used to determine whether the power supply reliability of the fire-fighting equipment meets the requirements.

[0021] A further technical solution is that the abnormal temperature monitoring moment is the moment when the temperature monitoring value is greater than a preset monitoring value.

[0022] A further technical solution is that when the power supply abnormality coefficient is greater than a preset abnormality coefficient threshold, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements.

[0023] A further technical solution is that the method for determining the operation risk period of the power distribution switchgear is:

[0024] Determining the load amount of the time period on different dates according to the historical load data of the power distribution switch device;

[0025] Determining an average load value for the period based on an average load value on different days for the period;

[0026] Whether the period is an operation risk period is determined based on the load average value of the period.

[0027] A further technical solution is that when the average load value of the time period is greater than a preset load threshold, the time period is determined to be an operation risk period.

[0028] A further technical solution is that the method for determining the online monitoring strategy of the fire-fighting equipment in the period is:

[0029] Based on the matching result of the operation risk period of the power distribution switchgear in the period, determine the power distribution switchgear in the operation risk period in the period and use it as the risk matching device;

[0030] According to the number of risk-matching devices in the time period, an artificial intelligence model is used to determine the matching coefficient between the time period and different online monitoring strategies, and the online monitoring strategy of the fire-fighting equipment is determined using the matching coefficient.

[0031] A further technical solution is that the artificial intelligence model is constructed using any one or more of a BP neural network, a LSTM neural network and a CNN neural network.

[0032] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned distribution room monitoring method based on artificial intelligence Internet of Things technology when running the computer program.

[0033] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0036] Figure 1 It is a flow chart of a method for monitoring a power distribution room based on artificial intelligence and Internet of Things technology;

[0037] Figure 2 It is a flow chart to determine whether the power supply reliability of fire-fighting equipment meets the requirements;

[0038] Figure 3 is a flow chart of a method for determining risky switchgear in a power distribution switchgear;

[0039] Figure 4 It is a flow chart to determine whether the safety risk of the distribution room meets the requirements;

[0040] Figure 5 The present invention is a flow chart of a method for determining an operation risk period of a power distribution switchgear. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0042] When conducting safety monitoring of the distribution room, it is necessary to determine differentiated safety monitoring strategies for the fire-fighting equipment in the distribution room according to the distribution of the distribution circuit breakers in the distribution room, so as to ensure the reliability of the monitoring and processing of the fire-fighting equipment.

[0043] Determine whether the power supply reliability of fire-fighting equipment meets the requirements: Use temperature monitoring data to determine the abnormal temperature monitoring moments of the distribution copper busbar within the preset time period, and determine the power supply abnormality coefficient of the fire-fighting equipment based on the proportion of the number of abnormal temperature monitoring moments. When the power supply abnormality coefficient is less than 0.3, it is determined that the power supply reliability of the fire-fighting equipment meets the requirements.

[0044] Risky switchgear in distribution switchgear: Use the ratio of the load at different times to the rated load of the distribution switchgear to determine the high-load moments on different dates, and use the number of high-load moments to determine the high-load dates in the dates. When the proportion of high-load dates is greater than 0.5, it is determined that the distribution switchgear is a risky switchgear.

[0045] Operation risk period of the distribution switch equipment: when the average load value of the period is greater than a preset load threshold, the period is determined to be an operation risk period.

[0046] Online monitoring strategies for fire-fighting equipment in different time periods: The distribution switchgear that is in the operation risk period in the time period is used as the risk matching device. According to the number of risk matching devices in the time period, the artificial intelligence model is used to determine the matching coefficient between the time period and different online monitoring strategies, and the matching coefficient is used to determine the online monitoring strategy of the fire-fighting equipment.

[0047] The artificial intelligence model is constructed using any one or more of the BP neural network, LSTM neural network and CNN neural network.

[0048] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a distribution room monitoring method based on artificial intelligence Internet of Things technology is provided, which specifically includes:

[0049] S1 obtains the power distribution circuit of the fire-fighting equipment in the power distribution room, and proceeds to the next step when it is determined that the power supply reliability of the fire-fighting equipment meets the requirements based on the temperature monitoring data of the power distribution copper busbar of the power distribution circuit;

[0050] Furthermore, the fire-fighting equipment includes smoke monitoring equipment, automatic fire alarm equipment, automatic water sprinkler fire extinguishing equipment, and fire hydrant equipment.

[0051] Optionally, the temperature monitoring data includes temperature monitoring values ​​within a preset time period.

[0052] It should be noted that if Figure 2 As shown, determining that the power supply reliability of the fire-fighting equipment meets the requirements specifically includes:

[0053] Determine the temperature monitoring value of the distribution copper busbar at different times within a preset time period based on the temperature monitoring data of the distribution copper busbar of the distribution circuit;

[0054] Determine the abnormal moment of temperature monitoring of the distribution copper busbar within a preset time period by using the temperature monitoring quantity;

[0055] The power supply abnormality coefficient of the fire-fighting equipment is determined based on the proportion of the number of abnormal moments in the temperature monitoring, and the power supply abnormality coefficient is used to determine whether the power supply reliability of the fire-fighting equipment meets the requirements.

[0056] Optionally, the temperature monitoring abnormal moment is the moment when the temperature monitoring value is greater than a preset monitoring value.

[0057] It should be noted that when the power supply abnormality coefficient is greater than a preset abnormality coefficient threshold, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements.

[0058] In another embodiment, determining that the power supply reliability of the fire-fighting equipment meets the requirements specifically includes:

[0059] Determine the temperature monitoring value of the distribution copper busbar at different times within a preset time period based on the temperature monitoring data of the distribution copper busbar of the distribution circuit;

[0060] Determine the average temperature of the fire-fighting equipment by using the average value of the temperature monitoring values ​​at different times;

[0061] Based on the average temperature of the fire-fighting equipment, it is determined whether the power supply reliability of the fire-fighting equipment meets the requirements.

[0062] Furthermore, when the power supply reliability of the fire-fighting equipment does not meet the requirements, the online status of the fire-fighting equipment is monitored using a preset monitoring frequency.

[0063] Optionally, determining whether the power supply reliability of the fire-fighting equipment meets the requirements specifically includes:

[0064] S11 determines the temperature monitoring value of the distribution copper busbar at different times within a preset period of time based on the temperature monitoring data of the distribution copper busbar of the distribution circuit, and determines the abnormal time of the temperature monitoring of the distribution copper busbar within the preset period of time using the temperature monitoring value;

[0065] Optionally, the above step S11 includes the following contents:

[0066] S111 determines the temperature monitoring amount of the distribution copper busbar at different times within a preset period of time using the temperature monitoring data of the distribution copper busbar of the distribution circuit. When it is determined by using the temperature monitoring amount that there is no abnormal temperature monitoring moment within the preset period of time for the distribution copper busbar, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements. When it is determined by using the temperature monitoring amount that there is an abnormal temperature monitoring moment within the preset period of time for the distribution copper busbar, the process proceeds to step S112.

[0067] S112 obtains the number of abnormal temperature monitoring moments within a preset period. When the number of abnormal temperature monitoring moments is greater than the preset number of abnormal moments, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements. When the number of abnormal temperature monitoring moments is not greater than the preset number of abnormal moments, the process proceeds to step S113.

[0068] S113 determines the preset abnormality coefficients corresponding to different temperature monitoring abnormality moments based on the temperature monitoring values ​​at different temperature monitoring abnormality moments within a preset time period. When the sum of the preset abnormality coefficients corresponding to different temperature monitoring abnormality moments does not meet the requirements, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements. When the sum of the preset abnormality coefficients corresponding to different temperature monitoring abnormality moments meets the requirements, proceed to step S12.

[0069] S12 counts the number of temperature monitoring abnormal moments in different unit time periods within a preset period according to the distribution data of different temperature monitoring abnormal moments, and determines the temperature abnormality coefficients of different unit time periods in combination with the temperature monitoring amounts of different temperature monitoring abnormal moments, and determines the temperature monitoring abnormal period using the temperature abnormality coefficients;

[0070] Optionally, the above step S12 includes the following contents:

[0071] S121 calculates the number of abnormal temperature monitoring moments in different unit time periods within a preset period according to the distribution data of different abnormal temperature monitoring moments. When there is a unit time period in which the number of abnormal temperature monitoring moments does not meet the requirements, the process proceeds to step S122. When there is no unit time period in which the number of abnormal temperature monitoring moments does not meet the requirements, it is determined that the power supply reliability of the fire-fighting equipment meets the requirements.

[0072] S122: when the number of unit time periods in which the number of abnormal temperature monitoring moments does not meet the requirement is greater than the number of preset time periods, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirement; when the number of unit time periods in which the number of abnormal temperature monitoring moments does not meet the requirement is not greater than the number of preset time periods, the process proceeds to step S123;

[0073] S123 determines the temperature anomaly coefficients of different unit time periods based on the number of temperature monitoring abnormal moments in different unit time periods within a preset time period and the temperature monitoring amounts at different temperature monitoring abnormal moments, and uses the temperature anomaly coefficients to determine the temperature monitoring abnormal time period. When the number of the temperature monitoring abnormal time periods does not meet the requirements, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements. When the number of the temperature monitoring abnormal time periods meets the requirements, proceed to step S13.

[0074] S13 determines the power supply abnormality coefficient of the fire-fighting equipment based on the temperature monitoring abnormality period within the preset period and the temperature abnormality coefficients of different temperature monitoring abnormality periods, and uses the power supply abnormality coefficient to determine whether the power supply reliability of the fire-fighting equipment meets the requirements.

[0075] S2 determines the historical load data of different distribution switchgear based on the analysis result of the Internet of Things monitoring device of the distribution switchgear in the distribution room, and determines the risky switchgear among the distribution switchgear by using the historical load data of the distribution switchgear;

[0076] Furthermore, the historical load data of the distribution switch device is determined based on monitoring data of load current and load voltage of an Internet of Things monitoring device of the distribution switch device.

[0077] Specifically, Figure 3 As shown, the method for determining the risk switchgear in the power distribution switchgear is:

[0078] Determining the load of the power distribution switch device at different times based on the historical load data of the power distribution switch device;

[0079] Determine high-load moments on different dates according to the ratio of the load at different moments on different dates to the rated load of the power distribution switch device, and determine the high-load date on the date by using the number of the high-load moments;

[0080] Whether the power distribution switchgear is a risky switchgear is determined based on the number ratio of the high-load days.

[0081] Furthermore, the high load moment is a moment when the ratio of the load to the rated load of the power distribution switch device is greater than a preset load ratio.

[0082] Optionally, the high-load date is a date on which the number of high-load moments is greater than a preset number of moments.

[0083] It should be noted that when the proportion of the number of high-load dates is greater than the proportion of the number of preset dates, it is determined that the distribution switch device is a risky switch device.

[0084] Optionally, the method for determining the risky switchgear in the power distribution switchgear is:

[0085] Based on the historical load data of the power distribution switch device, determine the load of the power distribution switch device at different times, and determine the daily average load of the power distribution switch device according to the load at different times on different dates;

[0086] Determining a load proportional factor of the power distribution switch device by using a ratio of the daily average load to the rated load of the power distribution switch device;

[0087] It is determined whether the power distribution switchgear is a risk switchgear based on a load proportional factor of the power distribution switchgear.

[0088] Further, when the load proportional factor of the power distribution switch device is greater than a preset proportional factor threshold, the power distribution switch device is determined to be a risky switch device.

[0089] It can be understood that the method for determining the risky switchgear in the power distribution switchgear is:

[0090] S21 determines the load of the distribution switch device at different times based on the historical load data of the distribution switch device, determines the load proportional factors at different times according to the ratios of the load at different times on different dates to the rated load of the distribution switch device, and determines the high load moments on different dates using the load proportional factors;

[0091] S22 determines the load factors of different dates according to the number of high load moments in different dates and the load proportional factors of different high load moments;

[0092] S23 determines the risk factor of the power distribution switch device using the load factors on different dates, and determines whether the power distribution switch device is a risky switch device using the risk factor.

[0093] S3 determines the operation risk periods of different distribution switchgear based on the distribution data of the risk switchgear in the distribution room and determines that the safety risk of the distribution room meets the requirements based on the historical load data of different distribution switchgear;

[0094] Specifically, Figure 4 As shown, it is determined that the safety risk of the power distribution room meets the requirements, specifically including:

[0095] Based on the distribution data of the risk switchgear in the power distribution room, determining the number of risk switchgear in the power distribution room, and determining the basic risk coefficient of the power distribution room by using the number of risk switchgear;

[0096] Based on the risk switchgear distribution data in the power distribution room, determine the number of risk switchgear in different divided areas in the power distribution room, and determine the risk equipment aggregation coefficient of different divided areas in the power distribution room by using the number of risk switchgear in different divided areas and the proportion of the number of switchgear in the divided areas;

[0097] The safety risk coefficient of the distribution room is determined by summing the average value of the risk equipment aggregation coefficients of different divided areas and the basic risk coefficient of the distribution room, and the safety risk coefficient is used to determine whether the safety risk of the distribution room meets the requirements.

[0098] Furthermore, the basic risk coefficient of the power distribution room is determined according to the ratio of the number of risk switch devices in the power distribution room to the number of preset switch devices.

[0099] It should be noted that the method for determining the risk equipment clustering coefficient of the divided area is:

[0100] Determine a preset risk aggregation coefficient of the divided area under the number of risk switch devices by using the number of risk switch devices in the divided area;

[0101] The risk equipment aggregation coefficient of the divided area is determined by multiplying the preset risk aggregation coefficient by the proportion of the number of switch devices in the divided area.

[0102] Optionally, when the safety risk coefficient of the power distribution room is greater than a preset risk coefficient threshold, it is determined that the safety risk of the power distribution room does not meet the requirements.

[0103] Specifically, when the safety risk of the power distribution room does not meet the requirements, the online status of the fire-fighting equipment is monitored using a preset monitoring frequency.

[0104] In another embodiment, determining that the safety risk of the power distribution room meets the requirements specifically includes:

[0105] Based on the distribution data of the risk switchgear in the power distribution room, the number of risk switchgear in the power distribution room is determined, and when the number of risk switchgear in the power distribution room does not meet the requirement, it is determined that the safety risk of the power distribution room does not meet the requirement;

[0106] When the number of risk switchgear in the power distribution room meets the requirements:

[0107] The basic risk coefficient of the power distribution room is determined by using the number of risky switchgear and the proportion of the number of switchgear in the power distribution room. When the basic risk coefficient of the power distribution room is within a preset risk coefficient range:

[0108] It is determined that the safety risk of the power distribution room meets the requirements;

[0109] When the basic risk factor of the power distribution room is not within the preset risk factor range:

[0110] Based on the risk switchgear distribution data in the power distribution room, determining the number of risk switchgear in different divided areas in the power distribution room, when the number of divided areas in which the number of risk switchgear does not meet the requirement is greater than the preset number of divided areas, determining that the safety risk of the power distribution room does not meet the requirement;

[0111] When the number of risk switchgear does not meet the requirement and the number of division areas is not greater than the preset number of division areas:

[0112] Determine the risk equipment clustering coefficient of different divided areas in the power distribution room by using the number of risk switchgear in different divided areas and the proportion of the number of switchgear in the divided areas; when the number of divided areas whose regional equipment clustering coefficient is greater than the preset clustering coefficient is greater than the preset number of divided areas, it is determined that the safety risk of the power distribution room does not meet the requirements;

[0113] When the number of divided areas where the regional device clustering coefficient is greater than the preset clustering coefficient is not greater than the preset number of divided areas:

[0114] The safety risk coefficient of the distribution room is determined by summing the average value of the risk equipment aggregation coefficients of different divided areas and the basic risk coefficient of the distribution room, and the safety risk coefficient is used to determine whether the safety risk of the distribution room meets the requirements.

[0115] S4 uses artificial intelligence models to determine the online monitoring strategies for fire-fighting equipment in different time periods based on the matching results of the operating risk periods of distribution switchgear in different time periods.

[0116] Specifically, Figure 5 As shown, the method for determining the operation risk period of the distribution switchgear is:

[0117] Determining the load amount of the time period on different dates according to the historical load data of the power distribution switch device;

[0118] Determining an average load value for the period based on an average load value on different days for the period;

[0119] Whether the period is an operation risk period is determined based on the load average value of the period.

[0120] Furthermore, when the load average value of the time period is greater than a preset load threshold, the time period is determined to be an operation risk period.

[0121] It should be noted that the method for determining the online monitoring strategy of fire-fighting equipment in the period is:

[0122] Based on the matching result of the operation risk period of the power distribution switchgear in the period, determine the power distribution switchgear in the operation risk period in the period and use it as the risk matching device;

[0123] According to the number of risk-matching devices in the time period, an artificial intelligence model is used to determine the matching coefficient between the time period and different online monitoring strategies, and the online monitoring strategy of the fire-fighting equipment is determined using the matching coefficient.

[0124] Furthermore, the artificial intelligence model is constructed using any one or more of a BP neural network, a LSTM neural network, and a CNN neural network.

[0125] Optionally, the method for determining the online monitoring strategy of the fire-fighting equipment in the period is:

[0126] Based on the matching result of the operation risk period of the power distribution switchgear in the period, determine the power distribution switchgear in the operation risk period in the period and use it as the risk matching device;

[0127] An online monitoring strategy for fire-fighting equipment in the time period is determined according to the proportion of the number of risk-matching devices in the power distribution room in the time period.

[0128] It should be noted that, according to the proportion of the number of risk matching devices in the power distribution room during the period, the online monitoring strategy of the fire-fighting equipment during the period is determined, which specifically includes:

[0129] When the proportion of the risk-matching devices in the power distribution room during the time period is greater than the preset proportion, the online status of the fire-fighting equipment is monitored using the preset monitoring frequency;

[0130] When the proportion of risk-matching devices in the distribution room during the time period is not greater than the preset proportion, the matching monitoring frequency is determined using the proportion in the distribution room and the preset proportional coefficient, and the online status of the fire-fighting equipment in the time period is monitored based on the matching monitoring frequency.

[0131] Embodiment 2 In a second aspect, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned distribution room monitoring method based on artificial intelligence Internet of Things technology when running the computer program.

[0132] Optionally, the above step S21 includes the following contents:

[0133] S211 determines the load of the distribution switch device at different times based on the historical load data of the distribution switch device, determines the load proportional factor at different times according to the ratio of the load at different times on different dates to the rated load of the distribution switch device, and determines that the distribution switch device does not belong to the risk switch device when the load proportional factor is used to determine that the distribution switch device does not have a high load moment. When the distribution switch device has a high load moment, proceed to step S212;

[0134] S212: obtaining the number of high load moments of the power distribution switch device. When the number of high load moments is greater than the preset number of load moments, determining that the power distribution switch device belongs to a risky switch device. When the number of high load moments is not greater than the preset number of load moments, proceeding to step S213.

[0135] S213, based on the number of high-load moments of the power distribution switch device on different dates, when it is determined that there is a date with a number of high-load moments greater than the preset number of moments, the process proceeds to step S214; when there is no date with a number of high-load moments greater than the preset number of moments, the process proceeds to step S22;

[0136] S214 When the number of high-load moments is greater than the preset number of moments and the number of dates is greater than the preset number of dates, it is determined that the distribution switch device belongs to a risky switch device. When the number of high-load moments is greater than the preset number of moments and the number of dates is not greater than the preset number of dates, go to step S22.

[0137] Optionally, the above step S22 includes the following contents:

[0138] S221 determines the load factors of different dates based on the number of high-load moments in different dates and the load proportional factors of different high-load moments. When there is a date with a load factor greater than a preset load factor, the process proceeds to step S223. When there is no date with a load factor greater than the preset load factor, the process proceeds to step S222.

[0139] S222: when the average value of the load coefficients on different dates is less than the preset coefficient threshold, it is determined that the distribution switchgear does not belong to the risk switchgear; when the average value of the load coefficients on different dates is not less than the preset coefficient threshold, it proceeds to step S23;

[0140] S223 obtains the date when the load factor is greater than the preset load factor, and uses it as the high-load date. When the number of high-load dates is greater than the preset number of high-load dates, it is determined that the distribution switch device does not belong to the risky switch device. When the number of high-load dates is not greater than the preset number of high-load dates, go to step S23.

[0141] 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 device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0142] The above is a description of a specific embodiment of the specification. 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 drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0143] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A distribution room monitoring method based on artificial intelligence Internet of Things technology, characterized in that: Specifically include: Obtain the power distribution circuit of the fire-fighting equipment in the power distribution room, and proceed to the next step when it is determined that the power supply reliability of the fire-fighting equipment meets the requirements based on the temperature monitoring data of the power distribution copper busbar of the power distribution circuit; Based on the analysis result of the Internet of Things monitoring device of the distribution switchgear in the distribution room, determine the historical load data of different distribution switchgears, and determine the risky switchgear among the distribution switchgears by using the historical load data of the distribution switchgears; Based on the distribution data of the risk switchgear in the power distribution room, when it is determined that the safety risk of the power distribution room meets the requirements, the operation risk periods of different power distribution switchgear are determined based on the historical load data of different power distribution switchgear; According to the matching results of the operation risk periods of the distribution switchgear in different time periods, the artificial intelligence model is used to determine the online monitoring strategy of the fire protection equipment in different time periods; The method for determining the online monitoring strategy of fire fighting equipment in the period is: Based on the matching result of the operation risk period of the power distribution switch equipment in the period, determine the power distribution switch equipment in the operation risk period in the period, and use it as the risk matching equipment; When the proportion of the number of risk-matching devices in the power distribution room in the time period is greater than the preset proportion, the online status of the fire-fighting equipment is monitored using the preset monitoring frequency; When the proportion of risk-matching devices in the distribution room during the time period is not greater than the preset proportion, the matching monitoring frequency is determined using the proportion in the distribution room and the preset proportional coefficient, and the online status of the fire-fighting equipment in the time period is monitored based on the matching monitoring frequency.

2. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 1 is characterized in that: The fire-fighting equipment includes smoke monitoring equipment, automatic fire alarm equipment, automatic water sprinkler fire extinguishing equipment, and fire hydrant equipment.

3. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 1 is characterized in that: The temperature monitoring data includes temperature monitoring values ​​within a preset period of time.

4. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 1 is characterized in that: Determine that the power supply reliability of the fire-fighting equipment meets the requirements, including: Determine the temperature monitoring value of the distribution copper busbar at different times within a preset time period based on the temperature monitoring data of the distribution copper busbar of the distribution circuit; Determine the abnormal moment of temperature monitoring of the distribution copper busbar within a preset time period by using the temperature monitoring quantity; The power supply abnormality coefficient of the fire-fighting equipment is determined based on the proportion of the number of abnormal moments in the temperature monitoring, and the power supply abnormality coefficient is used to determine whether the power supply reliability of the fire-fighting equipment meets the requirements.

5. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 4 is characterized in that: The temperature monitoring abnormal moment is the moment when the temperature monitoring value is greater than the preset monitoring value.

6. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 4 is characterized in that: When the power supply abnormality coefficient is greater than a preset abnormality coefficient threshold, it is determined that the power supply reliability of the fire-fighting equipment does not meet the requirements.

7. The power distribution room monitoring method based on artificial intelligence and Internet of Things technology as claimed in claim 1 is characterized in that: The historical load data of the distribution switch device is determined based on the monitoring data of the load current and load voltage of the Internet of Things monitoring device of the distribution switch device.

8. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 1 is characterized in that: The method for determining the risk switchgear in the power distribution switchgear is: Determining the load of the power distribution switch device at different times based on the historical load data of the power distribution switch device; Determine high-load moments on different dates according to the ratio of the load at different moments on different dates to the rated load of the power distribution switch device, and determine the high-load date on the date by using the number of the high-load moments; Whether the power distribution switchgear is a risky switchgear is determined based on the number ratio of the high-load days.

9. The power distribution room monitoring method based on artificial intelligence Internet of Things technology as claimed in claim 8 is characterized in that: The high load moment is the moment when the ratio of the load to the rated load of the power distribution switch device is greater than a preset load ratio.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a distribution room monitoring method based on artificial intelligence Internet of Things technology as described in any one of claims 1-9 is executed.

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