Multi-feature electrical fire risk assessment analysis method, device, equipment and medium
By calculating the characteristic risk index of the switching quantity and digital quantity data packets of the electrical fire monitoring system and analyzing the multi-character risk assessment and analysis, the problem of high false alarm rate in traditional systems is solved, the accuracy and timeliness of electrical fire monitoring are improved, and effective prevention of electrical fires is achieved.
Patent Information
- Application Number
- CN202510020107.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-06
- Publication Date
- 2025-04-25
AI Technical Summary
The traditional electrical fire monitoring system has a high false alarm rate, which cannot effectively realize multi-character electrical fire risk assessment and analysis, resulting in insufficient accuracy and timeliness of monitoring and ineffective prevention of electrical fires.
By obtaining the switching quantity data packets and actual digital quantity data packets that characterize the switching state, the characteristic risk index is calculated separately to determine whether it is greater than the preset characteristic threshold. If it is greater than, a multi-element risk assessment analysis is carried out, and a machine learning algorithm is used to build a risk assessment analysis model to generate a sequence in the form of tensors for analysis.
Multi-character electrical fire risk assessment and analysis are realized, false alarm situations are reduced, and the accuracy and timeliness of electrical fire monitoring are improved, thereby effectively preventing electrical fires.
Smart Images
Figure CN119886831A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of electrical fire risk analysis, and in particular to a multi-feature electrical fire risk assessment and analysis method, device, equipment and medium. Background Art
[0002] Traditional electrical fire monitoring systems lack network connectivity and rely solely on dashboards to display data. They use preset safety thresholds on the dashboard, and trigger alarms when the thresholds are exceeded. With the rapid development of IoT technology, many traditional instruments have been transformed and upgraded into smart devices, but their basic working principles have not changed fundamentally. They still adopt a single parameter monitoring and threshold over-limit alarm mode. This IoT-based improvement does, to a certain extent, realize remote real-time monitoring and alarm notification functions for equipment status, marking an important step towards intelligent management. However, it also exposes a significant flaw: the false alarm rate remains high. Since monitoring data often encounters instantaneous peaks, the system frequently hits the alarm threshold, resulting in frequent alarms. This not only consumes valuable emergency response resources, but may also cause a "wolf cry" effect, making security personnel complacent and reducing their sensitivity to real alarms and response efficiency in the long run.
[0003] As can be seen from the above, how to achieve multi-feature electrical fire risk assessment and analysis, improve the accuracy and timeliness of electrical fire monitoring, and thus effectively prevent electrical fires is a problem to be solved in this field. Summary of the invention
[0004] In view of this, the purpose of the present invention is to provide a multi-feature electrical fire risk assessment and analysis method, device, equipment and medium, which can realize multi-feature electrical fire risk assessment and analysis, improve the accuracy and timeliness of electrical fire monitoring, and effectively prevent electrical fires. The specific scheme is as follows:
[0005] In a first aspect, the present application discloses a multi-feature electrical fire risk assessment and analysis method, comprising:
[0006] Obtaining a switch quantity data packet representing a switch state and an actual digital quantity data packet;
[0007] Calculating the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index;
[0008] According to business needs, determine whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, perform multi-characteristic risk assessment analysis on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain the risk assessment analysis result of electrical fire.
[0009] Optionally, the calculating of characteristic risk indexes of the switch quantity data packet and the actual digital quantity data packet respectively includes:
[0010] The characteristic risk index of the switch quantity data packet and the actual digital quantity data packet is calculated respectively by using a preset Internet of Things gateway.
[0011] Optionally, calculating a characteristic risk index of the switch quantity data packet to obtain a switch quantity characteristic risk index includes:
[0012] Arranging the switch quantity data packets in a bitmap manner to obtain a bitmap, and determining a bitmap value;
[0013] A characteristic risk index is calculated for the bitmap value to obtain a switch value characteristic risk index.
[0014] Optionally, the calculating of the characteristic risk index of the bitmap value to obtain the switch characteristic risk index includes:
[0015] Determining a preset bitmap threshold;
[0016] The ratio between the bitmap value and the bitmap threshold is calculated, and the ratio is used as the switch quantity characteristic risk index.
[0017] Optionally, calculating a characteristic risk index of the actual digital quantity data packet to obtain the actual digital quantity characteristic risk index includes:
[0018] Calculating the data sum of the actual digital quantity data packets and determining the number of the actual digital quantity data packets;
[0019] The product between a preset digital quantity threshold and the quantity is calculated, and the ratio between the data sum and the product is used as the actual digital quantity characteristic risk index.
[0020] Optionally, the performing multi-feature risk assessment analysis on the switch quantity feature risk index and the actual digital quantity feature risk index includes:
[0021] Use machine learning algorithms to build risk assessment analysis models;
[0022] The risk assessment analysis model is used to perform multi-feature risk assessment analysis on the switch quantity feature risk index and the actual digital quantity feature risk index.
[0023] Optionally, the risk assessment analysis model is used to perform a multi-feature risk assessment analysis on the switch quantity feature risk index and the actual digital quantity feature risk index, including:
[0024] Generate a sequence in tensor form based on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index;
[0025] The sequence is input into the risk assessment analysis model for multi-feature risk assessment analysis.
[0026] In a second aspect, the present application discloses a multi-feature electrical fire risk assessment and analysis device, comprising:
[0027] A data packet acquisition module is used to acquire a switch quantity data packet representing a switch state and an actual digital quantity data packet;
[0028] A calculation module, used to calculate the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively, so as to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index;
[0029] The assessment and analysis module is used to determine whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than a preset characteristic threshold according to business needs. If the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, a multi-characteristic risk assessment and analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment and analysis result of an electrical fire.
[0030] In a third aspect, the present application discloses an electronic device, comprising:
[0031] Memory, used to store computer programs;
[0032] A processor is used to execute the computer program to implement the aforementioned multi-feature electrical fire risk assessment and analysis method.
[0033] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the multi-feature electrical fire risk assessment and analysis method disclosed above are implemented.
[0034] It can be seen that the present application provides a multi-feature electrical fire risk assessment and analysis method, including obtaining a switch quantity data packet and an actual digital quantity data packet representing a switch state; performing characteristic risk index calculations on the switch quantity data packet and the actual digital quantity data packet respectively to obtain a switch quantity characteristic risk index and an actual digital quantity characteristic risk index; judging whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than a preset characteristic threshold value according to business needs; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold value, performing a multi-feature risk assessment and analysis on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment and analysis result of an electrical fire. The present application takes into account a variety of environmental and electrical parameters, calculates the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively, obtains the switch quantity characteristic risk index and the actual digital quantity characteristic risk index, and judges whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold according to business needs, so as to filter out instantaneous abnormal data and reduce false alarms. If greater than, multi-feature risk assessment and analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to realize multi-feature electrical fire risk assessment and analysis, improve the accuracy and timeliness of electrical fire monitoring, and thus effectively prevent electrical fires. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0036] Figure 1 A flow chart of a multi-feature electrical fire risk assessment and analysis method disclosed in this application;
[0037] Figure 2 A specific flow chart of a multi-feature electrical fire risk assessment analysis disclosed in this application;
[0038] Figure 3 This is a schematic diagram of the structure of a multi-feature electrical fire risk assessment and analysis device disclosed in this application;
[0039] Figure 4 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION
[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.
[0041] Traditional electrical fire monitoring systems lack network connection functions and only rely on dashboards to display data. They also display data on the dashboard through preset safety thresholds. When the threshold is exceeded, an alarm is triggered. With the rapid development of Internet of Things technology, many traditional instruments have been transformed and upgraded into smart devices, but their basic working principles have not changed fundamentally. They still adopt the mode of single parameter monitoring and threshold over-limit alarm. This improvement based on the Internet of Things has indeed realized the remote real-time monitoring and alarm notification functions of equipment status to a certain extent, marking an important step towards intelligent management. However, it also exposes a significant defect: the false alarm rate remains high. Since the monitoring data often encounters instantaneous peaks, the system frequently touches the alarm threshold, resulting in frequent alarms, which not only consumes precious emergency response resources, but also may cause the "wolf coming" effect, making security personnel careless and reducing the sensitivity to real alarms and response efficiency in the long run. As can be seen from the above, how to realize multi-feature electrical fire risk assessment and analysis, improve the accuracy and timeliness of electrical fire monitoring, and effectively prevent electrical fires is a problem to be solved in this field.
[0042] See also Figure 1 As shown, the embodiment of the present invention discloses a multi-feature electrical fire risk assessment and analysis method, which may specifically include:
[0043] Step S11: Acquire a switch quantity data packet representing a switch state and an actual digital quantity data packet.
[0044] Step S12: Calculating the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index.
[0045] In this embodiment, a preset Internet of Things gateway is used to calculate the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index.
[0046] Specifically, the characteristic risk index calculation of the switch quantity data packet to obtain the switch quantity characteristic risk index includes: arranging the switch quantity data packet in a bitmap manner to obtain a bitmap, and determining the bitmap value; performing characteristic risk index calculation on the bitmap value to obtain the switch quantity characteristic risk index. Among them, performing characteristic risk index calculation on the bitmap value to obtain the switch quantity characteristic risk index includes: determining a preset bitmap threshold; calculating the ratio between the bitmap value and the bitmap threshold, and using the ratio as the switch quantity characteristic risk index.
[0047] In this embodiment, a bitmap is used to record the timing state of a group of switch data packets. The entire bitmap value is represented by an integer. When the state is abnormal, the value is moved to the left by one position (i.e. multiplied by 2) plus the starting value. When the abnormal state returns to normal, the value is moved to the right by one position (i.e. divided by 2). The sensitivity of the bitmap value calculation can be designed by the number of bits moved, such as two positions to the left and one position to the right. The memory length of the timing state can be designed by yourself. For example, 16 bits means that a group of timing states can remember the most recent 16 states at most, and 32 bits means that 32 states can be remembered. For example, the trend is as shown in Table 1:
[0048] Table 1
[0049]
[0050] The first column indicates the periodic alarm status, 1 indicates alarm, and 0 indicates no alarm. The second column indicates the bitmap record value. We observe the change of the risk factor through the bitmap. The starting value is 128 and the bitmap length is 16 bits.
[0051] The trend is positive as shown in Table 2:
[0052] Table 2
[0053]
[0054] Specifically, a characteristic risk index is calculated for the actual digital quantity data packet to obtain the actual digital quantity characteristic risk index, including: calculating the data sum of the actual digital quantity data packet and determining the number of the actual digital quantity data packets; calculating the product between a preset digital quantity threshold and the number, and using the ratio between the data sum and the product as the actual digital quantity characteristic risk index.
[0055] That is to say, the process of calculating the characteristic risk index is as follows: first, obtain a recent switching quantity record and the most recent continuous data collection value to obtain a switching quantity data packet representing the switch state and an actual digital quantity data packet, then arrange the switching quantity data packet representing the switch state in a bitmap manner to obtain a bitmap value, and divide the bitmap value by the bitmap threshold to obtain a floating point number, which is used as the switching quantity characteristic risk index; then calculate the data sum of the actual digital quantity data packet, determine the number of actual digital quantity data packets, calculate the product between the preset digital quantity threshold and the number, and use the ratio between the data sum and the product as the actual digital quantity characteristic risk index, such as the current value: the number of actual digital quantity data packets is the most recent 32 values, and the digital quantity threshold of the current is 10A. Then the actual digital quantity characteristic risk index of the current is: (A0+A1+…+A32) / 10*32.
[0056] Step S13: Determine whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold according to business needs; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, perform multi-characteristic risk assessment analysis on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment analysis result of an electrical fire.
[0057] In this embodiment, if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, a machine learning algorithm is used to construct a risk assessment analysis model, and the risk assessment analysis model is used to perform multi-feature risk assessment analysis on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index.
[0058] Specifically, after constructing the risk assessment and analysis model, a sequence in tensor form is generated based on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index, and the sequence is input into the risk assessment and analysis model for multi-feature risk assessment and analysis to obtain a risk assessment and analysis result of an electrical fire.
[0059] That is, first determine whether the switch quantity feature risk index or the actual digital quantity feature risk index is greater than the preset feature threshold. The judgment process is a single feature risk feature judgment. If it is greater, a second round of machine learning analysis is performed. In other words, if it is greater, a multi-feature risk assessment analysis is performed on the switch quantity feature risk index and the actual digital quantity feature risk index. At this time, the input layer tensor of the risk assessment analysis model is the switch quantity feature risk index and the actual digital quantity feature risk index. Among them, the risk assessment analysis model is mainly used to analyze risky data. Through a lightweight risk prediction method, unnecessary calculations of the risk assessment model are avoided, thereby reducing computing power costs and improving early warning response speed.
[0060] The specific process of this application is as follows Figure 2 As shown, a switch quantity data packet representing the switch state and an actual digital quantity data packet are obtained; a preset Internet of Things gateway is used to calculate the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet, respectively, to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index; it is determined whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold value; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold value, a machine learning algorithm is used to construct a risk assessment analysis model, and the risk assessment analysis model is used to perform multi-feature risk assessment analysis on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment analysis result of electrical fire.
[0061] In this embodiment, a switch quantity data packet and an actual digital quantity data packet representing the switch state are obtained; characteristic risk indexes are calculated for the switch quantity data packet and the actual digital quantity data packet respectively to obtain a switch quantity characteristic risk index and an actual digital quantity characteristic risk index; according to business needs, it is determined whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than a preset characteristic threshold; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, a multi-characteristic risk assessment analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment analysis result of an electrical fire. The present application takes into account a variety of environmental and electrical parameters, calculates the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively, obtains the switch quantity characteristic risk index and the actual digital quantity characteristic risk index, and judges whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold according to business needs, so as to filter out instantaneous abnormal data and reduce false alarms. If greater than, multi-feature risk assessment and analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to realize multi-feature electrical fire risk assessment and analysis, improve the accuracy and timeliness of electrical fire monitoring, and thus effectively prevent electrical fires.
[0062] See also Figure 3 As shown, the embodiment of the present invention discloses a multi-feature electrical fire risk assessment and analysis device, which may specifically include:
[0063] The data packet acquisition module 11 is used to acquire the switch quantity data packet representing the switch state and the actual digital quantity data packet;
[0064] A calculation module 12, used to calculate the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively, so as to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index;
[0065] The assessment and analysis module 13 is used to determine whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than a preset characteristic threshold according to business needs. If the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, a multi-characteristic risk assessment and analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment and analysis result of an electrical fire.
[0066] In this embodiment, a switch quantity data packet and an actual digital quantity data packet representing the switch state are obtained; characteristic risk indexes are calculated for the switch quantity data packet and the actual digital quantity data packet respectively to obtain a switch quantity characteristic risk index and an actual digital quantity characteristic risk index; according to business needs, it is determined whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than a preset characteristic threshold; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, a multi-characteristic risk assessment analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment analysis result of an electrical fire. The present application takes into account a variety of environmental and electrical parameters, calculates the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively, obtains the switch quantity characteristic risk index and the actual digital quantity characteristic risk index, and judges whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold according to business needs, so as to filter out instantaneous abnormal data and reduce false alarms. If greater than, a multi-feature risk assessment and analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to realize a multi-feature electrical fire risk assessment and analysis, thereby improving the accuracy and timeliness of electrical fire monitoring, and effectively preventing electrical fires.
[0067] In some specific embodiments, the calculation module 12 may specifically include:
[0068] The characteristic risk index calculation module is used to use a preset Internet of Things gateway to calculate the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively.
[0069] In some specific embodiments, the calculation module 12 may specifically include:
[0070] An arrangement module, used for arranging the switch quantity data packets in a bitmap manner to obtain a bitmap and determine a bitmap value;
[0071] The switch quantity characteristic risk index calculation module is used to calculate the characteristic risk index of the bitmap value to obtain the switch quantity characteristic risk index.
[0072] In some specific embodiments, the calculation module 12 may specifically include:
[0073] A bitmap threshold determination module, used to determine a preset bitmap threshold;
[0074] The ratio module is used to calculate the ratio between the bitmap value and the bitmap threshold, and use the ratio as the switch quantity characteristic risk index.
[0075] In some specific embodiments, the calculation module 12 may specifically include:
[0076] A quantity determination module, used for calculating the data sum of the actual digital quantity data packets and determining the quantity of the actual digital quantity data packets;
[0077] The actual digital quantity characteristic risk index calculation module is used to calculate the product between a preset digital quantity threshold and the quantity, and use the ratio between the data sum and the product as the actual digital quantity characteristic risk index.
[0078] In some specific embodiments, the evaluation and analysis module 13 may specifically include:
[0079] Model building module, used to build risk assessment analysis models using machine learning algorithms;
[0080] The model assessment and analysis module is used to use the risk assessment and analysis model to perform multi-feature risk assessment and analysis on the switch quantity feature risk index and the actual digital quantity feature risk index.
[0081] In some specific embodiments, the evaluation and analysis module 13 may specifically include:
[0082] A sequence generation module, used to generate a sequence in tensor form based on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index;
[0083] The assessment and analysis module is used to input the sequence into the risk assessment and analysis model to perform multi-feature risk assessment and analysis.
[0084] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the multi-feature electrical fire risk assessment and analysis method performed by the electronic device disclosed in any of the aforementioned embodiments.
[0085] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.
[0086] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.
[0087] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, so as to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the multi-feature electrical fire risk assessment and analysis method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to including data transmitted from an external device received by the multi-feature electrical fire risk assessment and analysis device, the data 223 can also include data collected by its own input and output interface 25, etc.
[0088] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0089] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the multi-feature electrical fire risk assessment and analysis method disclosed in any of the aforementioned embodiments are implemented.
[0090] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0091] The above is a detailed introduction to a multi-feature electrical fire risk assessment and analysis method, device, equipment and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for a person skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A multi-feature electrical fire risk assessment and analysis method, characterized in that: include: Obtaining a switch quantity data packet representing a switch state and an actual digital quantity data packet; Calculating the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index; According to business needs, determine whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold; if the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, perform multi-characteristic risk assessment analysis on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain the risk assessment analysis result of electrical fire.
2. The multi-feature electrical fire risk assessment and analysis method according to claim 1, characterized in that: The calculating of characteristic risk indexes of the switch quantity data packet and the actual digital quantity data packet respectively includes: The characteristic risk index of the switch quantity data packet and the actual digital quantity data packet is calculated respectively by using a preset Internet of Things gateway.
3. The multi-feature electrical fire risk assessment and analysis method according to claim 1, characterized in that: Calculating the characteristic risk index of the switch quantity data packet to obtain the switch quantity characteristic risk index includes: Arranging the switch quantity data packets in a bitmap manner to obtain a bitmap, and determining a bitmap value; A characteristic risk index is calculated for the bitmap value to obtain a switch value characteristic risk index.
4. The multi-feature electrical fire risk assessment and analysis method according to claim 3, characterized in that: The calculating of the characteristic risk index of the bitmap value to obtain the switch characteristic risk index includes: Determining a preset bitmap threshold; The ratio between the bitmap value and the bitmap threshold is calculated, and the ratio is used as the switch quantity characteristic risk index.
5. The multi-feature electrical fire risk assessment and analysis method according to claim 1, characterized in that: Calculating the characteristic risk index of the actual digital quantity data packet to obtain the actual digital quantity characteristic risk index includes: Calculating the data sum of the actual digital quantity data packets and determining the number of the actual digital quantity data packets; The product between a preset digital quantity threshold and the quantity is calculated, and the ratio between the data sum and the product is used as the actual digital quantity characteristic risk index.
6. The multi-feature electrical fire risk assessment and analysis method according to any one of claims 1 to 5, characterized in that: The performing multi-feature risk assessment analysis on the switch quantity feature risk index and the actual digital quantity feature risk index comprises: Use machine learning algorithms to build risk assessment analysis models; The risk assessment analysis model is used to perform multi-feature risk assessment analysis on the switch quantity feature risk index and the actual digital quantity feature risk index.
7. The multi-feature electrical fire risk assessment and analysis method according to claim 6, characterized in that: The risk assessment and analysis model is used to perform multi-feature risk assessment and analysis on the switch quantity feature risk index and the actual digital quantity feature risk index, including: Generate a sequence in tensor form based on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index; The sequence is input into the risk assessment analysis model for multi-feature risk assessment analysis.
8. A multi-feature electrical fire risk assessment and analysis device, characterized in that: include: A data packet acquisition module is used to acquire a switch quantity data packet representing a switch state and an actual digital quantity data packet; A calculation module, used to calculate the characteristic risk index of the switch quantity data packet and the actual digital quantity data packet respectively, so as to obtain the switch quantity characteristic risk index and the actual digital quantity characteristic risk index; The assessment and analysis module is used to determine whether the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than a preset characteristic threshold according to business needs. If the switch quantity characteristic risk index or the actual digital quantity characteristic risk index is greater than the preset characteristic threshold, a multi-characteristic risk assessment and analysis is performed on the switch quantity characteristic risk index and the actual digital quantity characteristic risk index to obtain a risk assessment and analysis result of an electrical fire.
9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the multi-feature electrical fire risk assessment and analysis method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the multi-feature electrical fire risk assessment and analysis method as described in any one of claims 1 to 7 is implemented.