CN115640496B - Electrical hidden danger judgment method based on improved analytic hierarchy process - Google Patents
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Abstract
The embodiment of the invention provides an electrical hidden danger judgment method based on an improved analytic hierarchy process, and belongs to the technical field of fire protection and security. The electrical hidden danger judging method comprises the following steps: acquiring real-time sensing data of a site; judging whether the real-time sensing data meet a preset power operation stable data model or not; under the condition that the real-time sensing data do not meet the power operation stable data model, weighting the real-time sensing data by adopting an improved analytic hierarchy process; determining the current judging grade according to the weighted real-time sensing data; and determining whether to send out a fire alarm according to the judging grade. The method for judging the electrical hidden danger can analyze various factors on site, and realize accurate early warning of fire.
Description
Technical Field
The invention relates to the technical field of fire protection and security, in particular to an electrical hidden danger judgment method based on an improved analytic hierarchy process.
Background
Electrical fire monitoring systems have been commonly used in some buildings of a somewhat larger scale, but their use has not been as effective as desired. At present, most buildings in China only use residual current type electrical fire monitoring detectors, but do not use temperature measurement type electrical fire monitoring detectors, fault arc detectors and the like in a matched mode.
When data indexes of residual current, temperature, current and the like of the electrical equipment are abnormal, a sensor of the electrical fire monitoring system collects information of index change through an electromagnetic induction principle and the effect of temperature change, and transmits the information to a signal processing unit (signal processing unit) in the electrical fire monitoring system, after the steps of filtering, amplifying, A/D conversion, analysis, judgment, comparison and the like are carried out, once a key data index exceeds a preset value, an alarm signal is immediately sent out and is simultaneously transmitted to monitoring equipment of the electrical fire monitoring system, secondary identification and judgment are carried out through the monitoring equipment, when a fire is confirmed to possibly occur, a monitoring host computer sends a fire early warning signal, at the moment, an alarm indicating lamp is lightened and sends out the alarm signal, and related detailed information such as fire alarm and the like appears in liquid crystal display equipment of the monitoring system. And related staff on duty can contact a power engineer or equipment maintenance personnel to go to an equipment fault abnormal site for maintenance, inspection and treatment at the first time according to the display information, and simultaneously transmit fire alarm information to a central control room. In addition, the electrical fire monitoring system also has a communication networking function, and can transmit the detected related fire information to a monitoring system of a higher layer, so that a monitoring center of the higher layer can obtain fire alarm information.
However, in the prior art, the judgment of the fire alarm information is only simple threshold judgment, and the alarm can be given only when a fire happens or happens within a short time, so that the fire alarm mode obviously cannot give an early warning, and cannot give an early warning to the potential hidden danger of the fire.
Disclosure of Invention
The invention aims to provide an electrical hidden danger judging method based on an improved analytic hierarchy process, which can realize accurate early warning of fire by analyzing various factors on site.
In order to achieve the above object, an embodiment of the present invention provides an electrical hidden danger determining method based on an improved analytic hierarchy process, including:
acquiring real-time sensing data of a site;
judging whether the real-time sensing data meet a preset power operation stable data model or not;
under the condition that the real-time sensing data do not meet the power operation stable data model, weighting the real-time sensing data by adopting an improved analytic hierarchy process;
determining the current judging grade according to the weighted real-time sensing data;
and determining whether to send out a fire alarm according to the judged grade.
Optionally, the acquiring real-time sensing data of a site specifically includes:
and performing primary processing on the real-time sensing data by edge computing equipment arranged on the spot.
Optionally, the real-time sensing data includes energy consumption data, line node temperature data, line temperature rise data, ambient temperature data, power supply temperature data of the relevant transformer and the end point power consumption appliance, and vibration and noise sensor data.
Optionally, the energy consumption data, the line node temperature data, the line temperature rise data, the ambient temperature data, the power supply temperature data of the relevant transformer and the end-point power appliance, and the vibration and noise sensor data include an average value, an extreme value, and a variance value within a predetermined interval.
Optionally, the analytic hierarchy process comprises:
determining fluctuation intervals of the weight of each factor of the real-time sensing data;
constructing a judgment matrix of each factor according to the fluctuation interval;
calculating a consistency index of the judgment matrix;
judging whether the consistency index is smaller than a preset threshold value or not;
under the condition that the consistency index is judged to be smaller than the threshold value, outputting the weight of the factors of the real-time sensing data according to the judgment matrix;
and under the condition that the consistency index is judged to be larger than or equal to the threshold value, re-executing the step of constructing the judgment matrix of each factor according to the fluctuation interval.
Optionally, constructing a judgment matrix of each factor according to the fluctuation interval specifically includes:
randomly selecting a point value in the fluctuation interval according to the current real-time sensing data;
calculating importance parameters of the factors according to the point values;
and constructing the judgment matrix according to the importance parameters.
Optionally, randomly selecting a point value in the fluctuation interval according to the current real-time sensing data includes:
establishing an initial interval by using the historical maximum value and the historical minimum value of the corresponding factors of the real-time sensing data;
calculating the current position in the initial interval according to the current value of the corresponding element of the real-time sensing data;
determining a probability of a point value according to the location;
and selecting the point value according to the probability by adopting a roulette method.
Optionally, calculating a current position in the initial interval according to a current value of a corresponding element of the real-time sensing data, specifically including:
the position is calculated according to equation (1),
wherein the content of the first and second substances,
is shown as
The number of the elements is one,
is shown as
The lower limit value of the initial interval corresponding to each element,
is shown as
The upper limit value of the initial interval corresponding to each element.
Optionally, determining the probability of the point value according to the position specifically includes:
converting the position into any one point value of 0.1 to 0.9 at intervals of 0.1 according to a rounding principle;
determining the current peak probability according to the converted point value;
and taking the current point value as an origin, and respectively extending and calculating to two sides of the initial interval according to a linear superposition principle to obtain the probability of each other point value.
Optionally, the extension calculation comprises:
calculating the probability of each remaining point value according to the formula (2) to the formula (5),
wherein the content of the first and second substances,
left of the current point value
The probability of a point value being a function of,
the probability of the tth point value to the right of the current point value,
the position or sequence number corresponding to the first point value of the initial interval,
is the position or serial number corresponding to the current point value,
is the position or sequence number corresponding to the last point value of the initial interval,
、 
in the form of a slope parameter,
、 
is a bias parameter.
According to the technical scheme, the method for judging the electrical hidden danger based on the improved analytic hierarchy process provided by the invention has the advantages that the real-time sensing data on site are obtained, the real-time sensing data are analyzed by combining the power operation stable data model, and the real-time sensing data are weighted and calculated by the improved analytic hierarchy process under the condition that the model is not satisfied, so that the current judging grade is determined, and finally, the early warning operation of the fire disaster is completed in a judging grade mode. Compared with the prior art, the method provided by the invention combines multiple factors for judgment, and realizes more comprehensive judgment of the electrical hidden danger; on the other hand, in order to comprehensively consider the weight of various factors, the method provided by the invention carries out weighting calculation by improving the analytic hierarchy process, so that more accurate judgment of the electrical hidden danger is realized.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart of an electrical hazard determination method based on an improved analytic hierarchy process according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a line node according to one embodiment of the present invention;
fig. 3 is a partial flowchart of an electrical hazard determination method based on an improved analytic hierarchy process according to an embodiment of the present invention;
FIG. 4 is a partial flow diagram of a method for determining an electrical hazard based on improved analytic hierarchy process according to an embodiment of the present invention;
fig. 5 is a partial flowchart of an electrical hazard determination method based on improved analytic hierarchy process according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
In the embodiments of the present invention, unless otherwise specified, the use of directional terms such as "upper, lower, top, and bottom" is generally used with respect to the orientation shown in the drawings or the positional relationship of the components with respect to each other in the vertical, or gravitational direction.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between the various embodiments can be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not be within the protection scope of the present invention.
Fig. 1 is a flowchart illustrating an electrical hazard determination method based on improved analytic hierarchy process according to an embodiment of the present invention. In fig. 1, the method for determining an electrical hidden danger may include:
in step S10, acquiring real-time sensing data of a site;
in step S11, it is determined whether the real-time sensing data satisfies a preset power operation stable data model;
in step S12, weighting the real-time sensing data by using an improved analytic hierarchy process when it is determined that the real-time sensing data does not satisfy the power operation stable data model;
in step S13, determining a current judging level according to the weighted real-time sensing data;
in step S14, it is determined whether or not a fire alarm is issued according to the judged level.
In this embodiment, the real-time sensing data of the site may be measured by a sensor installed in the site. The distribution of line nodes in the field is shown in figure 2. In fig. 2, the specific electric devices connected to each partition from the utility power supply may be sequentially divided into a primary node, a secondary node, a tertiary node, and the like. Various sensors such as a current sensor, a voltage sensor, a temperature sensor, a vibration sensor, a fire gas sensor, a noise sensor, and the like may be provided for the power equipment of each line node, respectively. Further, considering that the real-time sensing data collected by the on-site sensor is an analog value that changes with time, the real-time sensing data may be initially processed by the edge computing device disposed on the site, so as to obtain a simplified value that can express the real-time sensing data. Specifically, the simplified real-time sensing data may include energy consumption data, line node temperature data, line temperature rise data, ambient temperature data, power supply temperature data of the relevant transformer and the end-point power consumption appliance, and vibration and noise sensor data including an average value, an extreme value, and a variance value within a predetermined interval.
Step S11 may be configured to determine whether the real-time sensing data meets a preset power operation stability data model. The power operation stability data model may take many forms as known to those skilled in the art. For example, the power operation stable data model may be obtained by acquiring a large amount of field data of stable operation in advance, and training a neural network (e.g., a fully connected network, etc.). Since the structure of the neural network and the corresponding training method are relatively common, they are not described herein again.
In the case that the power operation stability data model is judged to be satisfied, it is indicated that all the equipment on the current site operates normally, and therefore, any operation does not need to be executed. On the contrary, if the electric power operation stable data model is judged not to be satisfied, the comprehensive judgment needs to be carried out by combining the real-time sensing data, and whether fire warning needs to be sent out or not is judged by determining the judging grade. In consideration of the fact that real-time sensing data itself is various, and the importance degree of different types of real-time sensing data is different. Therefore, it is necessary to apply the analytic hierarchy process to weight the real-time sensing data of each kind. However, the weight ratio of the traditional analytic hierarchy process depends on experts to give scores, and the method for scoring by experts according to a self knowledge system depends on artificial experience, so that a large difference is easy to occur in the actual operation process, and the result of the weighting calculation deviates from the preset requirement. Thus, in this embodiment, the improved analytic hierarchy process may include the steps as shown in fig. 3. In the fig. 3, the analytic hierarchy process may include:
in step S20, determining a fluctuation interval of each factor weight of the real-time sensing data;
in step S21, a judgment matrix of each factor is constructed according to the fluctuation interval;
in step S22, a consistency index of the determination matrix is calculated;
in step S23, it is determined whether the consistency index is smaller than a preset threshold;
in step S24, when it is determined that the consistency index is smaller than the threshold, weights of factors of the real-time sensing data are output according to the determination matrix;
in the case where it is determined that the consistency index is greater than or equal to the threshold, the step of constructing the determination matrix for each factor from the fluctuation interval is executed again, that is, the step returns to the execution of step S20.
In the method as shown in fig. 3, step S20 is used to determine fluctuation intervals of the respective factor weights of the real-time sensed data. The fluctuation interval may be constructed by using a historical maximum value and a historical minimum value of a category corresponding to the real-time sensing data as an upper limit and a lower limit.
Step S21 is used to construct a judgment matrix for each factor according to the fluctuation interval. The traditional construction method of the judgment matrix is mainly characterized in that the importance of two factors is compared pairwise, and the weights of the two factors are determined by expert scoring, so that the construction is completed. For example, the significance comparison of conventional analytic hierarchy processes is shown in Table 1,
TABLE 1
The expert quantifies the scale values on the left side by making a decision on the right side.
However, the expert experience determines that there is a great uncertainty in the implementation, and finding one or more experts that are sufficient to understand the field is inherently difficult. Therefore, in this embodiment, the method of constructing the decision matrix may be to adopt the steps as shown in fig. 4. In fig. 4, the method for constructing the judgment matrix may be:
in step S30, a point value is randomly selected in the fluctuation interval according to the current real-time sensing data;
in step S31, calculating importance parameters of two factors according to the point values;
in step S32, a determination matrix is constructed according to the importance parameter.
In fig. 4, step S30 is used to select a point value corresponding to the real-time sensing number. Taking the temperature average value and the range of the master node as an example, in step S30, a corresponding point value, for example, 1 and 9, may be selected for the average value and the range respectively, and the magnitude relationship between the two values is combined, so as to obtain the corresponding scale values. That is, the average value is weighted 1/9 compared to the range, and the range is weighted 9 compared to the average value. Although the method can make the generation of the weight (or scale) divorced from the subjective judgment of experts, the generated weight is randomly generated, so that the constructed judgment matrix is difficult to express the characteristics of the current real-time sensing data. Therefore, in this embodiment, further, the step S30 may be to first construct an initial interval with the historical maximum value and the historical minimum value of the corresponding factor of the real-time sensing data; calculating the current position in the initial interval according to the current value of the corresponding element of the real-time sensing data; determining a probability of a choice point value according to the position; and finally, selecting a point value according to the probability by adopting a roulette method. Specifically, the construction of the initial interval may determine a fluctuation range of the current real-time sensing data, and since the real-time sensing data is power equipment, the smaller the real-time sensing data is, the lower the hidden danger is, so it can be considered that the smaller the value of the real-time sensing data is, the lower the hidden danger is, and otherwise, the higher the hidden danger is.
After the initial interval is generated, the purpose of calculating the current position of the current initial interval according to the current value of the corresponding element (i.e. one kind of real-time sensing data) of the real-time sensing data is to represent the importance of the current real-time sensing data, that is, the closer the position of the numerical value in the initial interval close to the upper limit value is, the more important the corresponding element is, and accordingly, the larger the point value to be selected is. Specifically, the position may be calculated according to formula (1),
wherein the content of the first and second substances,
is shown as
The number of the elements is one,
denotes the first
The lower limit value of the initial interval corresponding to each element,
is shown as
The upper limit value of the initial interval corresponding to each element.
Based on the calculated positions, a probability of selecting each point value may be determined. Although it has been described above that the closer the value is to the upper limit value in the initial interval, the more important the corresponding element is, the larger the point value to be selected. But this does not enable direct quantization of each point value. Since it is not possible to select the largest point value directly during the actual processing because the value is close to the upper limit value. Therefore, the probability of selecting each point value also needs to be determined for the current situation of the value, i.e. the steps as shown in fig. 5. In this fig. 5, the method of determining the probability may include:
in step S40, the position is converted to any one of 0.1 to 0.9 by the rounding principle and the interval is 0.1;
in step S41, determining a current peak probability from the converted point value;
in step S42, the current point value is used as an origin, and the calculation is extended to both sides of the initial interval according to the linear superposition principle, so as to obtain the probability of each of the other point values.
Wherein, the peak probability corresponding to the point value is the corresponding probability value. For example, assuming that the real-time sensing data has a value of 0.65, then according to the rounding principle, the corresponding point value is 0.7 (7 when being determined as the aforementioned scale), and the corresponding peak probability may also be 0.7.
The extended calculation may be such that the probabilities of the remaining point values are calculated according to equations (2) to (5),
wherein the content of the first and second substances,
the first to the left of the current point value
The probability of an individual point value,
to the right of the current point value
The probability of an individual point value,
the position or sequence number corresponding to the first point value of the initial interval,
is the position or serial number corresponding to the current point value,
is the position or sequence number corresponding to the last point value of the initial interval,
in the form of a slope parameter,
is a bias parameter.
Step S13, determining the current judging grade according to the weighted real-time sensing data; step S14 can determine whether to send out fire alarm according to the judged level. Specifically, the steps S13 and S14 may be a step S13 of calculating corresponding evaluation parameters by weighting, determining a judgment level by section judgment of the evaluation parameters, and finally determining whether fire warning needs to be issued by correspondence between the judgment level and options such as whether fire warning needs to be issued.
In the method shown in any of fig. 1 to 5, it is possible to judge not only the entire site but also a plurality of factors related to a local fire. For example, when the method provided by the invention is implemented for a community health service center in a town of western Qing district of Tianjin, according to the result after weighted calculation, it is found that the evaluation level of the power distribution cabinet of the roof direct-fired machine room is higher, and parameter abnormality may exist. In view of this, the staff carries out manual investigation on site to find that the direct-fired machine room power distribution cabinet finds that the B-phase wiring terminal of the power distribution cabinet main switch upper port electricity inlet is abnormal in appearance, the temperature measurement displays about 72 ℃, and the temperature test at the B-phase lower port reaches 100 ℃. Meanwhile, the phenomenon of overheating and melting of the next-stage control switch circuit is found.
According to the technical scheme, the method for judging the electrical hidden danger based on the improved analytic hierarchy process provided by the invention has the advantages that the real-time sensing data on site are obtained, the real-time sensing data are analyzed by combining the power operation stable data model, and the real-time sensing data are weighted and calculated by the improved analytic hierarchy process under the condition that the model is not satisfied, so that the current judging grade is determined, and finally, the early warning operation of the fire disaster is completed in a judging grade mode. Compared with the prior art, the method provided by the invention combines multiple factors for judgment, and realizes more comprehensive judgment of the electrical hidden danger; on the other hand, in order to comprehensively consider the weight of various factors, the method provided by the invention carries out weighting calculation by improving the analytic hierarchy process, so that more accurate judgment of the electrical hidden danger is realized.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
Those skilled in the art can understand that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct related hardware, where the program is stored in a storage medium and includes several instructions to enable a (may be a single chip, a chip, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, various different embodiments of the present invention may be arbitrarily combined with each other, and the embodiments of the present invention should be considered as disclosed in the disclosure of the embodiments of the present invention as long as the embodiments do not depart from the spirit of the embodiments of the present invention.
Claims (4)
1. An electrical hidden danger judgment method based on an improved analytic hierarchy process is characterized by comprising the following steps:
acquiring real-time sensing data of a site;
judging whether the real-time sensing data meet a preset power operation stable data model or not;
under the condition that the real-time sensing data do not meet the power operation stable data model, weighting the real-time sensing data by adopting an improved analytic hierarchy process;
determining the current judging grade according to the weighted real-time sensing data;
determining whether to send out a fire alarm according to the judging grade;
the analytic hierarchy process comprises:
determining fluctuation intervals of the weight of each factor of the real-time sensing data;
constructing a judgment matrix of each factor according to the fluctuation interval;
calculating a consistency index of the judgment matrix;
judging whether the consistency index is smaller than a preset threshold value or not;
under the condition that the consistency index is judged to be smaller than the threshold value, outputting the weight of the factors of the real-time sensing data according to the judgment matrix;
under the condition that the consistency index is judged to be larger than or equal to the threshold value, a step of constructing a judgment matrix of each factor according to the fluctuation interval is executed again;
constructing a judgment matrix of each factor according to the fluctuation interval, which specifically comprises the following steps:
randomly selecting a point value in the fluctuation interval according to the current real-time sensing data;
calculating importance parameters of the factors according to the point values;
constructing the judgment matrix according to the importance parameters;
randomly selecting a point value in the fluctuation interval according to the current real-time sensing data comprises:
establishing an initial interval by using the historical maximum value and the historical minimum value of the corresponding factors of the real-time sensing data;
calculating the current position in the initial interval according to the current value of the corresponding element of the real-time sensing data;
determining a probability of a point value according to the location;
selecting the point value according to the probability by adopting a roulette method;
calculating the current position in the initial interval according to the current value of the corresponding element of the real-time sensing data, specifically comprising:
the position is calculated according to equation (1),
wherein x is i Denotes the ith element, x min Lower limit value, x, of initial interval corresponding to the ith element max Representing the upper limit value of the initial interval corresponding to the ith element;
determining the probability of the point value according to the position specifically comprises:
converting the position into any one point value of 0.1 to 0.9 at intervals of 0.1 according to a rounding principle;
determining the current peak probability according to the converted point value;
taking the current point value as an origin, and respectively extending and calculating to two sides of the initial interval according to a linear superposition principle to obtain the probability of each other point value;
the extension calculation includes:
calculating the probability of each remaining point value according to the formula (2) to the formula (5),
x it 1 ＝k 1 t+b 1 ，t∈[0，t 0 ] (2)
x it 2 ＝k 2 t+b 2 ，t∈[t 0 ，t max ] (3)
wherein x is it 1 Probability, x, of the t-th point value to the left of the current point value it 2 The probability of the t-th point value to the right of the current point value, 0 represents the position or sequence number corresponding to the first point value in the initial interval, t 0 For the position or sequence number, t, corresponding to the current point value max Is the position or sequence number, k, corresponding to the last point value of the initial interval 1 、k 2 Is a slope parameter, b 1 、b 2 Is a bias parameter.
2. The method for judging the electrical hidden danger according to claim 1, wherein the step of acquiring real-time sensing data of a site specifically comprises the steps of:
and performing primary processing on the real-time sensing data by using edge computing equipment arranged on the spot.
3. The method for determining electrical hidden danger of claim 1, wherein the real-time sensing data includes energy consumption data, line node temperature data, line temperature rise data, ambient temperature data, power supply temperature data of related transformers and end-point power appliances, and vibration and noise sensor data.
4. The method according to claim 3, wherein the energy consumption data, the line node temperature data, the line temperature rise data, the ambient temperature data, the power supply temperature data of the relevant transformer and the end-point electrical appliance, and the vibration and noise sensor data comprise an average value, an extreme value and a variance value within a predetermined interval.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109102163A (en) * | 2018-07-20 | 2018-12-28 | 河海大学 | Power distribution network CPS methods of risk assessment based on variable weight Fuzzy AHP |
| CN111311085A (en) * | 2020-02-10 | 2020-06-19 | 清华大学合肥公共安全研究院 | Dynamic risk assessment method and device for building fire based on Internet of things monitoring |
| CN112597581A (en) * | 2020-11-16 | 2021-04-02 | 西南交通大学 | High-speed train temperature anomaly detection method based on space-time fusion decision |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109933881A (en) * | 2019-03-06 | 2019-06-25 | 武汉大学 | A kind of Fault Diagnosis of Power Electronic Circuits method based on optimization deepness belief network |
| CN113077161A (en) * | 2021-04-14 | 2021-07-06 | 华北电力大学 | Emergency repair decision method for emergency robot in power distribution room |
| CN113256075A (en) * | 2021-04-29 | 2021-08-13 | 浙江非线数联科技股份有限公司 | Enterprise risk level evaluation method based on hierarchical analysis and fuzzy comprehensive evaluation method |
| CN115759729A (en) * | 2021-11-12 | 2023-03-07 | 江西省国土空间调查规划研究院 | Disaster risk early warning management system based on algorithm and prediction method |
| CN114120565B (en) * | 2021-11-26 | 2023-01-24 | 燕山大学 | Forest fire early warning method |
| CN115013261B (en) * | 2022-08-08 | 2022-12-06 | 国网浙江省电力有限公司舟山供电公司 | State monitoring method and system for offshore wind farm |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109102163A (en) * | 2018-07-20 | 2018-12-28 | 河海大学 | Power distribution network CPS methods of risk assessment based on variable weight Fuzzy AHP |
| CN111311085A (en) * | 2020-02-10 | 2020-06-19 | 清华大学合肥公共安全研究院 | Dynamic risk assessment method and device for building fire based on Internet of things monitoring |
| CN112597581A (en) * | 2020-11-16 | 2021-04-02 | 西南交通大学 | High-speed train temperature anomaly detection method based on space-time fusion decision |
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