Fire risk degree determination method, electronic device, storage medium, and program product

Abstract

This application provides a method, electronic device, storage medium, and program product for determining the degree of fire hazard. The method includes: acquiring multiple first indicator data corresponding to multiple dimensions of a target material, wherein the multiple first indicator data are associated with the degree of fire hazard corresponding to the target material; determining a first weight corresponding to each first indicator data based on the multiple first indicator data, wherein the first weight indicates the influence coefficient of the first indicator data on the degree of fire hazard corresponding to the target material; and determining the degree of fire hazard corresponding to the target material based on the multiple first indicator data, the first weight corresponding to each first indicator data, and a preset degree of fire hazard correction coefficient. This method is used to accurately and scientifically determine the degree of fire hazard of target materials in rail transit scenarios, thereby effectively improving the fire safety level of rail transit.

Description

Technical Field

[0001] This application relates to the fields of safety science and engineering technology, and in particular to a method for determining the degree of fire hazard, electronic equipment, storage medium and program product. Background Technology

[0002] With the rapid development of rail transit, the public safety of rail transit has become increasingly important. Due to the confined space and dense population of rail vehicles, a fire can easily cause serious consequences. Therefore, the need for a scientific assessment of the fire hazard of materials used in rail vehicles is becoming increasingly urgent.

[0003] In existing technologies, the fire hazard level of various materials on rail vehicles is usually evaluated based on expert experience, and then materials with high fire hazard level are screened out. The overall safety is improved by reducing the application of high fire hazard materials on rail vehicles.

[0004] However, evaluation methods that rely on expert experience are highly subjective and difficult to avoid personal judgment biases. This directly leads to inaccurate evaluation results of the fire hazard level of target materials, which may affect the scientific nature of material selection for rail vehicles and fail to fully guarantee the fire safety of rail transit. Summary of the Invention

[0005] The fire hazard determination method, electronic equipment, storage medium, and program products provided in this application are used to accurately and scientifically determine the fire hazard level of target materials in rail transit scenarios, thereby effectively improving the fire safety level of rail transit.

[0006] In a first aspect, embodiments of this application provide a method for determining the degree of fire hazard, including:

[0007] Acquire multiple primary indicator data corresponding to multiple dimensions of the target material, and these multiple primary indicator data are correlated with the degree of danger corresponding to the target material when a fire occurs.

[0008] Based on multiple primary indicator data, a primary weight corresponding to each primary indicator data is determined. The primary weight is used to indicate the influence coefficient of the primary indicator data on the degree of danger corresponding to the target material when a fire occurs.

[0009] Based on multiple primary indicator data, the primary weights corresponding to each primary indicator data, and the preset risk level correction coefficient, the risk level corresponding to the target material when it is involved in a fire is determined.

[0010] In one possible implementation, multiple first indicator data are standardized to obtain multiple second indicator data.

[0011] Based on multiple secondary indicator data, corresponding primary weights are determined.

[0012] In one possible implementation, multiple correlation coefficients are determined based on multiple first indicator data, and the correlation coefficients are used to indicate the correlation between the first indicator data of any two dimensions.

[0013] Based on multiple correlation coefficients and the first weights corresponding to each first indicator data, multiple second weights are determined.

[0014] Based on multiple primary indicator data, multiple secondary weights, and preset hazard correction coefficients, the hazard level corresponding to the target material when it is involved in a fire is determined.

[0015] In one possible implementation, if the correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the two corresponding first weights are merged to obtain a second weight.

[0016] Otherwise, the corresponding second weight is determined as the first weight.

[0017] In one possible implementation, the degree of danger corresponding to a fire involving the target material satisfies the following formula:

[0018]

[0019] in, The degree of danger corresponding to a fire involving the target material; The first indicator data for the j-th dimension; The first weight of the j-th dimension; The preset hazard level correction factor is determined by ventilation level a, personnel density level b, and ambient temperature level c.

[0020] In one possible implementation, the correlation coefficient satisfies the following formula:

[0021]

[0022] in, The correlation coefficient is the relationship between any first indicator data X in any dimension and any first indicator data Y in a different dimension from X. Let X be the covariance of Y; It is the product of the standard deviations of X and Y.

[0023] Secondly, embodiments of this application provide a fire hazard determination device, comprising:

[0024] The acquisition module is used to acquire multiple primary indicator data corresponding to multiple dimensions of the target material. These multiple primary indicator data are associated with the degree of danger corresponding to the target material when it is in a fire.

[0025] The processing module is used to determine the first weight corresponding to each first indicator data based on multiple first indicator data. The first weight is used to indicate the influence coefficient of the first indicator data on the degree of danger corresponding to the target material when a fire occurs.

[0026] The determination module is used to determine the degree of danger of a target material when it is in the event of a fire, based on multiple first indicator data, the first weight corresponding to each first indicator data, and a preset danger correction coefficient.

[0027] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0028] The memory stores instructions that the computer executes;

[0029] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0031] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0032] The fire hazard determination method, electronic device, storage medium, and program product provided in this application acquire multi-dimensional first indicator data to fully reproduce the true hazardous characteristics of raw materials in a fire, providing comprehensive and objective basic data support for subsequent risk assessment. Furthermore, by determining the first weight corresponding to each first indicator data, the influence coefficient of the indicator on the degree of hazard is quantified, making the assessment results more aligned with the actual safety needs of rail transit. Finally, a hazard correction coefficient is introduced in conjunction with the specific application scenarios of rail transit, constructing a systematic material fire risk assessment system. Ultimately, this achieves accurate and scientific determination of the fire hazard level of target materials in rail transit scenarios, thereby effectively improving the fire safety level of rail transit. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0034] Figure 1 A flowchart illustrating the method for determining the degree of fire hazard provided in this application embodiment. Figure 1 ;

[0035] Figure 2 A flowchart illustrating the method for determining the degree of fire hazard provided in this application embodiment. Figure 2 ;

[0036] Figure 3 This is a schematic diagram of the fire hazard determination device provided in the embodiments of this application;

[0037] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0038] Figure 5 An example diagram of correlation coefficients provided for embodiments of this application.

[0039] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0040] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0041] To address the issue that the aforementioned evaluation methods, which rely heavily on expert experience and are highly subjective, making it difficult to avoid personal judgment bias, and thus leading to inaccurate assessments of the fire hazard of target materials, potentially affecting the scientific selection of materials for rail vehicles and failing to fully guarantee the fire safety of rail transit, the following technical concept is proposed: Construct a fire hazard determination method based on multi-dimensional data quantitative analysis. This involves systematically collecting multi-dimensional primary indicator data related to the fire hazard of target materials (such as peak heat release rate, total CO release, and smoke release rate), breaking the limitations of single indicators or subjective experience, and providing a comprehensive and objective basis for evaluation. Then, based on this data, the influence coefficient of each indicator on the hazard level (i.e., the primary weight) is scientifically determined, transforming subjective experience into quantifiable objective parameters and avoiding personal judgment bias. Finally, by combining multi-dimensional indicator data, corresponding weights, and preset hazard correction coefficients tailored to rail transit scenarios, the fire hazard level of the target material is comprehensively calculated, achieving accurate and scientific determination of material fire risk. This provides a reliable basis for the selection of rail vehicle materials and fully guarantees the fire safety of rail transit.

[0042] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0043] Figure 1 A flowchart illustrating the method for determining the degree of fire hazard provided in this application embodiment. Figure 1 The fire hazard determination method provided in this application can be applied to any electronic device. For example... Figure 1 As shown, the method includes:

[0044] S101. Obtain multiple primary indicator data corresponding to multiple dimensions of the target material, and the multiple primary indicator data are associated with the degree of danger corresponding to the target material when a fire occurs.

[0045] Among them, the primary indicator data corresponding to multiple dimensions can be peak heat release rate (PHRR), total heat release rate (THRR), peak carbon dioxide release rate (PCO), total carbon dioxide release rate (TCO), peak carbon dioxide release rate (PCO2), total carbon dioxide release rate (TCO2), peak smoke release rate (PSM), total smoke release rate (TSM), and peak weight loss rate (PWLR), which measure the degree of danger of the target material when it is in fire from different dimensions.

[0046] For example, the first index data corresponding to multiple dimensions of the target material are shown in Table 1 below:

[0047]

[0048] Specifically, multiple primary indicator data corresponding to multiple dimensions of the target material are obtained from the database.

[0049] If possible, non-metallic material samples from various components of the rail vehicle will be manually collected, including roof insulation material, roof panels, lamp covers, side wall insulation material, side wall panels, floor fabric, floor adhesive film, seat panels, door seals, window seals, and windshield tarpaulin. The collected material samples will undergo pretreatment, such as washing and drying, to ensure the accuracy of the test results. A cone calorimeter will be used to test the pretreated material samples, and the test data for each sample will be obtained and entered into a database. The testing process must be carried out according to the pre-set operation manual to ensure the reliability of the test results.

[0050] S102. Based on multiple first indicator data, determine the first weight corresponding to each first indicator data. The first weight is used to indicate the influence coefficient of the first indicator data on the degree of danger corresponding to the target material when it is in a fire.

[0051] Possibly, multiple primary index data for each material sample corresponding to multiple material samples of the target material. For example, if the target material is a sidewall panel, the dimensions corresponding to the primary index data include PWLR and TCO2. The test data of 10 sidewall panels are as follows: PWLR data (unit, g / s): 0.03, 0.04, 0.05, 0.06, 0.07, 0.03, 0.08, 0.05, 0.06, 0.04; TCO2 data (unit, g): 95, 98, 100, 102, 97, 99, 101, 96, 103, 98.

[0052] Calculate the average value of the first indicator data for each dimension. For example, the average PWLR of 10 sidewall panels is 0.05 g / s, and the average TCO2 of 10 sidewall panels is 100 g.

[0053] Calculate the standard deviation of the first indicator data for each dimension. For example, the standard deviation of PWLR for 10 sidewall panels is approximately 0.016 g / s, and the standard deviation of TCO2 for 10 sidewall panels is approximately 2.5 g.

[0054] Based on the average and standard deviation of the first indicator data for each dimension, the corresponding volatility coefficient is determined, and then the first weight corresponding to each first indicator data is determined. The volatility coefficient can be determined by dividing the standard deviation by the average, and the first weight can be determined by the proportion of the volatility coefficient of each first indicator data to the total volatility coefficient of the first indicator data.

[0055] For example, based on the data in the example above, assuming that the total volatility coefficient of the five dimensions is 1.5, the first weight of the first indicator data corresponding to PWLR is 21%, and the first weight of the first indicator data corresponding to TCO2 is 1.7%.

[0056] S103. Based on multiple first indicator data, the first weight corresponding to each first indicator data and the preset danger correction coefficient, determine the danger level corresponding to the target material when a fire occurs.

[0057] Once the level of danger associated with a fire involving the target material is determined, the first step is to define the danger threshold, i.e., the critical value that distinguishes different danger levels. This threshold is not arbitrarily set; it must be determined comprehensively based on historical accident data, the material's application scenario, and safety objectives. Taking non-metallic materials in rail vehicles as an example, the danger threshold can be set according to the following methods:

[0058] Based on historical data of past fires involving rail vehicles, the material hazard characteristics that cause casualties and fire spread are analyzed. For example, when the material hazard level is ≥0.7, more than 90% of fires will result in serious casualties, so the high-risk threshold is set as R≥0.7; when 0.4≤hazard level<0.7, the fire is mostly localized and causes only minor damage, so the medium-risk threshold is set in this range; when the hazard level is <0.4, it almost never causes serious fires, so it is set as the low-risk threshold.

[0059] Different application scenarios have different safety level requirements, and the hazard threshold needs to be adjusted accordingly. For example, densely populated areas inside the carriage (such as seats and floor coverings) have high safety requirements and require strict thresholds, so the lower limit of the medium-risk hazard threshold may be set at 0.3; non-personnel areas outside the carriage (such as the outer layer of the roof insulation material) have relatively low safety requirements and can relax the threshold, so the lower limit of the medium-risk hazard threshold may be set at 0.5.

[0060] The hazard level of a target material in the event of a fire is compared with a hazard threshold to match the corresponding hazard level. For example, a certain floor covering has a hazard level of 0.35, which is less than 0.4, and matches the "low hazard" level; a certain door sealing strip has a hazard level of 0.62, which is between 0.4 and 0.7, and matches the "medium hazard" level; a certain windshield tarpaulin has a hazard level of 0.75, which is greater than 0.7, and matches the "high hazard" level.

[0061] For target materials with different hazard levels, risk reports are generated based on primary indicator data from different dimensions and the specific application scenarios of the target materials. These reports allow relevant personnel to mitigate risks and improve rail vehicle safety. For example...

[0062] A certain seat panel, used inside a rail vehicle carriage, is classified as medium-risk. The key risk indicator, Total CO Release (TCO), with a weight of 0.22, contributes the most to the hazard level, indicating that the seat panel releases a significant amount of CO upon combustion. In the event of a fire, this could easily lead to CO poisoning among passengers. The scenario risk is that, given the dense population and limited evacuation routes within the carriage, the smoke and toxic gases produced during the combustion of this medium-risk material could delay evacuation, posing a certain safety hazard. Recommendations: It is recommended to add magnesium hydroxide flame retardant to this material to reduce the heat release rate and CO generation. It should only be used in non-core evacuation areas within the carriage (such as the rear of the carriage), with a maximum usage area of ​​5m² per carriage.

[0063] For example, the preset hazard level correction coefficients determined based on the in-vehicle wind speed of the rail vehicle are shown in Table 2 below:

[0064]

[0065] The fire hazard determination method provided in this application obtains multi-dimensional first indicator data to fully restore the true hazardous characteristics of raw materials in a fire, providing comprehensive and objective basic data support for subsequent risk assessment. Then, by determining the first weight corresponding to each first indicator data, the influence coefficient of the indicator on the hazard level is quantified, making the assessment results more aligned with the actual safety needs of rail transit. Finally, a hazard correction coefficient is introduced in conjunction with the specific application scenarios of rail transit, constructing a systematic material fire risk assessment system. Ultimately, this achieves accurate and scientific determination of the fire hazard level of target materials in rail transit scenarios, thereby effectively improving the fire safety level of rail transit.

[0066] Figure 2 A flowchart illustrating the method for determining the degree of fire hazard provided in this application embodiment. Figure 2 .like Figure 2 As shown, the method includes:

[0067] S201. Obtain multiple primary indicator data corresponding to multiple dimensions of the target material, and the multiple primary indicator data are associated with the degree of danger corresponding to the target material when a fire occurs.

[0068] S202. Based on multiple first indicator data, determine the first weight corresponding to each first indicator data. The first weight is used to indicate the influence coefficient of the first indicator data on the degree of danger corresponding to the target material when a fire occurs.

[0069] In one possible implementation, multiple first indicator data are standardized to obtain multiple second indicator data; based on the multiple second indicator data, multiple corresponding first weights are determined.

[0070] Specifically, for primary indicator data of different dimensions, some primary indicator data (such as peak heat release rate) show a positive correlation with the degree of fire hazard, while others (such as material flame retardancy rating) show a negative correlation with the degree of fire hazard. Therefore, different standardization methods are used to standardize primary indicator data with positive or negative correlation trends.

[0071] For example, the first indicator data with a positive correlation trend is standardized using the following formula, including:

[0072]

[0073] in, For the j-th dimension of the i-th target material; For the j-th dimension of the i-th target material; This is the first indicator data for the j-th dimension.

[0074] Furthermore, the data for the first indicator with a negative correlation trend are standardized using the following formula:

[0075]

[0076] in, For the j-th dimension of the i-th target material; For the j-th dimension of the i-th target material; This is the first indicator data for the j-th dimension.

[0077] For example, several second indicator data are shown in Table 3 below:

[0078]

[0079] Based on multiple secondary indicator data, the corresponding proportions of each secondary indicator data are determined. For example, multiple target materials can be introduced, and the proportions satisfy the following formula:

[0080]

[0081] in, The proportion of the j-th dimension of the i-th target material; is the second index data of the j-th dimension of the i-th target material; m is the total number of target materials.

[0082] Based on multiple proportions, the corresponding entropy value is determined. For example, the entropy value satisfies the following formula:

[0083]

[0084] in, represents the proportion of the j-th dimension of the i-th target material; m represents the total number of target materials; Let be the entropy value of the j-th dimension.

[0085] Based on multiple entropy values, multiple corresponding first weights are determined. For example, the first weights satisfy the following formula:

[0086]

[0087]

[0088] in, The first weight corresponds to the second indicator data in the j-th dimension; Let be the entropy value of the j-th dimension; The difference coefficient of the j-th dimension.

[0089] For example, the entropy values, difference coefficients, and weights of the second indicator data corresponding to multiple dimensions are shown in Table 4 below:

[0090]

[0091] S203. Based on multiple primary indicator data, determine multiple correlation coefficients. The correlation coefficients are used to indicate the correlation between primary indicator data in any two dimensions.

[0092] For example, the correlation coefficients of multiple primary indicator data corresponding to multiple dimensions are as follows: Figure 5 As shown, Figure 5 The example graph of correlation coefficients provided in the embodiments of this application shows that the warmer the color (red), the higher the correlation, and the cooler the color (blue), the lower the correlation.

[0093] In one possible implementation, the correlation coefficient satisfies the following formula:

[0094]

[0095] in, The correlation coefficient is the relationship between any first indicator data X in any dimension and any first indicator data Y in a different dimension from X. Let X be the covariance of Y; It is the product of the standard deviations of X and Y.

[0096] To make the correlation coefficient more accurate, we can introduce the first indicator data of m materials corresponding to the X and Y dimensions. The correlation coefficient also satisfies the following formula:

[0097]

[0098] in, The correlation coefficient is the relationship between any first indicator data X in any dimension and any first indicator data Y in a different dimension from X. Let X be the covariance of Y; The product of the standard deviations of X and Y; For any dimension of the i-th material, the first index data is used. The first index data of any dimension of the i-th material that is different from X; The average value of the first index data for the X dimension corresponding to the m types of materials; This represents the average value of the first index data in the Y dimension for the m types of materials.

[0099] S204. Based on multiple correlation coefficients and the first weights corresponding to each first indicator data, determine multiple second weights.

[0100] In one possible implementation, if the correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the two corresponding first weights are merged to obtain a second weight; otherwise, the corresponding second weight is determined as the first weight.

[0101] For example, the preset correlation coefficient threshold is 0.8, such as... Figure 5 As shown, the correlation coefficient between PSM and TSM is 0.8. At this point, the two corresponding first weights are combined to obtain the second weight, which satisfies the following formula:

[0102]

[0103] in, As the second weight; This is the first weight corresponding to PSM; This is the first weight corresponding to TSM.

[0104] For example, several second weights are shown in Table 5 below:

[0105]

[0106] S205. Based on multiple primary indicator data, multiple secondary weights, and a preset hazard correction coefficient, determine the hazard level corresponding to the target material when it is involved in a fire.

[0107] In one possible implementation, the degree of danger corresponding to a fire involving the target material satisfies the following formula:

[0108]

[0109] in, The degree of danger corresponding to a fire involving the target material; The first indicator data for the j-th dimension; The first weight of the j-th dimension; The preset hazard level correction factor is determined by ventilation level a, personnel density level b, and ambient temperature level c.

[0110] It should be noted that the degree of danger corresponding to a fire involving the target material, as determined in S205, also satisfies the above formula. It represents the second weight of the j-th dimension.

[0111] For example, the degree of danger corresponding to the target material when it catches fire is shown in Table 6 below:

[0112]

[0113] To fully reflect the impact of material hazards on rail vehicles, it may be necessary to consider the area where the material is applied. Table 7 below shows the application area and normalized values ​​of different materials on a specific rail vehicle.

[0114]

[0115] It is possible to determine the fire hazard of different materials based on the normalized area shown in Table 7 and the hazard levels of different materials shown in Table 6, as shown in Table 8 below:

[0116]

[0117] The fire hazard determination method provided in this application obtains multi-dimensional first indicator data to fully restore the true hazardous characteristics of raw materials in a fire, providing comprehensive and objective basic data support for subsequent risk assessment. Then, by determining the first weight corresponding to each first indicator data, the influence coefficient of the indicator on the hazard level is quantified, making the assessment results more aligned with the actual safety needs of rail transit. Finally, a hazard correction coefficient is introduced in conjunction with the specific application scenarios of rail transit, constructing a systematic material fire risk assessment system. Ultimately, this achieves accurate and scientific determination of the fire hazard level of target materials in rail transit scenarios, thereby effectively improving the fire safety level of rail transit.

[0118] Because the units and magnitudes of different primary indicators vary greatly (e.g., peak heat release rate is measured in kW / m², while total CO release is measured in g), directly using them for weight calculations would overemphasize the influence of indicators with large units and weaken the role of indicators with small units. Standardization (e.g., mapping data to the 0-1 range) can eliminate interference from units and magnitudes, placing all secondary indicator data on the same comparable dimension. The primary weights determined based on the secondary indicator data only reflect the true impact of the indicator data on the degree of danger, rather than the magnitude differences in the data themselves, thereby improving the accuracy of the primary weights and laying a reliable foundation for subsequent danger calculations.

[0119] In real-world fire scenarios, some indicators exhibit strong correlations (such as total CO release and total smoke release, which often increase simultaneously during combustion). If these highly correlated indicators are calculated with independent weights, related risks will be repeatedly amplified, leading to an overestimation of the final hazard level and misjudgment. By first calculating the correlation coefficient between any two indicators and then adjusting the first weight to obtain the second weight, the correlation between indicators can be effectively identified and addressed. This avoids the impact of repeatedly calculating related indicators, ensuring that the weight allocation better reflects the actual operational logic of various risk factors in a fire, and further improving the scientific rigor and rationality of hazard assessment.

[0120] When the correlation coefficient between two indicators is greater than or equal to a preset threshold (e.g., ≥0.8, indicating a high correlation), the second weight is obtained by merging the corresponding first weights. This avoids the risk amplification problem caused by "double-weighting highly correlated indicators." For example, total CO release and total smoke release are highly correlated. Merging their weights preserves their combined risk impact on personnel poisoning and visual obstruction, while preventing over-calculation of such risks due to independent weighting. When the correlation coefficient is less than the preset threshold, the first weight is directly used as the second weight, ensuring that the independent influence of low-correlation or uncorrelated indicators is not disturbed. This rule of merging as needed and retaining them independently allows the second weight to avoid calculation bias caused by indicator correlation and accurately reflect the actual role of each indicator, ultimately improving the accuracy of the risk determination results.

[0121] Figure 3 This is a schematic diagram of the fire hazard determination device provided in the embodiments of this application, as shown below. Figure 3 As shown, the fire hazard determination device 30 provided in this embodiment includes an acquisition module 301, a processing module 302, and a determination module 303.

[0122] The acquisition module 301 is used to acquire multiple first indicator data corresponding to multiple dimensions of the target material. The multiple first indicator data are associated with the degree of danger when the target material is in a fire.

[0123] Processing module 302 is used to determine the first weight corresponding to each first indicator data based on multiple first indicator data. The first weight is used to indicate the influence coefficient of the first indicator data on the degree of danger corresponding to the target material when a fire occurs.

[0124] The determination module 303 is used to determine the degree of danger of a target material when it is in fire, based on multiple first indicator data, the first weight corresponding to each first indicator data, and a preset danger correction coefficient.

[0125] In one possible implementation, the processing module 302 is specifically used for:

[0126] The data for multiple primary indicators are standardized to obtain multiple data for secondary indicators.

[0127] Based on multiple secondary indicator data, corresponding primary weights are determined.

[0128] In one possible implementation, the determining module 303 is specifically used for:

[0129] Based on multiple primary indicator data, multiple correlation coefficients are determined. The correlation coefficients are used to indicate the correlation between primary indicator data in any two dimensions.

[0130] Based on multiple correlation coefficients and the first weights corresponding to each first indicator data, multiple second weights are determined.

[0131] Based on multiple primary indicator data, multiple secondary weights, and preset hazard correction coefficients, the hazard level corresponding to the target material when it is involved in a fire is determined.

[0132] In one possible implementation, the determining module 303 is specifically used for:

[0133] If the correlation coefficient is greater than or equal to the preset correlation coefficient threshold, then the two corresponding first weights are merged to obtain the second weight;

[0134] Otherwise, the corresponding second weight is determined as the first weight.

[0135] In one possible implementation, the degree of danger corresponding to a fire involving the target material satisfies the following formula:

[0136]

[0137] in, The degree of danger corresponding to a fire involving the target material; The first indicator data for the j-th dimension; The first weight of the j-th dimension; The preset hazard level correction factor is determined by ventilation level a, personnel density level b, and ambient temperature level c.

[0138] In one possible implementation, the correlation coefficient is shown in the following formula:

[0139]

[0140] in, The correlation coefficient is the relationship between any first indicator data X in any dimension and any first indicator data Y in a different dimension from X. Let X be the covariance of Y; It is the product of the standard deviations of X and Y.

[0141] The fire hazard determination device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0142] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus.

[0143] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0144] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0145] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0146] The memory may include high-speed memory (Random Access Memory, RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0147] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0148] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0149] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0150] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0151] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0152] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0155] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0157] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining the degree of fire hazard, characterized in that, include: Acquire multiple first indicator data corresponding to multiple dimensions of the target material, and the multiple first indicator data are associated with the degree of danger corresponding to the target material when a fire occurs; Based on the multiple first indicator data, a first weight corresponding to each first indicator data is determined. The first weight is used to indicate the influence coefficient of the first indicator data on the degree of danger corresponding to the target material when a fire occurs. Based on the multiple first indicator data, the first weight corresponding to each first indicator data, and the preset danger correction coefficient, the danger level corresponding to the target material when it is in fire is determined.

2. The method according to claim 1, characterized in that, The step of determining the first weight corresponding to each first indicator data based on the plurality of first indicator data includes: The multiple first indicator data are standardized to obtain multiple second indicator data. Based on the multiple second indicator data, the corresponding multiple first weights are determined.

3. The method according to claim 1, characterized in that, The determination of the degree of danger of the target material in the event of a fire, based on the plurality of first indicator data, the first weight corresponding to each first indicator data, and a preset degree of danger correction coefficient, includes: Based on the multiple first indicator data, multiple correlation coefficients are determined, and the correlation coefficients are used to indicate the correlation between the first indicator data of any two dimensions. Based on the multiple correlation coefficients and the first weights corresponding to each first indicator data, multiple second weights are determined; Based on the multiple first indicator data, the multiple second weights, and the preset hazard correction coefficient, the hazard level corresponding to the target material when it is involved in a fire is determined.

4. The method according to claim 3, characterized in that, The determination of multiple second weights based on the multiple correlation coefficients and the first weights corresponding to each first indicator data includes: If the correlation coefficient is greater than or equal to a preset correlation coefficient threshold, then the two corresponding first weights are merged to obtain a second weight; Otherwise, the corresponding second weight is determined to be the first weight.

5. The method according to claim 1, characterized in that, The degree of danger corresponding to the target material when it is involved in a fire satisfies the following formula: in, The degree of danger corresponding to a fire involving the target material; The first indicator data for the j-th dimension; The first weight of the j-th dimension; The preset hazard level correction factor is determined by ventilation level a, personnel density level b, and ambient temperature level c.

6. The method according to claim 3, characterized in that, The correlation coefficient satisfies the following formula: in, The correlation coefficient is the relationship between any first indicator data X in any dimension and any first indicator data Y in a different dimension from X. Let X be the covariance of Y; It is the product of the standard deviations of X and Y.

7. A device for determining the degree of fire hazard, characterized in that, include: The acquisition module is used to acquire multiple first indicator data corresponding to multiple dimensions of the target material, and the multiple first indicator data are associated with the degree of danger corresponding to the target material when a fire occurs. The processing module is used to determine a first weight corresponding to each first indicator data based on the plurality of first indicator data, wherein the first weight is used to indicate the influence coefficient of the first indicator data on the degree of danger corresponding to the target material when a fire occurs; The determination module is used to determine the degree of danger of the target material when it is in fire, based on the plurality of first indicator data, the first weight corresponding to each first indicator data and the preset degree of danger correction coefficient.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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