Electrical fire monitoring prediction method and apparatus

Abstract

The application relates to the technical field of fire monitoring, in particular to an electrical fire monitoring and prediction method and device, wherein the electrical fire monitoring and prediction method comprises the following steps: acquiring temperature parameters, pyrolysis particle concentration parameters and air quality parameters of a monitored object in multiple calculation periods; calculating the change trend and the danger coefficient of each parameter according to each parameter in the multiple calculation periods; and predicting whether a fire occurs at the monitored object based on the calculated change trend and the danger coefficient of each parameter. The application can improve the accuracy of fire prediction and reduce the false alarm rate.

Description

Technical Field

[0001] The present invention relates to the technical field of fire monitoring, and in particular to an electrical fire monitoring and prediction method and device. Background Art

[0002] Currently, the vast majority of existing electrical fire monitoring solutions utilize devices such as residual current electrical fire detectors, temperature-based detectors, arc fault detectors, and pyrolytic particle detectors. These detectors detect diverse data types and have different functions. Existing electrical fire monitoring equipment often monitors only a single parameter, making it susceptible to interference from external factors (such as environmental and human factors), leading to false alarms. These false alarms result in unnecessary waste of manpower and material resources. Summary of the Invention

[0003] The present invention provides an electrical fire monitoring and prediction method and device, which can improve the accuracy of fire prediction and reduce the false alarm rate.

[0004] In order to solve the above problems, the present invention adopts the following technical solutions:

[0005] According to a first aspect of the present invention, an embodiment of the present invention provides an electrical fire monitoring and prediction method, comprising the following steps:

[0006] Obtain temperature parameters, pyrolysis particle concentration parameters and air quality parameters of the monitored object within multiple calculation cycles;

[0007] Calculate the change trend and risk factor of each parameter based on the parameters in multiple calculation cycles;

[0008] Based on the calculated change trends and risk factors of various parameters, it is predicted whether a fire will occur at the monitored object.

[0009] In some embodiments, the step of predicting whether a fire has occurred at the monitored object based on the calculated change trends and hazard coefficients of the various parameters specifically includes: when the change trend of any parameter is gradually increasing and the ratio of the hazard coefficient to the standard threshold is greater than the set value, determining that a fire has occurred at the monitored object; or, when the change trend of any parameter is relatively constant and the hazard coefficient is greater than the standard threshold, determining that a fire has occurred at the monitored object.

[0010] In some embodiments, the step of calculating the changing trend of each parameter specifically includes: first calculating the overall trend f(t) of each parameter,

[0011]

[0012] Each parameter has 2n sampling data points in a calculation cycle, n ≥ 2; t is the time at the end of a calculation cycle; Sa(i) is the real-time value of each parameter at the sampling time;

[0013] Then calculate the subsequent trend g(t) of each parameter based on the data of the last 1 / 4 segment of the calculation cycle.

[0014]

[0015] make g(t)=G={def}, then multiply the matrix F and G to get the changing trend of each parameter Among them, the nine results of F*G represent nine changing trends of the parameters, H1 is a gradual increase trend, H2 is a gradual increase and then stable trend, H3 is a gradual increase, then maintain, and finally a gradual decrease trend, H4 is a periodic change trend, H5 is a relatively constant trend, H6 is a short-term increase and then return to the initial state trend, H7, H8 and H9 are all load reduction trends.

[0016] In some embodiments, the step of calculating the risk factor specifically includes: the risk factor K is calculated as follows:

[0017]

[0018] Where i is any sampling moment or multiple consecutive sampling moments within a calculation cycle; when i is any sampling moment within a calculation cycle, PMx(i) is the PM value at the i sampling moment, and T(i) is the temperature value at the i sampling moment; when i is multiple consecutive sampling moments within a calculation cycle, PMx(i) and T(i) are both the average values ​​of the multiple consecutive sampling moments.

[0019] In some embodiments, the following steps are further included: after determining that a fire has occurred at the monitored object, determining the danger level of the fire, and issuing a corresponding fire alarm according to the danger level of the fire.

[0020] According to a second aspect of the present invention, an embodiment of the present invention provides an electrical fire monitoring and prediction device, comprising:

[0021] A parameter acquisition module is used to obtain temperature parameters, pyrolysis particle concentration parameters and air quality parameters of the monitored object within multiple calculation cycles;

[0022] Comprehensive calculation module, used to calculate the change trend and risk factor of each parameter based on the parameters in multiple calculation cycles;

[0023] The fire prediction module is used to predict whether a fire will occur at the monitored object based on the calculated change trends and risk factors of various parameters.

[0024] In some embodiments, when the change trend of any parameter is gradually increasing and the ratio of the risk factor to the standard threshold is greater than the set value, the fire prediction module determines that a fire has occurred at the monitored object; or, when the change trend of any parameter is relatively constant and the risk factor is greater than the standard threshold, the fire prediction module determines that a fire has occurred at the monitored object.

[0025] In some embodiments, the comprehensive calculation module calculates the change trend of each parameter according to the following method: first calculate the overall trend f(t) of each parameter,

[0026]

[0027] Each parameter has 2n sampling data points in a calculation cycle, n ≥ 2; t is the time at the end of a calculation cycle; Sa(i) is the real-time value of each parameter at the sampling time;

[0028] Then calculate the subsequent trend g(t) of each parameter based on the data of the last 1 / 4 segment of the calculation cycle.

[0029]

[0030] make g(t)=G={def}, then multiply the matrix F and G to get the changing trend of each parameter Among them, the nine results of F*G represent nine changing trends of the parameters, H1 is a gradual increase trend, H2 is a gradual increase and then stable trend, H3 is a gradual increase, then maintain, and finally a gradual decrease trend, H4 is a periodic change trend, H5 is a relatively constant trend, H6 is a short-term increase and then return to the initial state trend, and H7, H8 and H9 are all load reduction trends.

[0031] In some embodiments, the comprehensive calculation module calculates the risk factor according to the following method: The risk factor K is calculated as follows:

[0032]

[0033] Where i is any sampling moment or multiple consecutive sampling moments within a calculation cycle; when i is any sampling moment within a calculation cycle, PMx(i) is the PM value at the i sampling moment, and T(i) is the temperature value at the i sampling moment; when i is multiple consecutive sampling moments within a calculation cycle, PMx(i) and T(i) are both the average values ​​of the multiple consecutive sampling moments.

[0034] In some embodiments, a fire alarm module is further included, which is used to determine the danger level of the fire after determining that a fire has occurred at the monitored object, and to issue a corresponding fire alarm based on the danger level of the fire.

[0035] The present invention has at least the following beneficial effects: the present invention obtains the temperature parameters, pyrolysis particle concentration parameters and air quality parameters of the monitored object within multiple calculation cycles, and then calculates the change trend and hazard coefficient of each parameter based on the various parameters within the multiple calculation cycles. Finally, based on the calculated change trend and hazard coefficient of each parameter, it predicts whether a fire will occur at the monitored object. Since multiple parameters are used and the change trend and hazard coefficient of the comprehensive reference parameters are taken into account, the interference of external factors can be greatly reduced, thereby improving the accuracy of fire prediction and reducing the false alarm rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic structural diagram of an electrical fire monitoring system according to an embodiment of the present invention;

[0037] Figure 2 A flow chart of an electrical fire monitoring and prediction method according to an embodiment of the present invention;

[0038] Figure 3 Schematic diagram of the nine changing trends of the present invention;

[0039] Figure 4 A schematic diagram of a module of an electrical fire monitoring and prediction device according to an embodiment of the present invention;

[0040] Figure 5 This is a module diagram of an electrical fire monitoring and prediction device according to another embodiment of the present invention.

[0041] Wherein, the accompanying drawings are marked as follows:

[0042] Temperature detection module 11, pyrolysis particle detection module 12, air quality detection module 13, main control module 20, power module 21, linkage control output module 22;

[0043] Parameter acquisition module 100, comprehensive calculation module 200, fire prediction module 300, fire alarm module 400. DETAILED DESCRIPTION

[0044] The following description of the present invention is provided with reference to the accompanying drawings to facilitate a more comprehensive understanding of the various embodiments of the present invention as defined in the claims and their equivalents. The description includes various specific details to assist understanding, but these details are to be construed as merely exemplary. Therefore, those skilled in the art will appreciate that various changes and modifications may be made to the various embodiments described herein without departing from the scope and spirit of the present invention.

[0045] In the description of the present invention, descriptions of orientations, such as up, down, front, back, left, and right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the present invention.

[0046] It will be understood that when one element (e.g., a first element) is “connected” to another element (e.g., a second element), the element may be directly connected to the other element or an intervening element (e.g., a third element) may be present between the element and the other element.

[0047] Before describing the method of the present invention in detail, it is necessary to describe the hardware structure of the electrical fire monitoring and prediction system of the present invention. Figure 1 As shown, the electrical fire monitoring and prediction system includes a temperature detection module 11, a pyrolysis particle detection module 12, an air quality detection module 13, a main control module 20, and a power supply module 21. The temperature detection module 11, the pyrolysis particle detection module 12, the air quality detection module 13, and the power supply module 21 are all connected to the main control module 20.

[0048] The power supply module 21 provides electrical energy to the main control module 20 and other modules that require electricity, so that these modules can be powered on and operate. The temperature detection module 11 includes a temperature sensor, which is fixed on the surface, inside or near the monitored object to detect the temperature in real time or at a fixed time, and send the detected temperature value to the main control module 20. The pyrolytic particle detection module 12 is used to detect the concentration of pyrolytic particles in the environment of the monitored object, and send the detected pyrolytic particle concentration to the main control module 20. The air quality detection module 13 is used to detect the concentration of specific gases (such as hydrogen, toluene, etc.) in the air and / or PM values ​​and other parameters that characterize the air quality in real time or at a fixed time, and send the detected concentration of specific gases and / or PM values ​​to the main control module 20. The main control module 20 may include a control chip such as a single-chip microcomputer, which is used to calculate and process the various parameters obtained according to a set control program, and predict whether a fire occurs.

[0049] Of course, the electrical fire monitoring and prediction system of the present invention may also include a linkage control output module 22 connected to the main control module 20. When the main control module 20 predicts a fire, it will also notify the linkage control output module 22, which will then distribute the message to a host, server, or alarm device.

[0050] The embodiment of the present invention provides an electrical fire monitoring and prediction method, which will be described from the perspective of the main control module. Figure 2 As shown, the following steps are included:

[0051] S100: Acquire temperature parameters, pyrolysis particle concentration parameters, and air quality parameters of a monitored object within multiple calculation cycles.

[0052] The duration of each calculation cycle is set in advance. Each calculation cycle has multiple sampling moments. Temperature parameters, pyrolysis particle concentration parameters and air quality parameters are sampled at each sampling moment, so that data of multiple calculation cycles can be obtained. Each calculation cycle has multiple temperature parameters, pyrolysis particle concentration parameters and air quality parameters.

[0053] S200: Calculating the change trend and risk factor of each parameter based on the parameters in multiple calculation cycles.

[0054] Since the occurrence of fire will inevitably lead to changes in temperature parameters, pyrolysis particle concentration parameters and air quality parameters, the changing trends of temperature parameters, pyrolysis particle concentration parameters and air quality parameters will accordingly indicate whether a fire will occur. Therefore, the changing trends of these parameters are very important, and the hazard factor is a reflection of whether these changing trends are dangerous.

[0055] S300: Predicting whether a fire occurs at the monitored object based on the calculated change trends of various parameters and the risk factor.

[0056] By comprehensively judging the changing trends and hazard factors of various parameters, a fire is predicted at the monitored object. Therefore, this embodiment utilizes multiple parameters that can represent the fire situation, rather than a single parameter. By comprehensively considering the changing trends and hazard factors of the reference parameters, the interference of external factors can be greatly reduced, thereby improving the accuracy of fire prediction and reducing the false alarm rate.

[0057] In some embodiments, step S300 specifically includes: when the change trend of any parameter is gradually increasing and the ratio of the risk factor to the standard threshold is greater than the set value, determining that a fire has occurred at the monitored object; or, when the change trend of any parameter is relatively constant and the risk factor is greater than the standard threshold, determining that a fire has occurred at the monitored object.

[0058] When the parameter's trend is gradually increasing, it indicates a high probability of fire. To avoid misjudgments, the ratio of the hazard factor to the standard threshold is further used for judgment. Both the standard threshold and the set value can be pre-set based on historical data or experience. If the ratio of the hazard factor to the standard threshold is greater than the set value, a fire is determined to have occurred at the monitored object. If the ratio is less than or equal to the set value, a fire is not determined to have occurred at the monitored object. This preset value is less than 1. Since the probability of fire is already high, the hazard factor does not need to be equal to or greater than the standard threshold. For example, the preset value can be 0.8.

[0059] If the parameter's trend is relatively constant, meaning the change in the parameter's value is minimal, the monitored object may be operating normally with no anomalies. Alternatively, it may be in a stable fire phase, with minimal parameter change. In this case, the hazard factor is needed for further assessment. If the hazard factor is greater than the threshold, a fire is confirmed at the monitored object. If the hazard factor is less than or equal to the threshold, no fire is confirmed at the monitored object.

[0060] Except for the above two situations, it is generally believed that no fire has occurred at the monitored object in other situations.

[0061] Furthermore, the step of calculating the changing trend of each parameter in step S200 specifically includes: first calculating the overall trend f(t) of each parameter, where the overall trend is the overall reflection of the parameter change in a calculation cycle. Specifically, it is calculated by the following formula:

[0062]

[0063] Each parameter has 2n sampling data points in a calculation cycle, n ≥ 2; t is the time at the end of a calculation cycle; Sa(i) is the real-time value of each parameter at the sampling time;

[0064] Then calculate the subsequent trend g(t) of each parameter based on the data of the last 1 / 4 segment of the calculation cycle.

[0065]

[0066] make g(t)=G={def}, then multiply the matrix F and G to get the changing trend of each parameter Among them, the 9 results of F*G represent 9 changing trends of the parameters.

[0067] like Figure 3 As shown, H1 is a gradual growth trend. The temperature, pyrolysis particle concentration and harmful gas content suddenly and sharply increase from the lower normal values, indicating that there is a high possibility of fire at the monitored object. For this situation, a comprehensive analysis is made in combination with the hazard coefficient value. If the ratio of the hazard coefficient to the standard threshold is greater than the set value, it is determined that a fire has occurred at the monitored object.

[0068] H2 first gradually increases and then maintains a stable trend. This may be because the line load is operating at the rated power or a higher power close to the rated power. The temperature starts to rise from a low temperature and then stabilizes in a certain range. The concentration of pyrolytic particles and the content of harmful gases also tend to be stable. In this case, it is not considered that a fire has occurred at the monitored object.

[0069] H3 shows a trend of gradually increasing, maintaining, and finally decreasing. This may be because the load increases in a certain period of time, and the power consumption decreases after a period of time, which causes the various parameters to gradually increase, maintain, and finally decrease. This situation is also normal and does not indicate a fire at the monitored object.

[0070] H4 is a periodic change trend, which may be due to a periodic rise and fall of the load. This situation is also normal and does not indicate a fire at the monitored object.

[0071] H5 is a relatively constant trend. The numerical value of the parameter changes very little and remains near a constant value. This indicates that the monitored object may be in normal working condition without abnormality, or it may be in a stable period of fire. Therefore, it is necessary to further judge it in combination with the risk factor.

[0072] H6 shows a trend of first increasing for a short time and then returning to the initial state. This may be because the load increases in a short time and then returns to the initial state. This situation is also normal and does not indicate a fire at the monitored object.

[0073] H7, H8, and H9 all show a load reduction trend, and the temperature, pyrolysis particle concentration, and harmful gas content gradually decrease. At this time, the probability of a fire is extremely low, so it is not considered that a fire has occurred at the monitored object.

[0074] In some embodiments, the step of calculating the risk factor in step S200 specifically includes: the calculation method of the risk factor K is as follows:

[0075]

[0076] Where i is any sampling moment or multiple consecutive sampling moments within a calculation cycle; when i is any sampling moment within a calculation cycle, PMx(i) is the PM value at the i sampling moment, and T(i) is the temperature value at the i sampling moment; when i is multiple consecutive sampling moments within a calculation cycle, PMx(i) and T(i) are both the average values ​​of the multiple consecutive sampling moments.

[0077] K is the correlation coefficient between gas particle content and temperature. Since rising temperature is positively correlated with gas particle content, it is defined as the hazard factor K. The PM value can be the sum of PM1.0, PM2.5, and PM10 particle content, or any of these. T is the temperature. When the temperature rises to a certain value (for example, a cable exceeding 80°C), the monitored object will decompose into particles of various sizes and some large, hazardous gases. Generally, a standard threshold for the hazard factor is 1.2.

[0078] In some embodiments, after step S300, the following steps are further included: after determining that a fire has occurred at the monitored object, determining the danger level of the fire, and issuing a corresponding fire alarm according to the danger level of the fire.

[0079] After a fire is confirmed, this embodiment can issue a corresponding fire alarm, so that relevant personnel can be informed of the fire situation in a timely manner and take timely response measures to nip the fire risk in the bud.

[0080] Fires are also categorized into different danger levels, with different fire alarms corresponding to each danger level. For example, when the danger level is high, a control signal can be sent to the audible and visual alarm, causing it to sound and visually alarm. This signal can also be used to alert fire rescue departments such as fire stations and hospitals through a reserved port, generating and recording a high-risk alarm event. When the danger level is low, a control signal can be sent to the audible and visual alarm, causing it to sound and visually alarm, generating and recording a low-risk alarm event. This allows for timely and targeted rescue measures for different fire situations, avoiding insufficient rescue resources and unnecessary waste of manpower and material resources.

[0081] The hazard level can be determined based on the hazard coefficient. Specifically, a mapping relationship between the hazard level and the hazard coefficient can be established. Each hazard level has a corresponding hazard coefficient range. The hazard coefficient range is determined, and then the hazard level is determined.

[0082] The embodiment of the present invention also provides an electrical fire monitoring and prediction device, such as Figure 4 Shown, including:

[0083] The parameter acquisition module 100 is used to obtain the temperature parameters, pyrolysis particle concentration parameters and air quality parameters of the monitored object within multiple calculation cycles;

[0084] Comprehensive calculation module 200, for calculating the change trend and risk factor of each parameter based on the parameters in multiple calculation cycles;

[0085] The fire prediction module 300 is used to predict whether a fire occurs at the monitored object based on the calculated change trends and risk factors of various parameters.

[0086] In some embodiments, when the change trend of any parameter is gradually increasing and the ratio of the hazard coefficient to the standard threshold is greater than the set value, the fire prediction module 300 determines that a fire has occurred at the monitored object; or, when the change trend of any parameter is relatively constant and the hazard coefficient is greater than the standard threshold, the fire prediction module 300 determines that a fire has occurred at the monitored object.

[0087] In some embodiments, the comprehensive calculation module 200 calculates the variation trend of each parameter according to the following method: first calculate the overall trend f(t) of each parameter,

[0088]

[0089] Each parameter has 2n sampling data points in a calculation cycle, n ≥ 2; t is the time at the end of a calculation cycle; Sa(i) is the real-time value of each parameter at the sampling time;

[0090] Then calculate the subsequent trend g(t) of each parameter based on the data of the last 1 / 4 segment of the calculation cycle.

[0091]

[0092] make g(t)=G={def}, then multiply the matrix F and G to get the changing trend of each parameter Among them, the nine results of F*G represent nine changing trends of the parameters, H1 is a gradual increase trend, H2 is a gradual increase and then stable trend, H3 is a gradual increase, then maintain, and finally a gradual decrease trend, H4 is a periodic change trend, H5 is a relatively constant trend, H6 is a short-term increase and then return to the initial state trend, and H7, H8 and H9 are all load reduction trends.

[0093] In some embodiments, the comprehensive calculation module 200 calculates the risk factor according to the following method: The risk factor K is calculated as follows:

[0094]

[0095] Where i is any sampling moment or multiple consecutive sampling moments within a calculation cycle; when i is any sampling moment within a calculation cycle, PMx(i) is the PM value at the i sampling moment, and T(i) is the temperature value at the i sampling moment; when i is multiple consecutive sampling moments within a calculation cycle, PMx(i) and T(i) are both the average values ​​of the multiple consecutive sampling moments.

[0096] In some embodiments, as Figure 5 As shown, the electrical fire monitoring and prediction device of this embodiment further includes a fire alarm module 400, which is used to determine the fire danger level after determining that a fire has occurred at the monitored object, and to issue a corresponding fire alarm according to the fire danger level.

[0097] The above-mentioned embodiment of the electrical fire monitoring and prediction device is an embodiment of the device class corresponding to the electrical fire monitoring and prediction method. For the specific description of the electrical fire monitoring and prediction device, please refer to the above-mentioned embodiment of the electrical fire monitoring and prediction method, which will not be repeated here.

[0098] The terms and words used in the above description and claims are not limited to the literal meanings, but are merely used by the applicant to enable a clear and consistent understanding of the present invention. Therefore, it should be clear to those skilled in the art that the above description of various embodiments of the present invention is provided for illustration only and is not intended to limit the present invention as defined by the appended claims and their equivalents.

Claims

1. A method for monitoring and predicting electrical fires, characterized in that: The steps include: Obtain temperature parameters, pyrolysis particle concentration parameters and air quality parameters of the monitored object within multiple calculation cycles; Calculate the change trend and risk factor of each parameter based on the parameters in multiple calculation cycles; Based on the calculated change trends and risk factors of various parameters, predict whether a fire will occur at the monitored object; The step of calculating the changing trend of each parameter specifically includes: first calculating the overall trend f(t) of each parameter, , where each parameter has 2n sampling data points in a calculation cycle, n ≥ 2; t is the time at the end of a calculation cycle; Sa(i) is the real-time value of each parameter at the sampling time; Then calculate the subsequent trend g(t) of each parameter based on the data of the last 1 / 4 segment of the calculation cycle. , let f(t)=F= , g(t)=G={def}, and then multiply the matrix F and G to get the change trend of each parameter F*G= · = , where the 9 results of F*G represent 9 changing trends of the parameters, H1 is a gradual increase trend, H2 is a gradual increase and then stable trend, H3 is a gradual increase, then maintain, and finally a gradual decrease trend, H4 is a periodic change trend, H5 is a relatively constant trend, H6 is a short-term increase and then return to the initial state trend, H7, H8 and H9 are all load reduction trends.

2. The electrical fire monitoring and prediction method according to claim 1, characterized in that: The step of predicting whether a fire has occurred at the monitored object based on the calculated change trends and hazard coefficients of the various parameters specifically includes: when the change trend of any parameter is gradually increasing and the ratio of the hazard coefficient to the standard threshold is greater than a set value, determining that a fire has occurred at the monitored object; or, when the change trend of any parameter is relatively constant and the hazard coefficient is greater than the standard threshold, determining that a fire has occurred at the monitored object.

3. The electrical fire monitoring and prediction method according to claim 2, characterized in that: The steps for calculating the risk factor include: The risk factor K is calculated as follows, K= ; Wherein, i is any sampling moment or multiple consecutive sampling moments within a calculation cycle; when i is any sampling moment within a calculation cycle, PMx(i) is the PM value at the i sampling moment, and T(i) is the temperature value at the i sampling moment; when i is multiple consecutive sampling moments within a calculation cycle, PMx(i) and T(i) are both the average values ​​of the multiple consecutive sampling moments.

4. The electrical fire monitoring and prediction method according to any one of claims 1 to 3, characterized in that: The method further includes the following steps: after determining that a fire has occurred at the monitored object, determining the danger level of the fire, and issuing a corresponding fire alarm according to the danger level of the fire.

5. An electrical fire monitoring and prediction device, characterized in that: include: A parameter acquisition module is used to obtain temperature parameters, pyrolysis particle concentration parameters and air quality parameters of the monitored object within multiple calculation cycles; Comprehensive calculation module, used to calculate the change trend and risk factor of each parameter based on the parameters in multiple calculation cycles; The fire prediction module is used to predict whether a fire will occur at the monitored object based on the calculated change trends and risk factors of various parameters; The comprehensive calculation module calculates the changing trend of each parameter according to the following method: first calculate the overall trend f(t) of each parameter, , where each parameter has 2n sampling data points in a calculation cycle, n ≥ 2; t is the time at the end of a calculation cycle; Sa(i) is the real-time value of each parameter at the sampling time; Then calculate the subsequent trend g(t) of each parameter based on the data of the last 1 / 4 segment of the calculation cycle. , let f(t)=F= , g(t)=G={def}, and then multiply the matrix F and G to get the change trend of each parameter F*G= · = Among them, the 9 results of F*G represent 9 changing trends of parameters, H1 is a gradual increase trend, H2 is a gradual increase and then stable trend, H3 is a gradual increase, then maintain, and finally a gradual decrease trend, H4 is a periodic change trend, H5 is a relatively constant trend, H6 is a short-term increase and then return to the initial state trend, H7, H8 and H9 are all load reduction trends.

6. The electrical fire monitoring and prediction device according to claim 5, characterized in that: When the change trend of any parameter is gradually increasing and the ratio of the risk factor to the standard threshold is greater than the set value, the fire prediction module determines that a fire has occurred at the monitored object; or when the change trend of any parameter is relatively constant and the risk factor is greater than the standard threshold, the fire prediction module determines that a fire has occurred at the monitored object.

7. The electrical fire monitoring and prediction device according to claim 6, characterized in that: The comprehensive calculation module calculates the risk factor according to the following method: The calculation method of the risk factor K is as follows, K= ; Where i is any sampling moment or multiple consecutive sampling moments within a calculation cycle; when i is any sampling moment within a calculation cycle, PMx(i) is the PM value at the i sampling moment, and T(i) is the temperature value at the i sampling moment; when i is multiple consecutive sampling moments within a calculation cycle, PMx(i) and T(i) are both the average values ​​of the multiple consecutive sampling moments.

8. The electrical fire monitoring and prediction device according to any one of claims 5 to 7, characterized in that: It also includes a fire alarm module, which is used to determine the danger level of the fire after determining that a fire has occurred at the monitored object, and to issue a corresponding fire alarm based on the danger level of the fire.

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