Elevator fireproof intelligent monitoring and early warning method, system and equipment

Abstract

The invention relates to the technical field of elevator fire prevention, and provides an elevator fire prevention intelligent monitoring and early warning method, system and equipment. Building an elevator fireproof sensitive word library; constructing a dynamic sensitive lexicon updating mechanism; obtaining the real-time attention of each sensitive word and a trend value in a preset time period, and adjusting the sensitivity of each sensitive word based on the real-time attention and the trend value; multi-dimensional characteristic parameters of the elevator fireproof area are obtained, and the fire occurrence probability of the elevator fireproof area is calculated according to the multi-dimensional characteristic parameters and the sensitivity; and intelligent monitoring and early warning of elevator fire prevention are achieved according to the fire occurrence probability. According to the method, the multi-dimensional characteristic parameters of the elevator fire prevention area are considered, the fire occurrence probability of the elevator fire prevention area is calculated in combination with the multi-dimensional characteristic parameters and the sensitivity, the probability condition of the fire occurrence of the elevator can be predicted more accurately, and therefore monitoring and early warning of the elevator fire and fire prevention safety are better achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator fire prevention, and more specifically, to an elevator fire prevention intelligent monitoring and early warning method, system and device. Background Art

[0002] In recent years, elevator and freight elevator safety accidents have occurred frequently. At present, with the continuous development of the elevator industry and the improvement of public safety awareness, the requirements for elevator fire prevention safety are getting higher and higher. Currently, the fire prevention measures for elevators generally only set temperature and smoke sensors in the elevator car. When the detected temperature and / or smoke value reaches the set value, the automatically activated fire extinguisher is used to extinguish the fire in the elevator car. The existing elevator fire prevention methods generally can only extinguish the fire after the fire occurs and do not have the ability to monitor and early warn of fire hazards. At this time, the fire has reached a certain level, and the fire extinguishing effect of the fire extinguisher is not ideal. Therefore, how to predict fire hazards before the occurrence of a fire hazard, thereby reducing the difficulty of triggering fire extinguishing, so that the fire extinguishing device can be activated even before the fire occurs, so as to better achieve the fire prevention safety of the elevator has become an urgent problem to be solved in this field.

[0003] Therefore, it is necessary to develop an intelligent monitoring and early warning method for elevator fires to predict the probability of elevator fires, so as to better achieve the fire prevention safety of elevators. Summary of the Invention

[0004] Based on this, in order to solve the problem that the existing elevator fire prevention methods generally can only extinguish the fire after the fire occurs and do not have the ability to monitor and early warn of fire hazards, the present invention provides an elevator fire prevention intelligent monitoring and early warning method, system and device. By obtaining multiple different elevator fire prevention areas, constructing an elevator fire prevention sensitive word library and dynamically updating the elevator fire prevention sensitive word library based on a dynamic sensitive word library update mechanism, the real-time and accuracy of the elevator fire prevention sensitive word library are realized; adjusting the sensitivity of each sensitive word based on the real-time attention degree and trend value, and calculating the probability of a fire occurring in the elevator fire prevention area according to the multi-dimensional characteristic parameters and the sensitivity, which can more accurately predict the probability of an elevator fire, so as to better realize the monitoring and early warning of elevator fires and fire prevention safety. The specific technical solutions are as follows:

[0005] An elevator fire prevention intelligent monitoring and early warning method, which includes the following steps:

[0006] For a target elevator, obtain multiple different elevator fire prevention areas;

[0007] For the elevator fire prevention area, construct multiple different elevator fire prevention sensitive word libraries, and the multiple different elevator fire prevention sensitive word libraries correspond to the multiple different elevator fire prevention areas one by one;

[0008] Build a dynamic sensitive word library update mechanism, and dynamically update the elevator fire protection sensitive word library based on the dynamic sensitive word library update mechanism;

[0009] For each of the elevator fire protection sensitive word libraries, obtain the real-time attention degree of each sensitive word and the trend value within a preset time period, and adjust the sensitivity of each sensitive word based on the real-time attention degree and the trend value;

[0010] Obtain the multi-dimensional characteristic parameters of the elevator fire protection area, and calculate the fire occurrence probability of the elevator fire protection area according to the multi-dimensional characteristic parameters and the sensitivity;

[0011] According to the fire occurrence probability, realize the intelligent monitoring and early warning of elevator fire protection.

[0012] By taking into account the multi-dimensional characteristic parameters of the elevator fire protection area, the elevator fire intelligent monitoring and early warning method combines the multi-dimensional characteristic parameters and the sensitivity to calculate the fire occurrence probability of the elevator fire protection area, which can more accurately predict the probability of elevator fire, so as to better realize the monitoring and early warning of elevator fire and fire safety; in addition, by obtaining multiple different elevator fire protection areas, building an elevator fire protection sensitive word library and dynamically updating the elevator fire protection sensitive word library based on the dynamic sensitive word library update mechanism, the real-time and accuracy of the elevator fire protection sensitive word library are realized, and the accuracy of elevator fire monitoring and early warning is further improved.

[0013] Preferably, the specific method for building a dynamic sensitive word library update mechanism includes the following steps:

[0014] Obtain the age of the target elevator and the first elevator fire frequency in the past historical time period;

[0015] Obtain the second elevator fire frequency in several different radius range areas where the target elevator is located;

[0016] Obtain the third elevator fire probability of the target elevator at the current moment;

[0017] Obtain the sensitive word library update frequency according to the first elevator fire frequency, the second elevator fire frequency and the third elevator fire probability;

[0018] Build a dynamic sensitive word library update mechanism based on the sensitive word library update frequency.

[0019] Preferably, the specific method for obtaining the real-time attention degree of each sensitive word and the trend value within a preset time period includes the following steps:

[0020] Select multiple search engines, and based on the selected multiple search engines, obtain the popularity and search volume of each sensitive word;

[0021] Obtain the search weights of multiple search engines, and based on the search weights, the popularity, and the search volume, obtain the real-time attention degree and the trend value of each sensitive word.

[0022] Preferably, the specific method for calculating the fire occurrence probability of the elevator fire prevention area according to the multi-dimensional characteristic parameters and the sensitivity includes the following steps:

[0023] Construct a mapping relationship between the characteristic parameters and the sensitive words;

[0024] According to the mapping relationship, obtain the regional sensitivity of each dimension characteristic parameter;

[0025] Obtain the real-time characteristic value of each dimension characteristic parameter, and calculate the fire occurrence probability of the elevator fire prevention area according to the real-time characteristic value and the regional sensitivity.

[0026] An elevator fire prevention intelligent monitoring and warning system for implementing the elevator fire prevention intelligent monitoring and warning system, which includes:

[0027] A fire prevention area acquisition module, configured to acquire multiple different elevator fire prevention areas for a target elevator;

[0028] A sensitive word library construction module, configured to construct multiple different elevator fire prevention sensitive word libraries for the elevator fire prevention area, and the multiple different elevator fire prevention sensitive word libraries correspond to the multiple different elevator fire prevention areas one by one;

[0029] A dynamic update module, configured to construct a dynamic sensitive word library update mechanism, and dynamically update the elevator fire prevention sensitive word library based on the dynamic sensitive word library update mechanism;

[0030] A sensitivity acquisition module, configured to, for each elevator fire prevention sensitive word library, acquire the real-time attention degree of each sensitive word and the trend value within a preset time period, and adjust the sensitivity of each sensitive word based on the real-time attention degree and the trend value;

[0031] A fire occurrence probability calculation module, configured to acquire the multi-dimensional characteristic parameters of the elevator fire prevention area, and calculate the fire occurrence probability of the elevator fire prevention area according to the multi-dimensional characteristic parameters and the sensitivity;

[0032] A monitoring and warning module, configured to implement intelligent monitoring and warning of elevator fire prevention according to the fire occurrence probability.

[0033] Preferably, the dynamic update module includes:

[0034] A fire frequency acquisition unit, configured to acquire the age of the target elevator, the first elevator fire frequency in a past historical time period, the second elevator fire frequency in several different radius range areas where the target elevator is located, and the third elevator fire probability at the current moment of the target elevator;

[0035] An update frequency acquisition unit, configured to acquire a sensitive word library update frequency according to the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability;

[0036] A dynamic update unit, configured to construct a dynamic sensitive word library update mechanism based on the sensitive word library update frequency.

[0037] Preferably, the sensitivity acquisition module includes:

[0038] A search engine selection unit, configured to select multiple search engines, and based on the selected multiple search engines, acquire the popularity and search volume of each sensitive word;

[0039] A search weight acquisition unit, configured to acquire the search weights of the multiple search engines, and according to the search weights, the popularity, and the search volume, acquire the real-time attention degree and trend value of each sensitive word.

[0040] Preferably, the fire occurrence probability calculation module includes:

[0041] A mapping relationship construction unit, configured to construct a mapping relationship between feature parameters and sensitive words;

[0042] A regional sensitivity acquisition unit, configured to acquire the regional sensitivity of each dimensional feature parameter according to the mapping relationship;

[0043] A fire occurrence probability calculation unit, configured to acquire the real-time feature value of each dimensional feature parameter, and calculate the fire occurrence probability of the elevator fire prevention area according to the real-time feature value and the regional sensitivity.

[0044] Preferably, the elevator fire prevention intelligent monitoring and warning system includes a smoke sensor and a temperature sensor installed in the elevator fire prevention area, the multi-dimensional feature parameters include a smoke signal and a temperature signal, and the elevator fire prevention area includes an elevator shaft, an elevator car, and an elevator entrance corridor.

[0045] An elevator fire prevention intelligent monitoring and warning device, which includes:

[0046] A memory, storing executable instructions;

[0047] A controller, configured to execute the executable instructions and implement the elevator fire prevention intelligent monitoring and warning method. Description of the Drawings

[0048] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0049] Figure 1 is a schematic diagram of the overall process of an elevator fire prevention intelligent monitoring and early warning method in an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of the process of a specific method for constructing a dynamic sensitive word library update mechanism in an embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of the process of a specific method for obtaining the real-time attention degree and the trend value within a preset time period of each sensitive word in an embodiment of the present invention;

[0052] Figure 4 is a schematic diagram of the process of a specific method for calculating the fire occurrence probability of an elevator fire prevention area in an embodiment of the present invention;

[0053] Figure 5 is a schematic diagram of the overall structure of an elevator fire prevention intelligent monitoring and early warning system in an embodiment of the present invention.

[0054] Figure 6 is a schematic diagram of an elevator fire prevention area in an embodiment of the present invention.

[0055] Reference numerals: 1, elevator car; 2, hoistway; 3, entrance corridor passage. Detailed implementation manners

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with its embodiments. It should be understood that the specific implementation manners described herein are only used to explain the present invention and do not limit the protection scope of the present invention.

[0057] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration and do not represent the only implementation manner.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the description of this invention herein are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0059] In this invention, the "first" and "second" do not represent specific quantities and orders, but are only used for name distinction.

[0060] As Figure 1 shown, an embodiment of this invention provides an elevator fire prevention intelligent monitoring and early warning method, which includes the following steps:

[0061] S1. For a target elevator, obtain multiple different elevator fire prevention areas.

[0062] As Figure 6 shown, for the target elevator, obtain multiple elevator fire prevention areas including but not limited to elevator car 1, hoistway 2, and entrance corridor passage 3. For elevator fires, they often occur in areas such as elevator cars, hoistways, and entrance corridor passages, especially elevator cars and elevator hoistways. Sometimes, there are cartons, debris, and other sundries, as well as trash cans, in the elevator entrance corridor. Due to residents discarding unextinguished cigarette butts or placing flammable items, there are certain fire hazards and a certain probability of causing a fire.

[0063] Obtaining multiple different elevator fire prevention areas for the target elevator has the function of being able to conduct targeted monitoring and early warning for different elevator fire prevention areas, thereby improving the accuracy of elevator fire hazard monitoring and early warning.

[0064] S2. For the elevator fire prevention areas, construct multiple different elevator fire prevention sensitive word libraries, and the multiple different elevator fire prevention sensitive word libraries correspond to the multiple different elevator fire prevention areas one by one. Among them, the elevator fire prevention sensitive word library includes multiple sensitive words. For the sensitive words, they can be understood as target object factors that pose a fire hazard and cause elevator fire accidents, including but not limited to cigarette butts, flammable items, elevator motor current power, ventilation air outlets, and circuits.

[0065] For each elevator fire prevention area, extract the target object factors that may cause elevator fire accidents and construct an elevator fire prevention sensitive word library. Of course, technicians can also list and screen multiple sensitive words to construct an elevator fire prevention sensitive word library.

[0066] By constructing an elevator fire prevention sensitive word library, it helps to monitor the target object factors that may cause fire accidents in elevator fire prevention areas, so as to more comprehensively conduct early warning for elevator fire prevention.

[0067] S3. Build a dynamic sensitive word library update mechanism, and dynamically update the elevator fire prevention sensitive word library based on the dynamic sensitive word library update mechanism.

[0068] Preferably, as Figure 2 shown, in step S3, the specific method for building a dynamic sensitive word library update mechanism includes the following steps:

[0069] S31. Obtain the age of the target elevator and the first elevator fire frequency in the past historical time period.

[0070] S32. Obtain the second elevator fire frequency in several different radius range areas where the target elevator is located;

[0071] S33. Obtain the third elevator fire probability of the target elevator at the current moment;

[0072] S34. Obtain the sensitive word library update frequency according to the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability;

[0073] S35. Build a dynamic sensitive word library update mechanism based on the sensitive word library update frequency.

[0074] The service life of the elevator will cause damage and aging of the lines and electrical components, increasing the probability of fire. The past historical time period and different radius ranges can be set by technicians. The first elevator fire frequency in the past historical time period refers to the frequency of fires and early warnings of fire hazards occurring in the target elevator in the past historical time period. The larger the value of this first elevator fire frequency, the greater the probability of its occurrence of fire.

[0075] In addition, the occurrence of elevator fires or the existence of fire hazards are sometimes related to factors such as the current climate environment of the elevator, the overall fire prevention awareness of the user group, and whether the fire prevention safety publicity and education within the regional scope are in place. The second elevator fire frequency in different radius range areas can be understood as the frequency of elevator fires and early warnings of fire hazards occurring in areas with different radius ranges centered on the location of the target elevator. The third elevator fire probability of the target elevator at the current moment can be understood as the probability of fire and early warnings of fire hazards occurring at the current moment, which can be specifically quantified to a certain day or a continuous period of several hours on the same day. It can be obtained by calculating the weighted average of the ratio between the average value and the standard value of multiple factors that lead to the emergence of fire hazards and the occurrence of fires. These multiple factors include but are not limited to air humidity and temperature. For example, when the weather is dry and the air humidity is low, static electricity accumulation and aging and short - circuit of lines are likely to occur, leading to elevator fires. When the temperature is too high, the operating temperature of electrical components will be too high, causing them to overheat, thereby increasing the possibility of short - circuit fires.

[0076] Obtaining the update frequency of the sensitive word library based on the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability can be understood as using the weighted average of the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability as the update frequency of the sensitive word library, and constructing a dynamic sensitive word library update mechanism based on the update frequency of the sensitive word library, and dynamically updating the sensitive word library according to the update frequency of the sensitive word library.

[0077] Here, by obtaining the update frequency of the sensitive word library through the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability, multiple factors that may lead to fire hazards or fires in the elevator are taken into account, a more practical update frequency of the sensitive word library can be obtained, and a more practical dynamic sensitive word library update mechanism can be constructed, so as to dynamically and real-time update the elevator fire prevention sensitive word library.

[0078] S4. For each of the elevator fire prevention sensitive word libraries, obtain the real-time attention degree of each sensitive word and the trend value within a preset time period, and adjust the sensitivity of each sensitive word based on the real-time attention degree and the trend value.

[0079] Preferably, as Figure 3 shown, in step S4, the specific method for obtaining the real-time attention degree of each sensitive word and the trend value within a preset time period includes the following steps:

[0080] S41. Select multiple search engines, and based on the selected multiple search engines, obtain the popularity and search volume of each sensitive word;

[0081] S42. Obtain the search weights of the multiple search engines, and based on the search weights, the popularity, and the search volume, obtain the real-time attention degree and the trend value of each sensitive word.

[0082] The multiple search engines include but are not limited to Baidu, Sogou, Yahoo, and Toutiao Search. The search weights of the search engines can be obtained based on the rankings, download volumes, or daily active user numbers of the search engines, or the search weights can be set by technicians based on the rankings, download volumes, or daily active user numbers of the search engines.

[0083] The real-time attention degree of the sensitive word corresponds to its popularity, and the two are equal or in a proportional relationship. The trend value of the sensitive word can be calculated based on the trend of the search volume of the sensitive word within a preset time period, or an algorithm model can be used to perform weighted processing on the collected data to obtain the trend value.

[0084] Generally speaking, the greater the popularity and / or search volume of sensitive words, the more attention the public pays at present, the higher its importance, and the more worthy of key monitoring. From a certain perspective, for elevator fires, if the popularity and / or search volume of a certain sensitive word is large, it means that it is more likely to be a factor leading to fire hazards or fires in elevators. For example, when the search volume or popularity of the sensitive words "electric vehicle" or "battery" increases, in many cases, it is because elevator users bring electric vehicles into the elevator, causing elevator fires, or bring batteries home for charging, resulting in fires. At this time, "electric vehicle" or "battery" is an important factor leading to fire hazards or fires in elevators, and its sensitivity can be increased for key monitoring.

[0085] Here, it should be noted that the sensitivity of sensitive words is directly proportional to the degree of monitoring. When the sensitivity increases, the degree of monitoring of the corresponding target object factors should be increased accordingly. The degree of monitoring includes, but is not limited to, the monitoring frequency of target object factors and the technical personnel and computing memory allocated during the monitoring process.

[0086] Therefore, to obtain the real-time attention and trend value of each sensitive word, and adjust the sensitivity of each sensitive word based on the real-time attention and the trend value, big data and Internet technologies can be used to conveniently and reliably adjust the sensitivity of sensitive words according to current hot topics and the public's concerns, so as to better calculate the fire occurrence probability of the elevator fire prevention area based on the multi-dimensional characteristic parameters and the sensitivity.

[0087] Sensitivity can be understood as the weight ratio coefficient of a sensitive word affecting the probability of fire hazards or the probability of fire occurrence. With a higher sensitivity, a small change in the parameters of the target object factor corresponding to the sensitive word (such as the quantity and volume of sundries in the entrance corridor, the number of cigarette butts, the motor current power, or the degree of wear of the line, etc.) will lead to a relatively large change in the probability of fire hazards. On the contrary, with a lower sensitivity, a large change in the parameters of the target object factor corresponding to the sensitive word will result in a relatively small change in the probability of fire hazards.

[0088] Adjusting the sensitivity of each sensitive word based on the real-time attention and the trend value can be understood as adjusting the sensitivity of the sensitive word according to the size of the real-time attention and the trend value.

[0089] S5. Obtain the multi-dimensional characteristic parameters of the elevator fire prevention area, and calculate the fire occurrence probability of the elevator fire prevention area according to the multi-dimensional characteristic parameters and the sensitivity.

[0090] Specifically, the fire occurrence probability of the elevator fire prevention area can be jointly calculated according to the multi-dimensional characteristic parameters and the sensitivity of the corresponding sensitive words. The weights of the characteristic parameters and / or the fire warning probability threshold can be automatically adjusted according to the sensitivity of the sensitive words.

[0091] S6. Implement intelligent monitoring and early warning for elevator fire prevention according to the fire occurrence probability. When the fire occurrence probability is greater than the fire early warning probability threshold, generate an elevator fire early warning signal and send it to the target user group to remind technicians to perform timely maintenance on the elevator and check for fire hazards.

[0092] In summary, the elevator fire prevention intelligent monitoring and early warning method takes into account the multi-dimensional characteristic parameters of the elevator fire prevention area, combines the multi-dimensional characteristic parameters and sensitivity to calculate the fire occurrence probability of the elevator fire prevention area, can more accurately predict the probability of elevator fire, and thus better realize the monitoring and early warning of elevator fire and fire prevention safety; in addition, by obtaining multiple different elevator fire prevention areas, constructing an elevator fire prevention sensitive word library and dynamically updating the elevator fire prevention sensitive word library based on the dynamic sensitive word library update mechanism, the real-time and accuracy of the elevator fire prevention sensitive word library are realized, and the accuracy of elevator fire monitoring and early warning is further improved.

[0093] As a preferred technical solution, as Figure 4 shown, in step S5, the specific method for calculating the fire occurrence probability of the elevator fire prevention area according to the multi-dimensional characteristic parameters and the sensitivity includes the following steps:

[0094] S51. Construct a mapping relationship between the characteristic parameters and the sensitive words; the mapping relationship can be one-to-many or many-to-many. Each dimension of characteristic parameters corresponds to multiple sensitive words.

[0095] S52. According to the mapping relationship, obtain the regional sensitivity of each dimension of characteristic parameters; by calculating the weighted average of the sensitivities of the sensitive words mapped by each dimension of characteristic parameters, obtain the regional sensitivity.

[0096] S53. Obtain the real-time characteristic value of each dimension of characteristic parameters, and calculate the fire occurrence probability of the elevator fire prevention area according to the real-time characteristic value and the regional sensitivity.

[0097] Here, a fire early warning probability threshold can be set, and the size of the fire early warning probability threshold can be adjusted accordingly according to the regional sensitivity. When the regional sensitivity increases, the fire early warning probability threshold is reduced; when the regional sensitivity decreases, the fire early warning probability threshold is increased accordingly.

[0098] At the same time, a corresponding fire early warning standard value and a fire probability calculation weight are set for each dimension of characteristic parameters. The fire probability calculation weight is adjusted based on the regional sensitivity. The greater the regional sensitivity, the greater the fire probability calculation weight, and the fire occurrence probability of the elevator fire prevention area is calculated according to the weighted average of the ratio values between the real-time characteristic values of multiple different dimensions of characteristic parameters and the fire early warning standard values.

[0099] In this way, by comprehensively calculating the fire occurrence probability of the elevator fire prevention area in combination with multi-dimensional characteristic parameters and sensitivity, the sensitivity of the fire occurrence probability calculation can be improved, and the probability of elevator fire can be predicted more accurately, so as to better realize the monitoring and early warning of elevator fire and fire prevention safety.

[0100] The present invention also provides an elevator fire prevention intelligent monitoring and early warning system for implementing the elevator fire prevention intelligent monitoring and early warning system, as Figure 5 shown, which includes: a fire prevention area acquisition module, a sensitive word library construction module, a dynamic update module, a sensitivity acquisition module, a fire occurrence probability calculation module, and a monitoring and early warning module.

[0101] The fire prevention area acquisition module is used to acquire a plurality of different elevator fire prevention areas for the target elevator; the sensitive word library construction module is used to construct a plurality of different elevator fire prevention sensitive word libraries for the elevator fire prevention areas, and the plurality of different elevator fire prevention sensitive word libraries correspond to the plurality of different elevator fire prevention areas one by one.

[0102] For the target elevator, a plurality of elevator fire prevention areas including but not limited to the elevator car, hoistway, and entrance corridor are acquired. For elevator fires, they often occur in areas such as the elevator car, hoistway, and entrance corridor, especially the elevator car and the elevator hoistway. Sometimes, there are cartons, debris, and other sundries piled up in the elevator entrance corridor, and there are trash cans placed. Due to residents discarding unextinguished cigarette butts or placing flammable items, there are certain fire hazards and a certain probability of causing a fire.

[0103] One of the functions of acquiring a plurality of different elevator fire prevention areas for the target elevator is that targeted monitoring and early warning can be carried out for different elevator fire prevention areas, thereby improving the accuracy of elevator fire hazard monitoring and early warning.

[0104] The dynamic update module is used to construct a dynamic sensitive word library update mechanism and dynamically update the elevator fire prevention sensitive word library based on the dynamic sensitive word library update mechanism; the sensitivity acquisition module is used to obtain the real-time attention degree and the trend value within a preset time period for each sensitive word in each elevator fire prevention sensitive word library, and adjust the sensitivity of each sensitive word based on the real-time attention degree and the trend value; the fire occurrence probability calculation module is used to obtain the multi-dimensional characteristic parameters of the elevator fire prevention area and calculate the fire occurrence probability of the elevator fire prevention area according to the multi-dimensional characteristic parameters and the sensitivity; the monitoring and early warning module is used to realize the intelligent monitoring and early warning of elevator fire prevention according to the fire occurrence probability.

[0105] Regarding the sensitive words, it can be understood that the target object factors that pose a fire hazard and may lead to elevator fire accidents include, but are not limited to, cigarette butts, flammable items, elevator motor current power, ventilation vents, and circuits.

[0106] Preferably, the dynamic update module includes a fire frequency acquisition unit, an update frequency acquisition unit, and a dynamic update unit.

[0107] The fire frequency acquisition unit is used to acquire the age of the target elevator, the first elevator fire frequency in the past historical time period, the second elevator fire frequency in several different radius range areas where the target elevator is located, and the third elevator fire probability at the current moment of the target elevator.

[0108] The update frequency acquisition unit is used to obtain the sensitive word library update frequency according to the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability; the dynamic update unit is used to construct a dynamic sensitive word library update mechanism based on the sensitive word library update frequency.

[0109] The sensitivity acquisition module includes: a search engine selection unit, which is used to select multiple search engines, and based on the selected multiple search engines, obtain the popularity and search volume of each sensitive word; a search weight acquisition unit, which is used to obtain the search weights of the multiple search engines, and according to the search weights, the popularity, and the search volume, obtain the real-time attention and trend value of each sensitive word.

[0110] The fire occurrence probability calculation module includes a mapping relationship construction unit, a regional sensitivity acquisition unit, and a fire occurrence probability calculation unit.

[0111] The mapping relationship construction unit is used to construct a mapping relationship between feature parameters and sensitive words; the regional sensitivity acquisition unit is used to obtain the regional sensitivity of each dimension feature parameter according to the mapping relationship; the fire occurrence probability calculation unit is used to obtain the real-time feature value of each dimension feature parameter, and calculate the fire occurrence probability of the elevator fire prevention area according to the real-time feature value and the regional sensitivity.

[0112] Obtaining the sensitive word library update frequency according to the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability can be understood as using the weighted average of the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability as the sensitive word library update frequency, constructing a dynamic sensitive word library update mechanism based on the sensitive word library update frequency, and dynamically updating the sensitive word library according to the sensitive word library update frequency.

[0113] Here, the update frequency of the sensitive word library is obtained through the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability. By taking into account multiple factors that may lead to fire hazards or fires in the elevator, a more realistic update frequency of the sensitive word library can be obtained, and a more practical dynamic sensitive word library update mechanism can be constructed, so as to dynamically and real-time update the elevator fire prevention sensitive word library.

[0114] The elevator fire prevention intelligent monitoring and early warning system includes a smoke sensor and a temperature sensor installed in the elevator fire prevention area. The multi-dimensional characteristic parameters include a smoke signal and a temperature signal. The elevator fire prevention area includes an elevator shaft, an elevator car, and an elevator entrance corridor. Of course, the multi-dimensional characteristic parameters may also include an elevator acceleration signal and a lateral displacement signal, and the elevator acceleration signal and the lateral displacement signal are respectively collected by an acceleration sensor and an inclinometer installed on the side wall of the outer box of the elevator car.

[0115] The sensitivity of a sensitive word is proportional to the degree of its monitoring. When the sensitivity increases, the monitoring degree of the corresponding target object factor should be increased accordingly. The monitoring degree includes, but is not limited to, the monitoring frequency of the target object factor and the technical personnel and computing memory allocated during the monitoring process.

[0116] Therefore, by obtaining the real-time attention and trend value of each sensitive word and adjusting the sensitivity of each sensitive word based on the real-time attention and the trend value, it is possible to conveniently and reliably adjust the sensitivity of the sensitive word according to current current affairs hotspots and the concerns of the public by using big data and Internet technologies, so as to better calculate the fire occurrence probability in the elevator fire prevention area based on the multi-dimensional characteristic parameters and the sensitivity.

[0117] The sensitivity can be understood as the weight ratio coefficient of the sensitive word affecting the fire hazard probability or the fire occurrence probability. With a higher sensitivity, a small change in the parameters of the target object factor corresponding to the sensitive word (such as the quantity and volume of sundries in the entrance corridor, the number of cigarette butts, the motor current power, or the degree of wear of the line, etc.) will lead to a relatively large change in the fire hazard probability. On the contrary, with a lower sensitivity, a large change in the parameters of the target object factor corresponding to the sensitive word will result in a relatively small change in the fire hazard probability.

[0118] Adjusting the sensitivity of each sensitive word based on the real-time attention and the trend value can be understood as correspondingly adjusting the sensitivity of the sensitive word according to the magnitudes of the real-time attention and the trend value.

[0119] In summary, by considering the multi-dimensional characteristic parameters of the elevator fire prevention area, the elevator fire prevention intelligent monitoring and early warning system combines the multi-dimensional characteristic parameters and sensitivity to calculate the fire occurrence probability of the elevator fire prevention area, which can more accurately predict the probability of elevator fire, so as to better realize the monitoring and early warning of elevator fire and fire prevention safety; in addition, by obtaining multiple different elevator fire prevention areas, constructing an elevator fire prevention sensitive word library and dynamically updating the elevator fire prevention sensitive word library based on the dynamic sensitive word library update mechanism, the real-time and accuracy of the elevator fire prevention sensitive word library are realized, and the accuracy of elevator fire monitoring and early warning is further improved.

[0120] The present invention also provides an elevator fire prevention intelligent monitoring and early warning device, which includes: a memory storing executable instructions; a controller for executing the executable instructions and implementing the elevator fire prevention intelligent monitoring and early warning method.

[0121] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0122] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. An elevator fire prevention intelligent monitoring and early warning method, characterized in that , the elevator fire intelligent monitoring and early warning method comprises the following steps: For the target elevator, obtain multiple different elevator fire protection zones; For the elevator fire protection area, a plurality of different elevator fire protection sensitive word libraries are constructed, and the plurality of different elevator fire protection sensitive word libraries correspond one to one to the plurality of different elevator fire protection areas; Constructing a dynamic sensitive word library update mechanism, and dynamically updating the elevator fire protection sensitive word library based on the dynamic sensitive word library update mechanism; For each of the elevator fire protection sensitive word libraries, obtain the real-time attention degree and the trend value within a preset time period of each sensitive word, and adjust the sensitivity of each sensitive word based on the real-time attention degree and the trend value; Acquire multi-dimensional characteristic parameters of the elevator fire protection zone, and calculate the probability of fire occurrence in the elevator fire protection zone according to the multi-dimensional characteristic parameters and the sensitivity; According to the fire occurrence probability, intelligent monitoring and early warning of elevator fire prevention is achieved.

2. The elevator fire intelligent monitoring and early warning method according to claim 1 is characterized in that ,The specific method of building a dynamic sensitive lexicon update mechanism includes the following steps: Obtaining the age of the target elevator and the first elevator fire frequency in a past historical time period; Obtaining second elevator fire frequencies in a plurality of areas with different radius ranges where the target elevator is located; Obtaining the third elevator fire probability of the target elevator at the current moment; Acquire a sensitive word library update frequency according to the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability; A dynamic sensitive word library update mechanism is constructed based on the sensitive word library update frequency.

3. An elevator fire prevention intelligent monitoring and early warning method as claimed in claim 2, characterized in that ,The specific method of obtaining the real-time attention of each sensitive word and the ,trend value in a preset time period includes the following steps: Selecting multiple search engines, and obtaining the popularity and search volume of each sensitive word based on the selected multiple search engines; The search weights of the multiple search engines are obtained, and the real-time attention and trend value of each sensitive word are obtained according to the search weights, the popularity and the search volume.

4. The intelligent monitoring and early warning method for elevator fire prevention as claimed in claim 3 is characterized in that The specific method for calculating the probability of fire occurrence in the elevator fire protection area according to the multi-dimensional characteristic parameters and the sensitivity includes the following steps: Construct a mapping relationship between feature parameters and sensitive words; According to the mapping relationship, the regional sensitivity of each dimensional feature parameter is obtained; The real-time characteristic value of each dimensional characteristic parameter is obtained, and the probability of fire occurrence in the elevator fire protection area is calculated according to the real-time characteristic value and the regional sensitivity.

5. An elevator fire intelligent monitoring and early warning system, used to implement the elevator fire intelligent monitoring and early warning system as described in any one of claims 1 to 4, characterized in that: The elevator fire prevention intelligent monitoring and early warning system includes: A fire protection zone acquisition module is used to acquire multiple different elevator fire protection zones for a target elevator; A sensitive word library construction module is used to construct a plurality of different elevator fire protection sensitive word libraries for the elevator fire protection zone, wherein the plurality of different elevator fire protection sensitive word libraries correspond one to one to the plurality of different elevator fire protection zones; A dynamic update module, used to construct a dynamic sensitive word library update mechanism, and dynamically update the elevator fire protection sensitive word library based on the dynamic sensitive word library update mechanism; A sensitivity acquisition module, for acquiring the real-time attention degree and the trend value within a preset time period of each sensitive word in each elevator fire protection sensitive word library, and adjusting the sensitivity of each sensitive word based on the real-time attention degree and the trend value; A fire occurrence probability calculation module, used to obtain the multi-dimensional characteristic parameters of the elevator fire protection area, and calculate the fire occurrence probability of the elevator fire protection area according to the multi-dimensional characteristic parameters and the sensitivity; The monitoring and early warning module is used to realize intelligent monitoring and early warning of elevator fire prevention according to the probability of fire occurrence.

6. An elevator fire prevention intelligent monitoring and early warning system as claimed in claim 5, characterized in that: The dynamic update module includes: A fire frequency acquisition unit, used to acquire the age of the target elevator and the first elevator fire frequency in the past historical time period, the second elevator fire frequency in several different radius ranges of the location of the target elevator, and the third elevator fire probability of the target elevator at the current moment; An update frequency acquisition unit, configured to acquire an update frequency of a sensitive word library according to the first elevator fire frequency, the second elevator fire frequency, and the third elevator fire probability; The dynamic updating unit is used to construct a dynamic sensitive word library updating mechanism based on the sensitive word library updating frequency.

7. An elevator fire prevention intelligent monitoring and early warning system as claimed in claim 6, characterized in that: The sensitivity acquisition module comprises: A search engine selection unit, used to select multiple search engines, and obtain the popularity and search volume of each sensitive word based on the selected multiple search engines; The search weight acquisition unit is used to obtain the search weights of the multiple search engines, and obtain the real-time attention and trend value of each sensitive word according to the search weights, the popularity and the search volume.

8. An elevator fire prevention intelligent monitoring and early warning system as claimed in claim 7, characterized in that: The fire occurrence probability calculation module includes: A mapping relationship building unit, used to build a mapping relationship between feature parameters and sensitive words; A regional sensitivity acquisition unit, used to acquire the regional sensitivity of each dimensional feature parameter according to the mapping relationship; The fire occurrence probability calculation unit is used to obtain the real-time characteristic value of each dimensional characteristic parameter, and calculate the fire occurrence probability of the elevator fire protection area according to the real-time characteristic value and the area sensitivity.

9. An elevator fire prevention intelligent monitoring and early warning system as claimed in claim 8, characterized in that: The elevator fire intelligent monitoring and early warning system includes a smoke sensor and a temperature sensor installed in the elevator fire protection area, the multi-dimensional characteristic parameters include smoke signals and temperature signals, and the elevator fire protection area includes an elevator shaft, an elevator car and an elevator entrance corridor.

10. An elevator fire prevention intelligent monitoring and early warning device, characterized in that: The elevator fire prevention intelligent monitoring and early warning equipment includes: A memory storing executable instructions; A controller is used to execute the executable instructions and implement the elevator fire intelligent monitoring and early warning method as described in any one of claims 1 to 4.

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