Fire detection method and device, program product and electronic equipment

Abstract

The invention provides a fire detection method and device, a program product and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: determining carbon monoxide concentration data, smoke concentration data, temperature change rate data and humidity change rate data corresponding to a target space in a first time period; determining a membership function included in a preset fuzzy inference system for performing fire behavior detection on the target space; processing the carbon monoxide concentration data, the smoke concentration data, the temperature change rate data and the humidity change rate data according to the membership function to obtain prediction result information; and determining fire behavior detection information of the target space in the first time period according to the prediction result information and a preset fuzzy rule included in a preset fuzzy reasoning system. According to the invention, comprehensive analysis is carried out on various data through the membership function created for the target space, so that the accuracy of fire detection is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and in particular, to a fire detection method, apparatus, program product, and electronic device. Background Art

[0002] Currently, in the related art, whether there is a fire is determined only based on simple temperature changes. Although such a method can achieve fire detection, the detection method of determining whether there is a fire only based on temperature changes may result in false detections.

[0003] Therefore, how to accurately detect a fire has become an urgent problem to be solved. Summary of the Invention

[0004] The present disclosure provides a fire detection method, a fire detection apparatus, a computer program product, and an electronic device to at least improve the accuracy of fire detection to a certain extent.

[0005] According to a first aspect of the present disclosure, a fire detection method is provided. The method includes:

[0006] Determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within a first time period;

[0007] Determine the membership functions included in a preset fuzzy inference system for fire detection of the target space;

[0008] Process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership functions to obtain prediction result information;

[0009] Determine the fire detection information of the target space within the first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0010] In a possible implementation manner, determining the membership functions included in a preset fuzzy inference system for fire detection of the target space includes:

[0011] Determine multiple groups of test data based on different combustion materials in the target space; each group of test data in the multiple groups of test data includes carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data;

[0012] Perform visual analysis processing on the multiple groups of test data to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0013] In a possible implementation, performing visual analysis processing on the multiple sets of test data to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively, includes:

[0014] Performing visual analysis processing on the multiple sets of test data to determine the reference parameters;

[0015] According to multiple change ranges of the reference parameters and the multiple sets of test data, determining the membership interval ranges of the variable parameters and the reference parameters;

[0016] According to the membership interval ranges of the variable parameters and the reference parameters, determining the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0017] In a possible implementation, performing visual analysis processing on the multiple sets of test data to determine the reference parameters, includes:

[0018] Performing visual analysis processing on the multiple sets of test data, and taking the carbon monoxide concentration as the reference parameter.

[0019] In a possible implementation, according to multiple change ranges of the reference parameters and the multiple sets of test data, determining the membership interval ranges of the variable parameters and the reference parameters, includes:

[0020] According to the change information of each variable parameter when the value of the reference parameter changes, determining the membership interval range of each variable parameter;

[0021] According to the value change range of the reference parameter, determining the membership interval range of the reference parameter.

[0022] In a possible implementation, according to the membership interval ranges of the variable parameters and the reference parameters, determining the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively, includes:

[0023] According to the interval range of the variable parameter and the preset center rule, determining the center position information of the variable parameter, and according to the interval range and the center position information of the variable parameter, determining the membership function corresponding to the variable parameter; and,

[0024] According to the interval range of the reference parameter and the preset center rule, determining the center position information of the reference parameter, and according to the interval range and the center position information of the reference parameter, determining the membership function corresponding to the reference parameter.

[0025] In a possible implementation manner, determining the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period includes:

[0026] Determining the temperature data and humidity data corresponding to the target space within the first time period, and respectively processing the temperature data and humidity data to obtain the temperature change rate data and humidity change rate data; and,

[0027] Determining the carbon monoxide concentration data and smoke concentration data corresponding to the target space within the first time period.

[0028] According to a second aspect of the present disclosure, there is provided a fire detection device, the device includes:

[0029] A first determination unit, configured to determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period;

[0030] A second determination unit, configured to determine the membership functions included in a preset fuzzy inference system for performing fire detection on the target space;

[0031] A processing unit, configured to process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership functions to obtain prediction result information;

[0032] A detection unit, configured to determine the fire detection information of the target space within the first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0033] In a possible implementation manner, the second determination unit is specifically configured to:

[0034] Determine multiple groups of test data generated based on different combustion materials in the target space; each group of test data in the multiple groups of test data includes carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data;

[0035] Perform visual analysis processing on the multiple groups of test data to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0036] In a possible implementation manner, the second determination unit is specifically configured to:

[0037] Perform visual analysis processing on the multiple groups of test data to determine the reference parameters;

[0038] Determine the membership interval ranges of the variable parameters and the reference parameters according to multiple change ranges of the reference parameters and the multiple sets of test data;

[0039] Determine the membership functions corresponding to the carbon monoxide concentration, the smoke concentration, the temperature change rate, and the humidity change rate respectively according to the membership interval ranges of the variable parameters and the reference parameters.

[0040] In a possible implementation manner, the second determination unit is specifically configured to:

[0041] Perform visual analysis processing on the multiple sets of test data, and use the carbon monoxide concentration as the reference parameter.

[0042] In a possible implementation manner, the second determination unit is specifically configured to:

[0043] Determine the membership interval ranges of the respective variable parameters according to the change information of each variable parameter when the value of the reference parameter changes;

[0044] Determine the membership interval range of the reference parameter according to the numerical change range of the reference parameter.

[0045] In a possible implementation manner, the second determination unit is specifically configured to:

[0046] Determine the central position information of the variable parameter according to the interval range of the variable parameter and the preset central rule, and determine the membership function corresponding to the variable parameter according to the interval range and the central position information of the variable parameter; and,

[0047] Determine the central position information of the reference parameter according to the interval range of the reference parameter and the preset central rule, and determine the membership function corresponding to the reference parameter according to the interval range and the central position information of the reference parameter.

[0048] In a possible implementation manner, the first determination unit is specifically configured to:

[0049] Determine the temperature data and the humidity data corresponding to the target space in the first time period, and process the temperature data and the humidity data respectively to obtain the temperature change rate data and the humidity change rate data; and,

[0050] Determine the carbon monoxide concentration data and the smoke concentration data corresponding to the target space in the first time period.

[0051] According to the third aspect of the present disclosure, there is provided a computer program product, including a computer program, where when the computer program is executed by a processor, the method according to the first aspect and its possible implementation manners are implemented.

[0052] According to a fourth aspect of the present disclosure, there is provided an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the method of the first aspect and its possible implementation manners by executing the executable instructions.

[0053] The technical solution of the present disclosure has the following beneficial effects:

[0054] In the embodiment of the present disclosure, it is possible to determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period, and then determine the membership functions included in the preset fuzzy inference system for fire detection of the target space, so that the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data can be processed according to the membership functions to obtain prediction result information; and, according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system, the fire detection information of the target space within the first time period can be determined.

[0055] It can be seen that the entire detection process of fire detection in the embodiment of the present disclosure is automated and completely requires no human participation, which improves the fire detection efficiency to a certain extent. And, since it is based on the membership functions included in the preset fuzzy inference system for fire detection of the target space, that is, according to the targeted membership functions, the temperature change rate data, humidity change rate data, carbon monoxide concentration data, and smoke concentration data are processed, so it is possible to more accurately determine whether there is a fire in the target space within the first time period, and avoid the false detection situation of detecting a fire due to abnormal temperature changes or abnormal humidity changes that are not caused by a fire, thus greatly improving the fire detection accuracy.

[0056] Other features and advantages of the present disclosure will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present disclosure. The objectives and other advantages of the present disclosure can be realized and obtained by the structures specifically pointed out in the written specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following will briefly introduce the drawings required to be used in the embodiments of the present disclosure. Obviously, the following introduced drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0058] Figure 1 Shows a schematic diagram of an application scenario in this exemplary embodiment;

[0059] Figure 2 Flowchart showing a fire detection method in this exemplary embodiment;

[0060] Figure 3 Schematic diagram showing a first analysis result in this exemplary embodiment;

[0061] Figure 4 Schematic diagram showing a second analysis result in this exemplary embodiment;

[0062] Figure 5 Schematic diagram showing a third analysis result in this exemplary embodiment;

[0063] Figure 6 Schematic diagram showing a fourth analysis result in this exemplary embodiment;

[0064] Figure 7 Schematic diagram showing a method for determining the membership degree interval range in this exemplary embodiment;

[0065] Figure 8 Schematic diagram of a page for creating a fuzzy control system in this exemplary embodiment;

[0066] Figure 9 Schematic diagram of a page for inputting a membership function in this exemplary embodiment;

[0067] Figure 10 Schematic diagram of a page for editing preset fuzzy rules in this exemplary embodiment;

[0068] Figure 11 Schematic diagram of a page for an output surface viewer in this exemplary embodiment;

[0069] Figure 12 Schematic diagram of the structure of a fire detection device in this exemplary embodiment;

[0070] Figure 13 Schematic diagram of the structure of an electronic device in this exemplary embodiment. Detailed implementation mode

[0071] To make the objectives, technical solutions, and advantages of the present disclosure more clearly understood, the following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure. Without conflict, the embodiments in the present disclosure and the features in the embodiments may be arbitrarily combined with each other. And although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a sequence different from that here.

[0072] The terms "including" and any variations thereof in the specification and claims of the present disclosure are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0073] One or more in the embodiments of the present disclosure, "more than one" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item)" or its similar expression below refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c may be single or multiple.

[0074] It should be noted that the terms "first", "second", "third", etc. in the specification, claims, and the above-mentioned drawings of the present disclosure are used to distinguish similar objects and do not necessarily describe a specific order, sequence, size, and priority. For example, the first difference and the second difference in the embodiments of the present disclosure are only used to distinguish different differences. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0075] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. The accompanying drawings are schematic diagrams of the present disclosure and are not necessarily drawn to scale. Some of the block diagrams shown in the accompanying drawings may be functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, or in hardware modules or integrated circuits, or in networks, processors, or microcontrollers. The embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein. The features, structures, or characteristics described in the present disclosure can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present disclosure. However, those skilled in the art should be aware that one or more specific details may be omitted when implementing the technical solutions of the present disclosure, or other methods, components, devices, steps, etc. may be used to replace one or more specific details.

[0076] It should be noted that in the embodiments of the present disclosure, some industry-standard solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solutions of the present disclosure, but it does not mean that the applicant has already or necessarily used this solution. In the technical solutions of the present disclosure, the collection, dissemination, use, etc. of temperature data, humidity data, smoke concentration data, and carbon monoxide (CO) data all comply with relevant national laws and regulations.

[0077] In related technologies, when detecting a fire, generally, a simple temperature change is used to determine whether there is a fire. Although this method can achieve fire detection, the detection method that determines whether there is a fire only based on temperature change may result in false detections, leading to a low accuracy rate of fire detection.

[0078] In view of this, the exemplary embodiments of the present disclosure provide a fire detection method. Through this method, the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space in the first time period can be determined. Then, the membership functions included in the preset fuzzy inference system for fire detection of the target space can be determined. Thus, the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data can be processed according to the membership functions to obtain prediction result information; and, according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system, the fire detection information of the target space in the first time period can be determined.

[0079] It can be seen that the entire detection process of fire detection in the embodiments of the present disclosure is automated and completely does not require manual participation, which improves the fire detection efficiency to a certain extent. Moreover, since the preset fuzzy inference system for fire detection in the target space is based on the membership function, that is, according to the targeted membership function, the temperature change rate data, humidity change rate data, carbon monoxide concentration data, and smoke concentration data are processed, it is possible to more accurately determine whether a fire occurs in the target space within the first time period, avoiding the false detection situation of detecting a fire due to abnormal temperature changes or abnormal humidity changes that are not caused by a fire, thereby greatly improving the fire detection accuracy.

[0080] To better understand the technical solutions provided by the embodiments of the present disclosure, the following briefly introduces the application scenarios applicable to the technical solutions provided by the embodiments of the present disclosure. It should be noted that the following introduced application scenarios are only used to illustrate the embodiments of the present disclosure rather than to limit them. In specific implementation, the technical solutions provided by the embodiments of the present disclosure can be flexibly applied according to actual needs.

[0081] In the embodiments of the present disclosure, the fire detection technology can be applied to various business scenarios that require fire detection, such as classrooms, stations, factory areas, laboratories, etc. For example, it can be applied to the business scenario of fire detection in a laboratory, and the embodiments of the present disclosure do not limit this.

[0082] Please refer to Figure 1 shown Figure 1 which is an application scenario applicable to the technical solutions of the embodiments of the present disclosure. In this scenario schematic diagram, it includes multiple acquisition devices 101 and an electronic device 102. Among them, the multiple acquisition devices are respectively acquisition device 101-1, acquisition device 101-2,..., acquisition device 101-n, where n is a positive integer. For example, the multiple acquisition devices 101 can respectively acquire temperature change rate data, humidity change rate data, carbon monoxide concentration data, and smoke concentration data. For example, acquisition device 101-1 among the multiple acquisition devices can acquire temperature data, acquisition device 101-2 can acquire humidity data, acquisition device 101-3 can acquire carbon monoxide concentration data, and acquisition device 101-4 can acquire smoke concentration data. Of course, it is also possible that one acquisition device among the multiple acquisition devices acquires two types of data, and the other acquisition devices separately acquire one type of data. For example, acquisition device 101-1 can acquire temperature data and humidity data, acquisition device 101-2 can acquire carbon monoxide concentration data, and acquisition device 101-3 can acquire smoke concentration data. The embodiments of the present disclosure do not limit this.

[0083] Among them, between the acquisition device 101 and the electronic device 102, and between each acquisition device 101, they can be directly or indirectly communicatively connected through one or more networks 103.

[0084] In an embodiment of the present disclosure, the acquisition device 101-1 sends the acquired temperature data, humidity data, carbon monoxide concentration data, and smoke concentration data to the electronic device 102. The electronic device 102 can determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period, and then can determine the membership functions included in the preset fuzzy inference system for fire detection of the target space. Thus, according to the membership functions, the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data can be processed to obtain prediction result information; and, according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system, the fire detection information of the target space within the first time period can be determined.

[0085] Among them, Figure 1 each acquisition device 101 can be a sensor and / or a detector, but is not limited thereto. In other words, various devices with the function of supporting the acquisition of temperature data, humidity data, carbon monoxide concentration data, and smoke concentration data can be used as the acquisition device 101.

[0086] And, Figure 1 the electronic device 102 in can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (Content Delivery Network, CDN), and big data and artificial intelligence platforms, but is not limited thereto.

[0087] Of course, the method provided by the embodiment of the present disclosure is not limited to the Figure 1 application scenario shown, and can also be used in other possible application scenarios, such as an application scenario where the fire detection method is only implemented by the electronic device. The embodiment of the present disclosure does not make any restrictions.

[0088] To further illustrate the technical solution provided by the embodiment of the present disclosure, the following will be described in detail in combination with the accompanying drawings and specific implementation manners. Although the embodiment of the present disclosure provides the method operation steps as shown in the following embodiments or drawings, based on routine or non-creative labor, more or fewer operation steps may be included in the method. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiment of the present disclosure. When the method is actually processed or the device is executed, it can be executed in the method order shown in the embodiment or drawing or executed in parallel.

[0089] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a fire detection method in an embodiment of the present disclosure. The process of the method can be executed by an electronic device, for example. The electronic device can be the electronic device 102 in Figure 1 . The specific implementation process of the method is as follows:

[0090] Step 201: Determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period.

[0091] In an embodiment of the present disclosure, the electronic device can determine the temperature data and humidity data corresponding to the target space within the first time period, and respectively process the temperature data and humidity data to obtain the temperature change rate data and humidity change rate data; and determine the carbon monoxide concentration data and smoke concentration data corresponding to the target space within the first time period.

[0092] In an embodiment of the present disclosure, the electronic device can receive the temperature data and humidity data of the target space within the first time period sent by the acquisition device. Among them, the acquisition device can be a single temperature and humidity collector or a temperature and humidity sensor. Of course, the acquisition device can also be a temperature collector and a humidity collector, and the acquisition device can also be a temperature sensor and a humidity sensor. The present disclosure does not limit this.

[0093] In an embodiment of the present disclosure, the target space can be understood as the space that needs to be detected for fire, such as a classroom, a factory building, a hospital building, a computer room, etc. The present disclosure does not limit this.

[0094] In an embodiment of the present disclosure, in order to avoid waste of computing resources, the fire in the target space can be detected according to a preset period. The preset period is, for example, 60 seconds, or 90 seconds, etc. The present disclosure does not limit this. Therefore, the electronic device can receive the temperature data and humidity data of the target space within the first time period sent by the acquisition device. Among them, the first time period can be determined corresponding to the foregoing preset period and the current time. That is to say, the electronic device can also detect the fire in the target space in the second time period, the third time period, etc.

[0095] In an embodiment of the present disclosure, the acquisition device can periodically send the temperature data and humidity data to the electronic device, or the electronic device can periodically receive the temperature data and humidity data sent by the acquisition device. The present disclosure does not limit this.

[0096] In an embodiment of the present disclosure, the electronic device can obtain the temperature change rate data and humidity change rate data by, but not limited to, the following steps:

[0097] Step A: Establish a target linked list and store the temperature data and humidity data of the target space based on the target linked list.

[0098] In the embodiments of the present disclosure, the electronic device may determine the temperature and humidity transfer information of the target space, and then determine the target length of the linked list according to the temperature and humidity transfer information, and establish a linked list with the target length as the target linked list.

[0099] In the embodiments of the present disclosure, the electronic device may determine the temperature and humidity transfer information of the target space according to at least one of the area information of the target space, the building type, and the material of the stored object. After determining the temperature and humidity transfer information of the target space, the target length of the linked list may be determined according to the temperature and humidity transfer information, and a linked list with the target length may be established as the target linked list.

[0100] For example, the electronic device may determine the temperature and humidity transfer information of the target space according to the area information of the target space; or, the electronic device may determine the temperature and humidity transfer information of the target space according to the building type information of the target space; or, the electronic device may determine the temperature and humidity transfer information of the target space according to the material of the object stored in the target space.

[0101] For example, the electronic device may determine the temperature and humidity transfer information of the target space according to the area information and the building type information of the target space; or, the electronic device may determine the temperature and humidity transfer information of the target space according to the area information and the material of the stored object; or, the electronic device may determine the temperature and humidity transfer information of the target space according to the building type information and the material of the stored object.

[0102] For example, the electronic device may determine the temperature and humidity transfer information of the target space according to the area information, the building type, and the material of the stored object in the target space.

[0103] It should be noted that in the embodiments of the present disclosure, if the temperature and humidity transfer information is an overall information, the obtained target linked list is a linked list; if the temperature and humidity transfer information includes two pieces of information, that is, temperature transfer information and humidity transfer information, the obtained target linked list includes two linked lists, namely the first linked list and the second linked list, and the embodiments of the present disclosure do not limit this.

[0104] To better understand the solution for determining the target linked list provided by the present disclosure, several specific examples are described below.

[0105] In a possible implementation, considering the differences in the area information of the target space and / or the building type, the situations of temperature transfer and humidity transfer will vary. Therefore, the temperature and humidity transfer information can be determined based on the area information of the target space and / or the building type, and then, based on the temperature and humidity transfer information, the length information of the linked list can be determined. Furthermore, based on the length information of the linked list, a target linked list can be established, so that the temperature change rate data and the humidity change rate data can be determined more accurately.

[0106] Optionally, if the temperature and humidity transfer information is an overall information, the target linked list obtained based on this is a single linked list.

[0107] For example, the electronic device can determine the area information of the target space, and then, according to the area information of the target space, determine the first temperature and humidity transfer information. Based on the first temperature and humidity transfer information and the pre-set corresponding relationship between the temperature and humidity transfer information and the level information, the first level information can be determined. Then, based on the first level information and the pre-set matching table, the target length of the linked list can be determined, so that a linked list with the target length can be established as the target linked list.

[0108] For example, the electronic device can determine the building type information of the target space. According to the building type information of the target space, determine the second temperature and humidity transfer information. Based on the second temperature and humidity transfer information and the pre-set corresponding relationship between the temperature and humidity transfer information and the level information, the second level information can be determined. Then, based on the second level information and the pre-set matching table, the target length of the linked list can be determined, so that a linked list with the target length can be established as the target linked list.

[0109] For example, the electronic device can determine the building type information and the area information of the target space. According to the area information of the target space, determine the first temperature and humidity transfer information. According to the building type information of the target space, determine the second temperature and humidity transfer information. Then, the comprehensive temperature and humidity transfer information can be determined according to the first temperature and humidity transfer information and the second temperature and humidity transfer information. Thus, based on the comprehensive temperature and humidity transfer information and the pre-set corresponding relationship between the temperature and humidity transfer information and the level information, the third level information can be determined. Then, based on the third level information and the pre-set matching table, the target length of the linked list can be determined, and a linked list with the target length can be established as the target linked list.

[0110] Among them, the aforementioned pre-set matching table includes multiple corresponding relationships, and each corresponding relationship includes level information and linked list length information. In this way, when the electronic device determines the level information, the length information of the linked list can be matched and determined from the pre-set matching table.

[0111] Optionally, if the temperature and humidity transfer information includes two pieces of information, namely temperature transfer information and humidity transfer information, the target linked list obtained based on this includes two linked lists, namely the first linked list and the second linked list.

[0112] That is to say, the electronic device determines the temperature transfer information and humidity transfer information corresponding to the target space according to the area information and / or building type information of the target space, determines the first length information according to the temperature transfer information, so as to establish a first linked list based on the first length information, and determines the second length information according to the humidity transfer information, so as to establish a second linked list based on the second length information.

[0113] For example, the electronic device can determine the first temperature transfer information and the first humidity transfer information based on the area information of the target space, and there is a pre-set first correspondence between the temperature transfer information and the level information and a second correspondence between the humidity transfer information and the level information. Thus, the first level information corresponding to the first temperature transfer information can be determined according to the first correspondence, and then the first length information matching the first level information can be filtered out from the pre-set matching table, and the linked list established based on the first length information is used as the first linked list. Also, the second level information corresponding to the first humidity transfer information can be determined according to the second correspondence, and then the second length information matching the second level information can be filtered out from the pre-set matching table, and the linked list established based on the second length information is used as the second linked list.

[0114] For example, the electronic device can determine the second temperature transfer information and the second humidity transfer information based on the building type information of the target space, and there is a pre-set first correspondence between the temperature transfer information and the level information and a second correspondence between the humidity transfer information and the level information. Thus, the first level information corresponding to the second temperature transfer information can be determined according to the first correspondence, and then the first length information matching the first level information can be filtered out from the pre-set matching table, and the linked list established based on the first length information is used as the first linked list. Also, the second level information corresponding to the second humidity transfer information can be determined according to the second correspondence, and then the second length information matching the second level information can be filtered out from the pre-set matching table, and the linked list established based on the second length information is used as the second linked list.

[0115] For example, an electronic device may determine first temperature transfer information and first humidity transfer information based on the area information of a target space, and determine second temperature transfer information and second humidity transfer information based on the building type information of the target space. The device may also determine a first weight corresponding to the first temperature transfer information, a second weight corresponding to the first humidity transfer information, a third weight corresponding to the second temperature transfer information, and a fourth weight corresponding to the second humidity transfer information. Thus, the comprehensive temperature transfer information corresponding to the target space can be determined based on the first temperature transfer information, the second temperature transfer information, the first weight, and the third weight. And the comprehensive humidity transfer information corresponding to the target space can be determined based on the first humidity transfer information, the second humidity transfer information, the second weight, and the fourth weight. Further, according to a first correspondence between the preset temperature transfer information and the level information, the third level information corresponding to the comprehensive temperature transfer information can be determined. Then, the first length information matching the third level information can be filtered out from a preset matching table, and the linked list established based on the first length information can be used as the first linked list. Also, according to a second correspondence between the preset humidity transfer information and the level information, the second level information corresponding to the comprehensive humidity transfer information can be determined. Then, the second length information matching the second level information can be filtered out from the preset matching table, and the linked list established based on the second length information can be used as the second linked list.

[0116] In a possible implementation, considering the differences in the objects stored in the target space, the temperature transfer and humidity transfer situations will vary. Therefore, the temperature and humidity transfer information can be determined based on the materials of the objects stored in the target space. Then, based on the temperature and humidity transfer information, the length information of the linked list can be determined. Further, based on the length information of the linked list, the target linked list can be established, so that the temperature change rate data and the humidity change rate data can be determined more accurately.

[0117] In a possible implementation, an electronic device may determine the humidity transfer information based on the area information of the target space, the building type, and the materials of the objects stored in it. Then, based on the temperature and humidity transfer information, the length information of the linked list can be determined. Further, based on the length information of the linked list, the target linked list can be established, so that the temperature change rate data and the humidity change rate data can be determined more accurately.

[0118] It can be seen that in the embodiments of the present disclosure, when determining the temperature change rate and the humidity change rate, not only the area information and the building type of the target space are considered, but also the characteristic information of the materials of the objects stored in the target space for transmitting temperature and humidity information is considered to comprehensively determine the length information of the linked list. Thus, the temperature change rate and the humidity change rate can be determined more precisely, and further the accuracy of fire detection can be improved.

[0119] Step B: Screen the temperature data and humidity data stored in the target linked list to obtain the processed temperature data and the processed humidity data.

[0120] In the embodiment of the present disclosure, the electronic device can inspect the temperature data and humidity data stored in the target linked list, screen out the non-standard data, and obtain the processed temperature data and the processed humidity data. Among them, the non-standard data is, for example, a negative value, or an unconventional temperature value (such as 2000 degrees) or humidity value, etc., which is not limited in the embodiment of the present disclosure.

[0121] Step C: Process the processed temperature data and the processed humidity data respectively to obtain the temperature change rate data and the humidity change rate data.

[0122] In the embodiment of the present disclosure, the electronic device can determine the first difference between the last temperature data and the first temperature data in the target linked list; obtain the temperature change rate data according to the first difference and the target length of the target linked list; and determine the second difference between the last humidity data and the first humidity data in the target linked list; obtain the humidity change rate data according to the second difference and the target length of the target linked list.

[0123] For example, if the last temperature data in the target linked list is 151 and the first temperature data is 156, the first difference 5 can be determined, and the target length of the target linked list is 60, then the temperature change rate data of 0.083 can be obtained. And if the last humidity data in the target linked list is 530 and the first humidity data is 536, the second difference of -6 can be determined, take its absolute value, and the target length of the target linked list is 60, then the humidity change rate data of 0.1 can be obtained.

[0124] In the embodiment of the present disclosure, when the target linked list includes a first linked list and a second linked list, the electronic device can determine the first difference between the last temperature data and the first temperature data in the first linked list; obtain the temperature change rate data according to the first difference and the target length of the first linked list; and determine the second difference between the last humidity data and the first humidity data in the second linked list; obtain the humidity change rate data according to the second difference and the target length of the second linked list.

[0125] For example, if the last temperature data in the first linked list is 143 and the first temperature data is 147, the first difference 4 can be determined, and the target length of the target linked list is 60, then the temperature change rate data of 0.067 can be obtained. And if the last humidity data in the target linked list is 430 and the first humidity data is 436, the second difference of -6 can be determined, take its absolute value, and the target length of the target linked list is 60, then the humidity change rate data of 0.1 can be obtained.

[0126] In an embodiment of the present disclosure, the electronic device may further receive carbon monoxide concentration data and smoke concentration data of a target space within a first time period sent by a collection device.

[0127] Optionally, the collection device for collecting carbon monoxide concentration data may be, for example, a carbon monoxide detector, an intelligent fixed carbon monoxide detector, etc., which is not limited in the embodiments of the present disclosure. For example, the electronic device may receive carbon monoxide concentration data of the target space within the first time period collected by a carbon monoxide detector, or may receive carbon monoxide concentration data of the target space within the first time period collected by an intelligent fixed carbon monoxide detector.

[0128] Optionally, the collection device for collecting smoke concentration data may be, for example, a smoke detector, a flue gas and dust particulate concentration tester, etc., which is not limited in the embodiments of the present disclosure. For example, the electronic device may receive smoke concentration data of the target space within the first time period collected by a smoke detector, where the smoke detector may be, for example, an ionization smoke detector, a photoelectric smoke detector, an infrared beam smoke detector, etc., which is not limited in the embodiments of the present disclosure.

[0129] Step 202: Determine the membership functions included in a preset fuzzy inference system for fire detection of the target space.

[0130] In an embodiment of the present disclosure, the electronic device may create different preset fuzzy inference systems, different membership functions, and preset fuzzy rules for different target spaces. Hereinafter, taking the target space as a computer room as an example, the solution for determining the membership function will be introduced.

[0131] In an embodiment of the present disclosure, each membership function and preset fuzzy rule included in the preset fuzzy inference system may be set correspondingly based on actual implementation. Among them, the membership function is a function used to describe the degree to which an element in a fuzzy set belongs to the set. It maps each element in the universe of discourse to a real number between 0 and 1, and this real number represents the membership degree of the element to the fuzzy set. For example, the membership function is a triangular membership function and can be determined correspondingly by the following formula (1):

[0132]

[0133] where a, b, and c are the values of the three points of the triangular membership function respectively.

[0134] In an embodiment of the present disclosure, the electronic device may determine the membership function by, but not limited to, the following steps:

[0135] Step a: Determine multiple groups of test data based on different combustion materials in the target space; each group of test data in the multiple groups of test data includes carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data.

[0136] In the embodiments of the present disclosure, a fire can be simulated by experimentally burning materials in a target space, so that the sensing data of temperature sensors, humidity sensors, CO sensors, and smoke sensors in the target space in the morning and evening of a day can be obtained. The sensing data is stored in a table, and the time points of smoking and fire ignition are marked with special background colors in the table. Thus, based on the data in the table, multiple groups of test data generated by different combustion materials in the target space can be determined. It should be noted that the multiple groups of test data generated by different combustion materials in the target space are data obtained before the first time period.

[0137] For example, assume that there are 3 devices in a computer room, namely device 1, device 2, and device 3. Combustible material 1 is set as an electric wire, and the position is directly below each device. Thus, the first group of test data generated after the electric wire located directly below each device burns can be obtained. In addition, combustible material 2 is set as a circuit board, and the position is on the side of each device. Thus, the second group of test data generated after the circuit board located on the side of each device burns can be obtained.

[0138] Among them, both the first group of test data and the second group of test data include the temperature data of device 1, the temperature data of device 2, the temperature data of device 3, the humidity data of device 1, the humidity data of device 2, the humidity data of device 3, the CO concentration data, and the smoke concentration data.

[0139] Step b: Perform visual analysis processing on multiple groups of test data to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0140] In the embodiments of the present disclosure, an electronic device can perform visual analysis processing on multiple groups of test data to determine reference parameters; then, according to multiple change ranges of the reference parameters and multiple groups of test data, determine the membership interval ranges of variable parameters and reference parameters; and, according to the membership interval ranges of the variable parameters and reference parameters, determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0141] For example, continuing with the example where there are 3 devices in the computer room mentioned above, namely device 1, device 2, and device 3, the electronic device can perform comprehensive visual comparison analysis on the first group of test data to obtain a first analysis result. The first analysis result is, for example, Figure 3 as shown, and, the electronic device can perform grouped independent visual analysis on the first group of test data to obtain a second analysis result. The second analysis result is, for example, Figure 4 as shown.

[0142] The electronic device can also perform comprehensive visual comparison and analysis on the second set of test data to obtain a third analysis result. For example, the third analysis result is Figure 5 as shown. In addition, the electronic device can perform grouped independent visual analysis on the second set of test data to obtain a fourth analysis result. For example, the fourth analysis result is Figure 6 as shown.

[0143] Referring to Figures 3 - 6 it can be seen that the change of smoke concentration is not obvious in the initial stage. The temperature and humidity change significantly within a day but there is no suitable fixed range, while the change of CO is relatively fixed. Therefore, CO can be selected as the benchmark.

[0144] Optionally, the electronic device can perform visual analysis processing on multiple sets of test data and use the carbon monoxide concentration as the benchmark parameter.

[0145] In the embodiments of the present disclosure, after determining the basic parameters, the membership interval range of each variable parameter can be determined according to the change information of each variable parameter when the value of the benchmark parameter changes; then, according to the value change range of the benchmark parameter, the membership interval range of the benchmark parameter can be determined.

[0146] For example, continuing with the previous example where there are 3 devices in the computer room, namely device 1, device 2, and device 3, referring to Figure 7 as shown, the change information of the temperature change rate, humidity change rate, and smoke concentration is respectively recorded based on the data collected by the CO sensor. When the CO concentration increases to 2, and when the CO concentration increases to 5.

[0147] Please continue to refer to Figure 7 , when the CO concentration increases to 2, it can be determined that the change information corresponding to the variable parameter being the temperature change rate includes 0.022, 0.020, 0.024, and 0.019. Thus, the membership interval range of the variable parameter being the temperature change rate can be determined to be from 0.019 to 0.024.

[0148] When the CO concentration increases to 2, it can be determined that the change information corresponding to the variable parameter being the humidity change rate includes 0.066, 0.059, 0.062, and 0.055. Thus, the membership interval range of the variable parameter being the humidity change rate can be determined to be from 0.052 to 0.068.

[0149] When the CO concentration increases to 2, it can be determined that the change information corresponding to the variable parameter being the smoke concentration includes 0, 0, 0, and 0. Thus, the membership interval range of the variable parameter being the smoke concentration can be determined to be less than 0.

[0150] Further, the electronic device can determine that the membership interval range when the reference parameter is the CO concentration is from 0 to 2.

[0151] Please continue to refer to Figure 7 , when the CO concentration increases to 5, it can be determined that the change information corresponding to the variable parameter being the temperature change rate includes 0.122, 0.131, and 0.126. Thus, it can be determined that the membership interval range when the variable parameter is the temperature change rate is greater than 0.1.

[0152] When the CO concentration increases to 5, it can be determined that the change information corresponding to the variable parameter being the humidity change rate includes 0.188, 0.190, and 0.184. Thus, it can be determined that the membership interval range when the variable parameter is the humidity change rate is greater than 0.17.

[0153] When the CO concentration increases to 5, it can be determined that the change information corresponding to the variable parameter being the smoke concentration includes 166, 152, and 0. Thus, it can be determined that the membership interval range when the variable parameter is the smoke concentration is from 0 to 159A.

[0154] Further, the electronic device can determine that the membership interval range when the reference parameter is the CO concentration is from 2 to 5.

[0155] In the embodiments of the present disclosure, according to the interval range of the variable parameter and the preset center rule, the center position information of the variable parameter is determined, and according to the interval range and the center position information of the variable parameter, the membership function corresponding to the variable parameter is determined; and according to the interval range of the reference parameter and the preset center rule, the center position information of the reference parameter is determined, and according to the interval range and the center position information of the reference parameter, the membership function corresponding to the reference parameter is determined.

[0156] Optionally, after determining the interval range of the variable parameter and the interval range of the reference parameter, the left and right endpoints of the interval range can be used as the left and right endpoints of the membership function respectively, and the position of the center of the membership function can be calculated using the triangle midline. Thus, the values of the three points of the triangular membership function can be obtained, that is, the values of a, b, and c in Formula 1 are determined, and further the membership function corresponding to the variable parameter can be determined. That is to say, if the membership function is a triangular membership function, the preset center rule can be the triangle midline rule. When the membership function is other membership functions, such as trapezoidal membership functions, Z-shaped membership functions, etc., the preset center rule can be correspondingly determined based on the characteristics of the membership function, and the embodiments of the present disclosure do not limit this.

[0157] In the embodiments of the present disclosure, considering that there are three states in fire prediction, namely, no risk, small risk, and large risk, three membership functions can be set for each variable parameter and reference parameter. That is to say, the carbon monoxide concentration corresponds to the membership function corresponding to the no-risk state, the membership function corresponding to the small-risk state, and the membership function corresponding to the large-risk state; the smoke concentration corresponds to the membership function corresponding to the no-risk state, the membership function corresponding to the small-risk state, and the membership function corresponding to the large-risk state; the temperature change rate corresponds to the membership function corresponding to the no-risk state, the membership function corresponding to the small-risk state, and the membership function corresponding to the large-risk state; and the humidity change rate corresponds to the membership function corresponding to the no-risk state, the membership function corresponding to the small-risk state, and the membership function corresponding to the large-risk state.

[0158] Step 203: Process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership function to obtain prediction result information.

[0159] In the embodiments of the present disclosure, the command "fuzzy" can be entered in a preset software to enter the fuzzy logic editing window and create a preset fuzzy control system. The preset software is, for example, MATLAB. For example, see Figure 8 as shown Figure 8 is a schematic diagram of the page for creating a fuzzy control system in the embodiments of the present disclosure. Then open the membership function editor and input the membership function. For example, see Figure 9 as shown Figure 9 is a schematic diagram of the page for inputting the membership function in the embodiments of the present disclosure. And define and edit the preset fuzzy rules. For example, see Figure 10 as shown Figure 10 is a schematic diagram of the page for editing the preset fuzzy rules in the embodiments of the present disclosure. For example, the preset fuzzy rule is: If the temperature change rate corresponds to no risk, and the humidity change rate corresponds to no risk, and the carbon monoxide concentration corresponds to no risk, and the smoke concentration corresponds to no risk, then it is determined that the prediction result information is no risk. Among them, the preset fuzzy rule describes the fuzzy relationship between the input variable and the output variable. In this way, the construction of the preset fuzzy inference system can be completed, and the preset fuzzy inference system can be deployed on an electronic device.

[0160] In the embodiments of the present disclosure, the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data can be processed according to the membership function to obtain prediction result information. For example, the electronic device can process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership function and output a surface viewer, so as to view the prediction result information. For example, see Figure 11As shown, the temperature change rate is 0.0341, the temperature difference change rate is 0.03, the CO concentration is 8.71, and the smoke concentration is 135, so that the predicted result information can be obtained as 0.629.

[0161] In the embodiments of the present disclosure, the electronic device may determine a first value, a second value, a third value, and a fourth value according to the temperature change rate data, the humidity change rate data, the carbon monoxide concentration data, the smoke concentration data, and their respective membership functions; then, according to the first value, the second value, the third value, the fourth value, and the graph of the output membership degree, the target graph may be obtained, and thus, according to the centroid of the target graph, the predicted result information may be determined.

[0162] In the specific implementation process, the electronic device may substitute the carbon monoxide concentration data, the smoke concentration data, the temperature change rate data, and the humidity change rate data into their respective membership functions to obtain the first value, the second value, the third value, and the fourth value. Since the preset fuzzy rule is in an "and" relationship, the smaller membership degree is taken to cut the graph of the output membership degree to obtain the output of this rule. By analogy, the graphs of the outputs of 12 rules are finally obtained, and finally the 12 graphs are stacked together to obtain a final graph, that is, the target graph. Finally, the centroid of the graph is calculated by means of defuzzification operation to obtain the final predicted result information.

[0163] Optionally, the formula for the defuzzification operation may be determined with reference to the following formula two:

[0164]

[0165] where μi represents the output value of the i-th rule, the output values of all rules form a fuzzy set μ, (μi) represents the membership degree of the element μi corresponding to the output membership function, and n is the number of elements in the fuzzy set.

[0166] Optionally, the electronic device may obtain the first value according to the temperature change rate data and the first membership function; obtain the second value according to the humidity change rate data and the second membership function; obtain the third value according to the carbon monoxide concentration data and the third membership function, and obtain the fourth value according to the smoke concentration data and the fourth membership function; where the first membership function, the second membership function, the third membership function, and the fourth membership function are all triangular membership functions.

[0167] It should be noted that in the embodiments of the present disclosure, the first membership function, the second membership function, the third membership function, and the fourth membership function may all include three membership functions, that is, the triangular membership function corresponding to the risk-free state, the triangular membership function corresponding to the small-risk state, and the triangular membership function corresponding to the large-risk state.

[0168] Step 204: Determine the fire detection information of the target space within the first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0169] For example, assume that the temperature change rate data is 0.05, and it is determined that 0.05 is within the triangular membership function region of small risk corresponding to the temperature change rate. Assume that the humidity change rate is 0.06, and it is determined that 0.06 is within the triangular membership function region of large risk corresponding to the humidity change rate. Assume that the carbon monoxide concentration is 25, and it is determined that 25 is within the triangular membership function region of large risk corresponding to the carbon monoxide concentration. Assume that the smoke concentration is 250, and it is determined that 250 is within the triangular membership function region of large risk corresponding to the smoke concentration. Then, input 0.05, 0.06, 25, and 250 into the preset fuzzy inference system to obtain a fuzzy output value of 0.695. Thus, according to the fuzzy output value of 0.695 and the prediction membership function, it can be determined that it is within the triangular membership function region of large risk corresponding to the fire detection information. Therefore, according to the preset fuzzy rules, the fire detection information of the target space within the first time period can be determined as large risk.

[0170] It can be seen that the entire detection process of fire detection in the embodiments of the present disclosure is automated and completely requires no human participation, which improves the fire detection efficiency to a certain extent. Moreover, since it is based on the membership function included in the preset fuzzy inference system for fire detection of the target space, that is, according to the targeted membership function, the temperature change rate data, humidity change rate data, carbon monoxide concentration data, and smoke concentration data are processed. Therefore, it can more accurately determine whether there is a fire in the target space within the first time period, avoiding the wrong detection situation where non-fire abnormal temperature changes or abnormal humidity changes are detected as fires, thereby greatly improving the fire detection accuracy.

[0171] An exemplary embodiment of the present disclosure also provides a fire detection device. Refer to Figure 12 As shown, the fire detection device 1200 includes the following program units:

[0172] The first determination unit 1201 is configured to determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period;

[0173] The second determination unit 1202 is configured to determine the membership function included in the preset fuzzy inference system for fire detection of the target space;

[0174] The processing unit 1203 is configured to process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership function to obtain prediction result information;

[0175] The detection unit 1204 is configured to determine the fire detection information of the target space within a first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0176] In a possible implementation manner, the second determination unit 1202 is specifically configured to:

[0177] Determine multiple groups of test data generated by different combustion materials within the target space; each group of test data in the multiple groups of test data includes carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data;

[0178] Perform visual analysis processing on the multiple groups of test data to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0179] In a possible implementation manner, the second determination unit 1202 is specifically configured to:

[0180] Perform visual analysis processing on the multiple groups of test data to determine the reference parameters;

[0181] According to multiple change ranges of the reference parameters and the multiple groups of test data, determine the membership interval ranges of the variable parameters and the reference parameters;

[0182] According to the membership interval ranges of the variable parameters and the reference parameters, determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate respectively.

[0183] In a possible implementation manner, the second determination unit 1202 is specifically configured to:

[0184] Perform visual analysis processing on the multiple groups of test data, and use the carbon monoxide concentration as the reference parameter.

[0185] In a possible implementation manner, the second determination unit is specifically configured to:

[0186] According to the change information of each variable parameter when the value of the reference parameter changes, determine the membership interval ranges of the respective variable parameters;

[0187] According to the numerical change range of the reference parameter, determine the membership interval range of the reference parameter.

[0188] In a possible implementation manner, the second determination unit 1202 is specifically configured to:

[0189] Determine the central position information of the variable parameter according to the interval range of the variable parameter and the preset central rule, and determine the membership function corresponding to the variable parameter according to the interval range and the central position information of the variable parameter; and,

[0190] Determine the central position information of the reference parameter according to the interval range of the reference parameter and the preset central rule, and determine the membership function corresponding to the reference parameter according to the interval range and the central position information of the reference parameter.

[0191] In a possible implementation manner, the first determination unit 1201 is specifically configured to:

[0192] Determine the temperature data and humidity data corresponding to the target space in the first time period, and process the temperature data and humidity data respectively to obtain temperature change rate data and humidity change rate data; and,

[0193] Determine the carbon monoxide concentration data and smoke concentration data corresponding to the target space in the first time period.

[0194] The specific details of each part in the above device have been described in detail in the implementation manner of the method part. The undisclosed detailed content can be referred to the implementation manner content of the method part, and thus will not be elaborated here.

[0195] It should be noted that although several modules or units of devices for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the exemplary embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0196] The exemplary embodiments of the present disclosure further provide a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the above-mentioned fire detection method is implemented.

[0197] In one implementation manner, the computer program product may be a tangible product including a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium may be a storage medium based on signals such as electricity, magnetism, light, electromagnetic, infrared, etc., including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory (Flash), mechanical hard disk (HDD), solid state drive (SSD), etc. Exemplarily, the computer program product may be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, NAND flash memory, etc.

[0198] In one embodiment, a computer program product may be an intangible product containing a computer program. Exemplarily, the computer program product may be implemented as a virtual digital product, such as an executable file storing the computer program, a digital file such as an installation package, etc.

[0199] The code of the computer program can be written in one or more programming languages. Examples of programming languages include C, Java, C++, etc. The program code can be executed entirely on the user's computing device, or partially on the user's computing device, or executed as an independent software package, or partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, such as a local area network (LAN), a wide area network (WAN), etc., or can be connected to an external computing device (e.g., through an Internet connection provided by an operator).

[0200] The computer program can be carried or transmitted by signals such as electricity, magnetism, light, electromagnetic, infrared, etc. The electronic device can convert the signal carrying the computer program into a digital signal and then run the computer program. When the computer program runs on the electronic device, its code is used to cause the electronic device to execute (more specifically, to cause the processor of the electronic device to execute) the method steps of various exemplary embodiments of the present disclosure. For example, the above-mentioned fire detection method can be executed, which includes the following steps: Step 201: Determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period; Step 202: Determine the membership functions included in the preset fuzzy inference system for fire detection of the target space; Step 203: Process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership functions to obtain prediction result information; Step 204: Determine the fire detection information of the target space within the first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0201] By implementing the above method steps through a computer program, the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period can be determined, and then the membership functions included in the preset fuzzy inference system for fire detection of the target space can be determined, so that the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data can be processed according to the membership functions to obtain prediction result information; and, the fire detection information of the target space within the first time period can be determined according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0202] It can be seen that the entire detection process of fire detection in the embodiments of the present disclosure is automated and completely requires no human participation, which improves the fire detection efficiency to a certain extent. Moreover, since the preset fuzzy inference system for fire detection in the target space is based on the membership functions included, that is, according to the targeted membership functions, the temperature change rate data, humidity change rate data, carbon monoxide concentration data, and smoke concentration data are processed, it is possible to more accurately determine whether a fire occurs in the target space within the first time period, avoiding the misdetection situation where a fire is detected due to abnormal temperature changes or abnormal humidity changes that are not caused by a fire, thus greatly improving the fire detection accuracy.

[0203] An exemplary embodiment of the present disclosure also provides an electronic device, which may include a processor and a memory. The memory stores executable instructions of the processor, such as a computer program. The processor executes the method steps of various exemplary embodiments of the present disclosure by executing the executable instructions.

[0204] Reference is made below Figure 13 , and the electronic device is exemplarily described in the form of a general computing device. It should be understood that Figure 13 the electronic device 1300 shown is only an example and should not impose limitations on the functions and usage scope of the embodiments of the present disclosure.

[0205] As Figure 13 shown, the electronic device 1300 may include: a processor 1310, a memory 1320, a bus 1330, an I / O (input / output) interface 1340, a network adapter 1350, and a display 1380.

[0206] The memory 1320 may include volatile memory, such as RAM 1321 and a cache unit 1322, and may also include non-volatile memory, such as ROM 1323. The memory 1320 may also include one or more program modules 1324, and such program modules 1324 include but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. For example, the program module 424 may include each unit in the above device.

[0207] The processor 1310 may include one or more processing units. For example, the processor 410 may include an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit), etc.

[0208] The processor 1310 can be used to execute the executable instructions stored in the memory 1320. For example, it can execute the above fire detection method, which includes the following steps: Step 201: Determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period; Step 202: Determine the membership functions included in the preset fuzzy inference system for fire detection of the target space; Step 203: Process the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data according to the membership functions to obtain prediction result information; Step 204: Determine the fire detection information of the target space within the first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0209] By executing the above method steps through the processor 1310, the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period can be determined. Then, the membership functions included in the preset fuzzy inference system for fire detection of the target space can be determined, so that the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data can be processed according to the membership functions to obtain prediction result information; and the fire detection information of the target space within the first time period can be determined according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

[0210] As can be seen, the entire fire detection process in the embodiments of the present disclosure is automated and completely requires no human participation, which improves the fire detection efficiency to a certain extent. Moreover, since the preset fuzzy inference system for fire detection in the target space is based on the membership functions included, that is, according to the targeted membership functions, the temperature change rate data, humidity change rate data, carbon monoxide concentration data, and smoke concentration data are processed, it is possible to more accurately determine whether a fire occurs in the target space within the first time period, avoiding the false detection situation where a fire is detected due to abnormal temperature changes or abnormal humidity changes that are not caused by a fire, thus greatly improving the fire detection accuracy.

[0211] The bus 1330 is used to implement the connection between different components of the electronic device 1300 and may include a data bus, an address bus, and a control bus.

[0212] The electronic device 1300 can communicate with one or more external devices 1400 (such as a keyboard, a mouse, an external controller, etc.) through the I / O interface 1340.

[0213] The electronic device 1300 can communicate with one or more networks through the network adapter 1350. For example, the network adapter 1350 can provide mobile communication solutions such as 3G / 4G / 5G, or provide wireless communication solutions such as wireless local area network, Bluetooth, and near field communication. The network adapter 1350 can communicate with other modules of the electronic device 1300 through the bus 1330.

[0214] The electronic device 1300 can display a graphical user interface, such as a fire warning message, through the display 1380.

[0215] Although Figure 13 not shown in the figure, other hardware and / or software modules can also be provided in the electronic device 1300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0216] As can be seen from the above, the technical solution of the present disclosure can be implemented as a method, a device, a system, a computer program product, a storage medium, an electronic device, etc. Those skilled in the art can understand that various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation manner, a complete software implementation manner (including firmware, microcode, etc.), or an implementation manner combining hardware and software aspects, such as can be respectively referred to as "circuit", "module", or "system".

[0217] It should be understood that the present disclosure is not limited to the specific method steps or structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. Based on the specific embodiments provided by the present disclosure, those skilled in the art will easily think of other embodiments. Therefore, the specific embodiments provided by the present disclosure are only exemplary, and the scope and spirit of the present disclosure are pointed out by the claims, and should cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not disclosed by the present disclosure.

Claims

1. A fire detection method, characterized in that: The method comprises: Determine carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within the first time period; Determining a membership function included in a preset fuzzy inference system for fire detection in the target space; According to the membership function, the carbon monoxide concentration data, the smoke concentration data, the temperature change rate data, and the humidity change rate data are processed to obtain prediction result information; The fire detection information of the target space within the first time period is determined according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

2. The method according to claim 1, characterized in that Determining the membership function of the preset fuzzy inference system for fire detection in the target space, including: Determine a plurality of groups of test data generated based on different combustion materials in the target space; each group of test data in the plurality of groups of test data includes carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data; Visual analysis is performed on the multiple groups of test data to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate.

3. The method according to claim 2, characterized in that Performing visual analysis on the multiple test data sets to determine the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate, respectively, including: Performing visual analysis on the multiple groups of test data to determine benchmark parameters; Determining the membership interval ranges of the variable parameters and the benchmark parameters according to the multiple variation ranges of the benchmark parameters and the multiple groups of test data; The membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate and humidity change rate are determined according to the membership interval ranges of the variable parameters and the reference parameters.

4. The method according to claim 3, characterized in that Performing visual analysis on the multiple groups of test data to determine benchmark parameters includes: Visual analysis is performed on the multiple groups of test data, and the carbon monoxide concentration is used as a reference parameter.

5. The method according to claim 4, characterized in that Determining the membership interval ranges of the variable parameters and the benchmark parameters according to the multiple variation ranges of the benchmark parameters and the multiple groups of test data, including: Determining the membership interval range of each variable parameter according to the change information of each variable parameter when the value of the reference parameter changes; The membership interval range of the benchmark parameter is determined according to the numerical variation range of the benchmark parameter.

6. The method according to claim 5, characterized in that Determining the membership functions corresponding to the carbon monoxide concentration, smoke concentration, temperature change rate, and humidity change rate, respectively, according to the membership interval range of the variable parameter and the reference parameter, including: Determining the center position information of the variable parameter according to the interval range of the variable parameter and the preset center rule, and determining the membership function corresponding to the variable parameter according to the interval range and the center position information of the variable parameter; and, According to the interval range of the benchmark parameter and the preset center rule, the center position information of the benchmark parameter is determined, and according to the interval range and the center position information of the benchmark parameter, the membership function corresponding to the benchmark parameter is determined.

7. The method according to any one of claims 1 to 6, characterized in that: Determine the carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space in the first time period, including: Determine the temperature data and humidity data corresponding to the target space in the first time period, and process the temperature data and humidity data respectively to obtain temperature change rate data and humidity change rate data; and, Determine carbon monoxide concentration data and smoke concentration data corresponding to the target space within the first time period.

8. A fire detection device, characterized in that: The device comprises: A first determination unit is used to determine carbon monoxide concentration data, smoke concentration data, temperature change rate data, and humidity change rate data corresponding to the target space within a first time period; A second determining unit, used to determine a membership function included in a preset fuzzy inference system for fire detection in the target space; a processing unit, configured to process the carbon monoxide concentration data, the smoke concentration data, the temperature change rate data, and the humidity change rate data according to the membership function to obtain prediction result information; The detection unit is used to determine the fire detection information of the target space within a first time period according to the prediction result information and the preset fuzzy rules included in the preset fuzzy inference system.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

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