Fire extinguishing early warning method and system and storage medium

Abstract

The invention relates to a fire extinguishing early warning method and system and a storage medium, the system at least comprises edge equipment and a battery fire extinguishing device, the battery fire extinguishing device is arranged in a to-be-monitored battery box cabin, and the battery fire extinguishing device is used for collecting current environment information in the battery box cabin; the current environment information is used for reflecting the working condition state of a battery in the battery box cabin; and the edge device is used for calling a working condition prediction model to perform battery working condition prediction on the current environment information to obtain a predicted battery working condition state. Therefore, the technical problem that corresponding early warning cannot be effectively provided for the battery in the prior art can be solved.

Description

Technical Field

[0001] The present disclosure relates to the field of battery management technology, and in particular, to a fire extinguishing early warning method, system and storage medium. Background Art

[0002] With the strong support of the state for the new energy vehicle industry, new energy vehicles have a long-term development space, the market ownership is increasing year by year, and user acceptance is getting higher and higher. As the core component of new energy vehicles, power batteries directly affect the safety performance of vehicles. How to make power batteries work stably for a long time and improve the service life of power batteries has become an important topic of discussion.

[0003] At present, most of the existing battery early warning solutions are equipped with sensors in the power battery compartment to monitor data, and issue an early warning when one or more monitoring indicators of the sensors are abnormal. However, at this time, the accident has often occurred, and it is impossible to effectively provide the power battery with the corresponding early warning. Therefore, how to accurately predict the operating status of the power battery is an important issue that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present disclosure is to provide a fire extinguishing warning method, system and storage medium, which can solve the technical problems existing in the prior art such as the inability to effectively provide corresponding early warning for batteries.

[0005] In order to achieve the above-mentioned object, the first aspect of the present disclosure provides a fire extinguishing warning system, the system at least comprising an edge device and a battery fire extinguishing device, the battery fire extinguishing device being arranged in a battery box compartment to be monitored, wherein:

[0006] The battery fire extinguishing device is used to collect current environmental information in the battery compartment and send the current environmental information to the edge device, wherein the current environmental information is used to reflect the working status of the battery in the battery compartment;

[0007] The edge device is used to receive the current environmental information, call the operating condition prediction model to predict the battery operating condition of the current environmental information, and obtain a predicted battery operating condition status. The battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

[0008] Optionally, the system further includes a cloud device, wherein:

[0009] The edge device is further used to send target key information to the cloud device, wherein the target key information includes the predicted battery operating state and / or environmental abnormality information, and the environmental abnormality information is information in which an abnormality occurs in the current environmental information;

[0010] The cloud device is used to receive the target key information, and perform corresponding fire extinguishing warning regulation on the edge device and / or the battery fire extinguishing device based on the target key information.

[0011] Optionally, the cloud device is used to:

[0012] Sending first control information to the edge device based on the target key information; and / or,

[0013] Sending second control information to the battery fire extinguishing device based on the target key information;

[0014] Among them, the first control information is used to instruct the edge device to perform corresponding fire extinguishing warning on the batteries in the battery compartment; the second control information is used to instruct the battery fire extinguishing device to perform corresponding fire extinguishing treatment on the batteries in the battery compartment.

[0015] Optionally, when the battery operating status is used to indicate that a corresponding fire extinguishing warning regulation needs to be performed on the batteries in the battery compartment,

[0016] The edge device is also used to provide a fire extinguishing warning to the batteries in the battery compartment, and / or control the battery fire extinguishing device to extinguish the fire of the batteries in the battery compartment.

[0017] Optionally, the system further comprises a training device, wherein the training device is used to:

[0018] Acquire training sample data, wherein the training sample data includes historical environmental information in the battery compartment and corresponding battery operating status;

[0019] The preset neural network model is trained according to the training sample data to obtain the operating condition prediction model.

[0020] Optionally, the acquiring training sample data includes:

[0021] Get initial sample data;

[0022] The initial sample data is preprocessed to obtain the training sample data, wherein the preprocessing includes at least one of the following processes: abnormality elimination, data conversion, data cleaning and feature selection.

[0023] Optionally, the battery operating state includes at least one of the following: a load operating state, a temperature operating state, a humidity operating state, and a pressure operating state.

[0024] Optionally, the current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data and environmental smoke data.

[0025] A second aspect of the present disclosure provides a fire extinguishing warning method, which is applied to a fire extinguishing warning system, wherein the system at least includes an edge device and a battery fire extinguishing device, wherein the battery fire extinguishing device is disposed in a battery compartment to be monitored, and the method includes:

[0026] The battery fire extinguishing device is used to collect current environmental information in the battery compartment, where the current environmental information is used to reflect the working status of the batteries in the battery compartment;

[0027] The edge device calls the operating condition prediction model to predict the battery operating condition of the current environmental information to obtain a predicted battery operating condition status, and the battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

[0028] For the contents not introduced or described in the embodiments of the present disclosure, please refer to the relevant introduction in the aforementioned system embodiments, which will not be repeated here.

[0029] A third aspect of the present disclosure provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the steps of the fire extinguishing warning method provided in the second aspect of the present disclosure.

[0030] The disclosed embodiment provides a fire extinguishing warning system, the system at least includes an edge device and a battery fire extinguishing device, the battery fire extinguishing device is arranged in the battery compartment to be monitored, wherein: the battery fire extinguishing device is used to collect the current environmental information in the battery compartment, and send the current environmental information to the edge device, the current environmental information is used to reflect the battery working condition status in the battery compartment; the edge device is used to receive the current environmental information, call the working condition prediction model to predict the battery working condition of the current environmental information, and obtain the predicted battery working condition status, the battery working condition status is used to indicate whether the battery in the battery compartment is subjected to corresponding fire extinguishing warning regulation, and the working condition prediction model is a model pre-trained with training sample data. In this way, the battery fire extinguishing device can collect more real and accurate current environmental information in the battery compartment, and then the edge device can use the working condition prediction model to monitor the current environmental information in the battery compartment in real time and predict the working condition, so as to predict and obtain a more precise / accurate battery working condition status, which is conducive to improving the accuracy and convenience of working condition prediction. At the same time, it also solves the technical problems existing in the prior art such as the inability to effectively provide corresponding early warning for the battery.

[0031] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0033] Figure 1 It is a schematic diagram of an application scenario according to an exemplary embodiment.

[0034] Figure 2 The figure is a schematic structural diagram of a fire extinguishing early warning system according to an exemplary embodiment.

[0035] Figure 3 It is a structural schematic diagram of another fire extinguishing early warning system according to an exemplary embodiment.

[0036] Figure 4 It is a structural schematic diagram of another fire extinguishing early warning system according to an exemplary embodiment.

[0037] Figure 5 The figure is a flow chart of a fire extinguishing early warning method according to an exemplary embodiment.

[0038] Figure 6 The diagram is a schematic diagram of the structure of an edge device according to an exemplary embodiment.

[0039] Figure 7 The figure is a schematic structural diagram of a battery fire extinguishing device according to an exemplary embodiment.

[0040] Figure 8 The figure is a schematic diagram showing the structure of a cloud device according to an exemplary embodiment.

[0041] Fig. 9 It is a schematic diagram of the structure of a training device according to an exemplary embodiment.

[0042] Fig.10 It is a schematic structural diagram of a communication device according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0044] In the description of the embodiments of the present disclosure, the terms used, such as "first", "second", etc., are used to distinguish similar objects and do not have to be understood as a specific order or sequence. In addition, in the description with reference to the accompanying drawings, the same symbols in different drawings represent the same elements unless otherwise stated.

[0045] In the description of the embodiments of the present disclosure, unless otherwise specified, "multiple" refers to two or more than two, and other quantifiers are similar thereto. "At least one item (individual)", "one item (individual) or multiple items (individual)" or similar expressions thereof refer to any combination of these items (individuals), including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual) can represent any number; for another example, one item (individual) or multiple items (individuals) of a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. "And / or" is a kind of association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " indicates that the associated objects before and after are in an "or" relationship. The singular forms of "one", "a kind of", "one", "the", "said", "above" and "aforementioned" are also intended to include plural forms unless the context clearly indicates other meanings.

[0046] In the description of the embodiments of the present disclosure, "in one case A, in another case B", "in response to one case A, in response to another case B", etc., the following technical solutions may be included according to the situation: A is executed independently of B, that is, in some embodiments A; B is executed independently of A, that is, in some embodiments B; A and B are selectively executed, that is, selected from A and B for execution in some embodiments; A and B are both executed, that is, A and B in some embodiments. When there are more branches such as A, B, C, etc., the above is also similar.

[0047] In the description of the embodiments of the present disclosure, “including A”, “comprising A”, “used to indicate A”, and “carrying A” may be interpreted as directly carrying A or as indirectly indicating A.

[0048] In the description of the embodiments of the present disclosure, terms such as “in response to…”, “in response to determining…”, “in the case of…”, “at…”, “when…”, “if…”, “if…”, etc. may be used interchangeably.

[0049] In some embodiments, terms such as "greater than", "greater than or equal to", "above", "higher than", and "not less than" can be interchangeable, and terms such as "less than", "less than or equal to", "below", "lower than", and "not greater than" can be interchangeable.

[0050] In some embodiments, terms such as "network element", "node", "function", "device", "equipment", "system", "chip", and "chip system" can be used interchangeably.

[0051] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure may be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns may also be implemented as an independent embodiment.

[0052] Although the operations or steps are described in a specific order in the embodiments or drawings of the present disclosure, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present disclosure, if there is no contradiction, these operations or steps can be performed in any order; these operations or steps can also be performed in parallel; some of these operations or steps can also be performed; and the operations or steps in multiple embodiments or drawings can also be arbitrarily combined, and the embodiments of the present disclosure are not limited to this.

[0053] It should be noted that the embodiments of the present disclosure are not exhaustive, but are only illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be combined arbitrarily; in addition, the embodiments can be combined arbitrarily, for example, some or all steps of different embodiments can be combined arbitrarily, and a certain embodiment can be combined arbitrarily with the optional implementation methods of other embodiments.

[0054] In the existing battery early warning scheme, the monitoring system often issues an early warning when one or more sensor indicators are abnormal, but the accident has already occurred at this time, and it is unable to effectively provide corresponding early warning for sudden battery disasters. In addition, such sensors may spray by mistake, for example, the power battery has not experienced thermal runaway, but the fire extinguishing device has already sprayed to extinguish the fire.

[0055] To solve the above problems, the present disclosure provides a fire extinguishing warning system, method and storage medium. First, a possible scenario embodiment applicable to the present disclosure is introduced. Figure 1 FIG. 1 is a schematic diagram of an application scenario according to an exemplary embodiment. Figure 1The scenario shown includes the edge, cloud and user side. Among them, the edge can be deployed with, for example, edge devices 100, battery fire extinguishing devices 200, memory and other customized devices or equipment. The cloud can be deployed with cloud devices 300, such as cloud servers and other devices or equipment. The user side can be deployed with user equipment 400, such as user mobile phones, tablet computers and other terminal devices with communication functions, which are not limited in this disclosure. Any device or device in this scenario can communicate with each other according to actual communication needs. For example, the various devices or devices in the edge are deployed in a car, and they can communicate with the cloud device 300 and / or the user device 400 through the vehicle communication bus, which is not limited in this disclosure.

[0056] The edge device 100 of the present disclosure refers to a device that can be installed at the edge and has computing capabilities, such as a device that has the ability to predict the battery operating state, etc. The edge device 100 may include, but is not limited to, for example, a vehicle controller, a vehicle monitoring device, etc.

[0057] The battery fire extinguishing device 200 disclosed in the present invention refers to a device that can be installed at the edge and has a fire extinguishing function, which may include but is not limited to sensors, memories, fire extinguishing devices or other custom devices, etc. The present invention deploys the edge device 100 and the battery fire extinguishing device 200 at the edge, which can reduce the stability requirements of the vehicle-mounted equipment on the network communication, so as to improve the smoothness of the communication.

[0058] The cloud device 300 disclosed in the present invention refers to a device that can be installed in the cloud and has a communication function, which may include but is not limited to devices or equipment such as a cloud server and a cloud platform.

[0059] The user device 400 of the present disclosure may include, but is not limited to, for example, a smart phone, a wearable device (personal hub), a personal or mobile multimedia player, a personal digital assistant (PDA), a laptop computer, a tablet computer, a smartbook, a palm computer or other personal electronic devices with communication functions, etc. The functions of each device or component in the embodiment of the present disclosure will be described in detail below in the present disclosure, and will not be repeated here.

[0060] Based on the above embodiments, the following describes the system embodiments involved in the present disclosure. Figure 2 FIG. 1 is a schematic diagram showing a structure of a fire extinguishing warning system according to an exemplary embodiment. Figure 2 The system shown may include at least an edge device 100 and a battery fire extinguishing device 200, wherein the battery fire extinguishing device 200 is disposed in a battery compartment to be monitored.

[0061] The battery fire extinguishing device 200 is used to collect current environmental information in the battery compartment and send the current environmental information to the edge device 100, where the current environmental information is used to reflect the battery operating status in the battery compartment;

[0062] The edge device 100 is used to receive the current environmental information, call the operating condition prediction model to predict the battery operating condition of the current environmental information, and obtain a predicted battery operating condition status. The battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning control on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

[0063] The present disclosure does not limit the implementation method for collecting the above-mentioned current environmental information. For example, the battery fire extinguishing device 200 can use the corresponding installed sensors to collect the above-mentioned current environmental information in the battery compartment in real time or periodically.

[0064] The environmental information involved in the present disclosure refers to the environmental information in the battery compartment, which may include but is not limited to any one or more of the following combinations: for example, the environmental temperature data, environmental humidity data, environmental pressure data, environmental smoke data, environmental volatile organic compounds (VOC) data or other environmental data used to describe the battery compartment, etc. Among them, the environmental smoke data includes but is not limited to, for example, carbon monoxide concentration, carbon dioxide concentration, other smoke concentrations or other data used to describe smoke, etc., which are not limited in the present disclosure.

[0065] The battery operating state involved in the present disclosure refers to the operating state used to describe the battery operation, which may include but is not limited to any one or more of the following combinations: load operating state, temperature operating state, humidity operating state, pressure operating state or other custom operating state, etc. In practical applications, the above-mentioned operating states can be further subdivided. For example, the load operating state can include an overload operating state and a non-overload operating state. The overload operating state refers to the operating state in which the load of the battery is higher than the preset load value, and the probability of the existence of a safety hazard is high. The non-overload operating state refers to the operating state in which the load of the battery is not higher than the preset load value, and the probability of the current existence of a safety hazard is low. The preset load value is a numerical value set by the system, for example, it can be an empirical value set according to user experience, or it can be a statistical value calculated based on a series of experimental data, etc., which is not limited by the present disclosure.

[0066] The temperature operating state may include an abnormal temperature operating state and a normal temperature operating state. The abnormal temperature operating state refers to an operating state in which the temperature data in the environmental information is not within the preset temperature range. At this time, it is very easy for the battery temperature to be too high or too low, and then a corresponding accident occurs. The normal temperature operating state refers to an operating state in which the temperature data in the environmental information is within the preset temperature range. The preset temperature range is the normal range of battery temperature set by the system, and the present disclosure does not make too many restrictions and details on this.

[0067] The humidity working state may include an abnormal humidity working state and a normal humidity working state. The abnormal humidity working state refers to a working state in which the humidity data in the environmental information is higher than a preset humidity value; and the normal humidity working state refers to a working state in which the humidity data in the environmental information is not higher than a preset humidity value. The preset humidity value is a humidity threshold value set by the system, for example, it may be an empirical value set according to user experience, or a statistical value calculated according to a series of experimental data, etc., which is not limited in the present disclosure.

[0068] The pressure operating state may include an abnormal pressure operating state and a normal pressure operating state. The abnormal pressure operating state refers to an operating state in which the pressure data in the environmental information is higher than the preset pressure value; and the normal pressure operating state refers to an operating state in which the pressure data in the environmental information is not higher than the preset pressure value. The preset pressure value is a pressure threshold set by the system, for example, it may be an empirical value set according to user experience, or a statistical value calculated based on a series of experimental data, etc., which is not limited in the present disclosure.

[0069] Accordingly, the edge device 100 can determine whether it is necessary to perform corresponding fire extinguishing warning regulation on the batteries in the battery compartment according to the above-mentioned battery operating status. For example, when the above-mentioned battery operating status is a corresponding abnormal operating status, it is used to indicate that it is necessary to perform corresponding fire extinguishing warning regulation on the batteries in the battery compartment. At this time, the edge device 100 can perform corresponding fire extinguishing warning on the batteries in the battery compartment, such as outputting fire extinguishing warning prompt information to prompt the user to extinguish the fire of the batteries in the battery compartment. The present disclosure does not limit the form of expression of the above-mentioned fire extinguishing warning prompt information, for example, it can be expressed in the form of subtitles, text, voice, vibration, or any one or a combination of multiple thereof.

[0070] In an optional embodiment, when the above-mentioned battery operating status is used to indicate the need for corresponding fire extinguishing warning regulation of the batteries in the battery compartment, the edge device 100 may also control the above-mentioned battery fire extinguishing device 200 to directly extinguish the batteries in the battery compartment. In a specific implementation, the edge device 100 may send control information or signaling to the battery fire extinguishing device 200 to instruct the battery fire extinguishing device 200 to extinguish the batteries in the battery compartment. It should be noted that when the above-mentioned battery operating status is used to indicate the need for corresponding fire extinguishing warning regulation of the batteries in the battery compartment, the above-mentioned two embodiments may be selected to be implemented separately or in combination, and the present disclosure does not limit this.

[0071] The operating condition prediction model involved in the present disclosure refers to a model used to predict the battery operating condition, which may include but is not limited to, for example, a deep neural network model, a recurrent neural network model, a recursive neural network model, a convolutional neural network model, a deep generative model, a deep belief network model, a generative adversarial network model or other deep learning models, etc. For the convenience of describing the present disclosure, the following description takes the operating condition prediction model as a neural network model as an example to introduce the relevant content, but it does not constitute a limitation, and it can be replaced with other models according to actual needs.

[0072] The operating condition prediction model disclosed in this disclosure is a pre-trained model. The following describes an example of training the operating condition prediction model. Figure 3 FIG. 1 is a schematic diagram showing a structure of a fire extinguishing warning system according to an exemplary embodiment. Figure 3 The system shown may include an edge device 100 and a battery fire extinguishing device 200, and the system may also include a training device 500. The relevant introduction of the edge device 100 and the battery fire extinguishing device 200 may refer to the relevant introduction of the aforementioned embodiment, and will not be repeated here. The training device 500 is used for:

[0073] Acquire training sample data, wherein the training sample data includes historical environmental information in the battery compartment and corresponding battery operating status;

[0074] The preset neural network model is trained according to the training sample data to obtain the operating condition prediction model.

[0075] The training device 500 involved in the present disclosure refers to a device used to train to obtain the above-mentioned operating condition prediction model, which can be determined according to actual conditions. For example, the training device 500 may include but is not limited to the above-mentioned edge device 100, cloud device 300, user device 400 or other custom selected devices.

[0076] The present disclosure does not limit the implementation method for obtaining the above training sample data. For example, in one example, the data can be obtained from other devices (such as user devices or servers) through the network. In another example, the training device 500 can obtain initial sample data, and then preprocess the initial sample data to obtain the final training sample data. The preprocessing includes but is not limited to any one or more of the following processes: for example, abnormal elimination, filtering, data conversion, data cleaning, feature selection or other data preprocessing.

[0077] The above-mentioned initial sample data and the above-mentioned training sample data involved in the present disclosure may include historical environmental information collected in the battery compartment and data such as the actual battery operating status corresponding to the historical environmental information. The introduction of the historical environmental information can refer to the relevant introduction of the aforementioned environmental information, which will not be repeated here.

[0078] The present disclosure does not limit the training implementation method of the above-mentioned neural network model. For example, the training device 500 can train each network layer (such as the input layer, hidden layer and output layer, etc.) in the neural network model according to the obtained training sample data, so as to obtain the model parameters of each network layer, thereby obtaining the final trained working condition prediction model mentioned above.

[0079] After obtaining the above working condition prediction model, the training device 500 can transplant / install the working condition prediction model into the edge device 100, so that the edge device 100 can use the working condition prediction model to predict the working condition of the current environmental information in the battery compartment. In this way, the training and use of the working condition prediction model can be designed separately, which can effectively avoid the working condition prediction model from relying too much on network stability, that is, reducing the degree of dependence of the edge device 100 on network stability.

[0080] See also Figure 4 FIG. 1 is a schematic diagram showing a structure of a fire extinguishing warning system according to an exemplary embodiment. Figure 4 The system shown may include an edge device 100 and a battery fire extinguishing device 200, and the system may also include a cloud device 300. The relevant introduction of the edge device 100 and the battery fire extinguishing device 200 may refer to the relevant introduction of the aforementioned embodiment, and will not be repeated here.

[0081] The edge device 100 is further used to send target key information to the cloud device 300, wherein the target key information includes the predicted battery operating state and / or environmental abnormality information, and the environmental abnormality information is information about abnormalities in the current environmental information;

[0082] The cloud device 300 is used to receive the target key information and perform corresponding fire extinguishing warning regulation on the edge device and / or the battery fire extinguishing device based on the target key information.

[0083] The target key information involved in the present disclosure includes but is not limited to the predicted battery operating status and / or environmental abnormality information. The environmental abnormality information refers to information that is abnormal in the above-mentioned current environmental information, such as environmental temperature data greater than a preset temperature value, environmental smoke concentration greater than a preset concentration value, and other data. In other words, when the edge device 100 detects an abnormality in the above-mentioned current environmental information, it can send the environmental abnormality information to the cloud device 300 for processing.

[0084] When the above-mentioned target key information includes the above-mentioned battery operating status, the cloud device 300 can perform corresponding fire extinguishing warning control on the edge device 100 and / or the battery fire extinguishing device 200 based on the above-mentioned battery operating status.

[0085] When the above-mentioned target key information includes the above-mentioned environmental abnormality information, the cloud device 300 can perform corresponding fire extinguishing warning control on the edge device 100 and / or the battery fire extinguishing device 200 based on the above-mentioned environmental abnormality information.

[0086] When the above-mentioned target key information includes the above-mentioned battery operating status and the above-mentioned environmental abnormality information, the cloud device 300 can perform corresponding fire extinguishing warning control on the edge device 100 and / or the battery fire extinguishing device 200 based on the above-mentioned battery operating status and the above-mentioned environmental abnormality information.

[0087] The present disclosure does not limit the specific implementation methods of the above-mentioned fire extinguishing warning control. For example, the cloud device 300 can send the first control information to the edge device 100 based on the above-mentioned target key information; and / or, the cloud device 300 can send the second control information to the battery fire extinguishing device 200 based on the above-mentioned target key information. Among them, the first control information is used to instruct the edge device 100 to perform a corresponding fire extinguishing warning on the battery in the battery compartment, and its specific control instruction content can be determined according to the above-mentioned target key information. For example, if the above-mentioned target key information indicates that the ambient temperature data is too high, the first control information can be used to instruct the edge device 100 to perform operations such as deceleration or pull over to reduce the battery temperature in the battery compartment and reduce safety hazards. The above-mentioned second control information is used to instruct the battery fire extinguishing device 200 to perform corresponding fire extinguishing treatment on the battery in the battery compartment. For example, if the above-mentioned target key information indicates that the ambient smoke concentration is large, indicating that the battery has caught fire at this time, the above-mentioned second control information is directly used to instruct the battery fire extinguishing device 200 to perform fire extinguishing treatment on the battery in the battery compartment.

[0088] By implementing the embodiments of the present disclosure, a fire extinguishing warning system is provided, the system comprising at least an edge device and a battery fire extinguishing device, the battery fire extinguishing device being arranged in a battery compartment to be monitored, wherein: the battery fire extinguishing device is used to collect current environmental information in the battery compartment, and send the current environmental information to the edge device, the current environmental information is used to reflect the operating condition of the battery in the battery compartment; the edge device is used to receive the current environmental information, call a working condition prediction model to predict the battery working condition of the current environmental information, and obtain a predicted battery working condition, the battery working condition is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment, and the working condition prediction model is a model pre-trained using training sample data. In this way, the battery fire extinguishing device can collect more real and accurate current environmental information in the battery compartment, and then the edge device can use the working condition prediction model to monitor the current environmental information in the battery compartment in real time and predict the working condition, so as to predict and obtain more precise / accurate battery working conditions, which is conducive to avoiding the phenomenon of battery fire extinguishing device not spraying or spraying by mistake in daily monitoring in the existing technology, preventing more serious accidents and disasters, and helping to improve the accuracy and convenience of working condition prediction and improve battery safety. At the same time, it also solves the technical problems existing in the existing technology such as the inability to effectively provide corresponding early warnings for batteries.

[0089] Based on the above embodiments, the following describes the method embodiments involved in the present disclosure. Figure 5 FIG. 1 is a flow chart of a fire extinguishing early warning method according to an exemplary embodiment. Figure 5 The method shown is applied to the above Figures 2 to 4 In the fire extinguishing early warning system shown, the method includes:

[0090] S501, the fire extinguishing warning system collects the current environmental information in the battery compartment through the battery fire extinguishing device 200, and the current environmental information is used to reflect the battery working condition in the battery compartment;

[0091] S502, the fire extinguishing warning system calls the operating condition prediction model through the edge device 100 to predict the battery operating condition of the current environmental information, and obtains the predicted battery operating condition status. The battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

[0092] In some embodiments, the battery operating status includes at least one of the following: a load operating status, a temperature operating status, a humidity operating status, and a pressure operating status.

[0093] In some embodiments, the current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data, and environmental smoke data.

[0094] The present disclosure does not limit the implementation method for collecting the above-mentioned current environmental information. For example, the battery fire extinguishing device 200 can use the corresponding installed sensors to collect the above-mentioned current environmental information in the battery compartment in real time or periodically.

[0095] The environmental information involved in the present disclosure refers to the environmental information in the battery compartment, which may include but is not limited to any one or more of the following combinations: for example, the environmental temperature data, environmental humidity data, environmental pressure data, environmental smoke data, environmental volatile organic compounds (VOC) data or other environmental data used to describe the battery compartment, etc. Among them, the environmental smoke data includes but is not limited to, for example, carbon monoxide concentration, carbon dioxide concentration, other smoke concentrations or other data used to describe smoke, etc., which are not limited in the present disclosure.

[0096] The battery operating state involved in the present disclosure refers to the operating state used to describe the battery operation, which may include but is not limited to any one or more of the following combinations: load operating state, temperature operating state, humidity operating state, pressure operating state or other customized operating state, etc. For the introduction of the above battery operating state, please refer to the relevant introduction in the above system embodiment, which will not be repeated here.

[0097] The edge device 100 can determine whether it is necessary to perform corresponding fire extinguishing warning regulation on the batteries in the battery compartment according to the above-mentioned battery operating status. For example, when the above-mentioned battery operating status is a corresponding abnormal operating status, it is used to indicate that it is necessary to perform corresponding fire extinguishing warning regulation on the batteries in the battery compartment. At this time, the edge device 100 can perform corresponding fire extinguishing warning on the batteries in the battery compartment, such as outputting fire extinguishing warning prompt information to prompt the user to extinguish the fire of the batteries in the battery compartment. The present disclosure does not limit the form of expression of the above-mentioned fire extinguishing warning prompt information, for example, it can be expressed in the form of subtitles, text, voice, vibration, or any one or a combination of multiple thereof.

[0098] In some embodiments, the fire extinguishing warning system performs corresponding fire extinguishing warning regulation on the edge device and / or the battery fire extinguishing device based on the target key information through the cloud device 300, and the target key information includes the predicted battery operating status and / or environmental abnormality information, and the environmental abnormality information is the information in which abnormalities occur in the current environmental information.

[0099] The target key information involved in the present disclosure includes but is not limited to the predicted battery operating status and / or environmental abnormality information. The environmental abnormality information refers to information that is abnormal in the above-mentioned current environmental information, such as environmental temperature data greater than a preset temperature value, environmental smoke concentration greater than a preset concentration value, and other data. In other words, when the edge device 100 detects an abnormality in the above-mentioned current environmental information, it can send the environmental abnormality information to the cloud device 300 for processing.

[0100] In some embodiments, the cloud device 200 sends a first control information to the edge device based on the target key information; and / or sends a second control information to the battery fire extinguishing device based on the target key information; wherein the first control information is used to instruct the edge device to perform a corresponding fire extinguishing warning on the battery in the battery compartment; and the second control information is used to instruct the battery fire extinguishing device to perform a corresponding fire extinguishing treatment on the battery in the battery compartment.

[0101] In some embodiments, when the battery operating condition is used to indicate that corresponding fire extinguishing warning regulation is required for the batteries in the battery compartment, the edge device 100 performs a fire extinguishing warning on the batteries in the battery compartment, and / or controls the battery fire extinguishing device to perform fire extinguishing treatment on the batteries in the battery compartment.

[0102] In some embodiments, the training device 500 obtains training sample data, and the training sample data includes historical environmental information in the battery compartment and the corresponding battery operating status; a preset neural network model is trained according to the training sample data to obtain the operating condition prediction model.

[0103] The training device 500 involved in the present disclosure refers to a device used to train to obtain the above-mentioned operating condition prediction model, which can be determined according to actual conditions. For example, the training device 500 may include but is not limited to the above-mentioned edge device 100, cloud device 300, user device 400 or other custom selected devices.

[0104] In some embodiments, the training device 500 acquires initial sample data; preprocesses the initial sample data to obtain the training sample data, and the preprocessing includes at least one of the following processes: anomaly elimination, data conversion, data cleaning, and feature selection.

[0105] The present disclosure does not limit the implementation method for obtaining the above training sample data. For example, in one example, the data can be obtained from other devices (such as user devices or servers) through the network. In another example, the training device 500 can obtain initial sample data, and then preprocess the initial sample data to obtain the final training sample data. The preprocessing includes but is not limited to any one or more of the following processes: for example, abnormal elimination, filtering, data conversion, data cleaning, feature selection or other data preprocessing.

[0106] The above-mentioned initial sample data and the above-mentioned training sample data involved in the present disclosure may include historical environmental information collected in the battery compartment and data such as the actual battery operating status corresponding to the historical environmental information. The introduction of the historical environmental information can refer to the relevant introduction of the aforementioned environmental information, which will not be repeated here.

[0107] The present disclosure does not limit the training implementation method of the above-mentioned neural network model. For example, the training device 500 can train each network layer (such as the input layer, hidden layer and output layer, etc.) in the neural network model according to the obtained training sample data, so as to obtain the model parameters of each network layer, thereby obtaining the final trained working condition prediction model mentioned above.

[0108] For the contents not introduced or described in the embodiments of the present disclosure, please refer to the relevant introduction in the aforementioned system embodiments, which will not be repeated here.

[0109] Through the disclosed embodiment, the current environmental information in the battery compartment is collected through the battery fire extinguishing device, and the current environmental information is used to reflect the battery operating status in the battery compartment; the edge device calls the operating condition prediction model to predict the battery operating condition of the current environmental information, and obtains the predicted battery operating condition. The battery operating condition is used to indicate whether the battery in the battery compartment is subjected to corresponding fire extinguishing warning regulation, and the operating condition prediction model is a model pre-trained with training sample data. In this way, the battery fire extinguishing device can collect more real and accurate current environmental information in the battery compartment, and then the edge device can use the operating condition prediction model to monitor the current environmental information in the battery compartment in real time and predict the operating condition, so as to predict a more precise / accurate battery operating condition, which is conducive to improving the accuracy and convenience of the operating condition prediction. At the same time, it also solves the technical problems existing in the prior art such as the inability to effectively provide corresponding early warnings for batteries.

[0110] In an exemplary embodiment, the present disclosure further provides a fire extinguishing warning system, which may include at least an edge device 100 and a battery fire extinguishing device 200. Optionally, the system may also include a cloud device 300 and a training device 500. Among them, the edge device 100 can execute the fire extinguishing warning method involving the edge device in the aforementioned embodiment of the present disclosure; the battery fire extinguishing device 200 can execute the fire extinguishing warning method involving the battery fire extinguishing device in the aforementioned embodiment of the present disclosure; the cloud device 300 can execute the fire extinguishing warning method involving the cloud device in the aforementioned embodiment of the present disclosure; the training device 500 can execute the fire extinguishing warning method involving the training device in the aforementioned embodiment of the present disclosure.

[0111] See also Figure 6 FIG. 1 is a schematic diagram showing the structure of an edge device according to an exemplary embodiment. Figure 6 The edge device 100 shown includes a communication module 101 and a processing module 102; wherein:

[0112] The communication module 101 is used to receive current environment information sent by the battery fire extinguishing device 200, and the current environment information is used to reflect the battery operating status in the battery compartment;

[0113] The processing module 102 is used to call the operating condition prediction model to predict the battery operating condition of the current environmental information to obtain a predicted battery operating condition status, and the battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

[0114] In some embodiments, the communication module 101 is also used to send target key information to the cloud device, and the target key information includes the predicted battery operating status and / or environmental abnormality information, and the environmental abnormality information is information about abnormalities in the current environmental information.

[0115] In some embodiments, when the battery operating condition is used to indicate that corresponding fire extinguishing warning regulation is required for the batteries in the battery compartment, the processing module 102 is specifically used to provide a fire extinguishing warning for the batteries in the battery compartment, and / or control the battery fire extinguishing device to perform fire extinguishing treatment on the batteries in the battery compartment.

[0116] In some embodiments, the battery operating status includes at least one of the following: a load operating status, a temperature operating status, a humidity operating status, and a pressure operating status.

[0117] In some embodiments, the current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data, and environmental smoke data.

[0118] See also Figure 7 FIG. 1 is a schematic diagram showing the structure of a battery fire extinguishing device according to an exemplary embodiment. Figure 7 The battery fire extinguishing device 200 shown includes a communication module 201 and a processing module 202; wherein:

[0119] The processing module 202 is used to collect current environmental information in the battery compartment;

[0120] The communication module 201 is used to send the current environment information to the edge device 100, and the current environment information is used to reflect the battery operating status in the battery compartment.

[0121] In some embodiments, the communication module 201 is further used to receive second control information sent by the cloud device 300, and the second control information is used to instruct the battery fire extinguishing device to perform corresponding fire extinguishing treatment on the batteries in the battery compartment.

[0122] In some embodiments, the battery operating status includes at least one of the following: a load operating status, a temperature operating status, a humidity operating status, and a pressure operating status.

[0123] In some embodiments, the current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data, and environmental smoke data.

[0124] See also Figure 8 FIG. 1 is a schematic diagram showing a structure of a cloud device according to an exemplary embodiment. Figure 8 The cloud device 300 shown includes a communication module 301 and a processing module 302; wherein:

[0125] The communication module 301 is used to receive target key information sent by the edge device 100, wherein the target key information includes the predicted battery operating state and / or environmental abnormality information, and the environmental abnormality information is information in which an abnormality occurs in the current environmental information;

[0126] The processing module 302 is used to perform corresponding fire extinguishing warning regulation on the edge device and / or the battery fire extinguishing device based on the target key information.

[0127] In some embodiments, the communication module 301 is specifically used for:

[0128] Sending first control information to the edge device based on the target key information; and / or,

[0129] Sending second control information to the battery fire extinguishing device based on the target key information;

[0130] Among them, the first control information is used to instruct the edge device to perform corresponding fire extinguishing warning on the batteries in the battery compartment; the second control information is used to instruct the battery fire extinguishing device to perform corresponding fire extinguishing treatment on the batteries in the battery compartment.

[0131] In some embodiments, the battery operating status includes at least one of the following: a load operating status, a temperature operating status, a humidity operating status, and a pressure operating status.

[0132] In some embodiments, the current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data, and environmental smoke data.

[0133] See also Fig. 9 FIG. 1 is a schematic diagram showing a structure of a training device according to an exemplary embodiment. Fig. 9 The training device 500 shown includes a communication module 501 and a processing module 502; wherein:

[0134] The communication module 501 is used to obtain training sample data, wherein the training sample data includes historical environmental information in the battery compartment and corresponding battery operating status;

[0135] The processing module 502 is used to train a preset neural network model according to the training sample data to obtain the operating condition prediction model.

[0136] In some embodiments, the communication module 501 is specifically used to obtain initial sample data;

[0137] The processing module 502 is further used to pre-process the initial sample data to obtain the training sample data, wherein the pre-processing includes at least one of the following processes: abnormal elimination, data conversion, data cleaning and feature selection.

[0138] In some embodiments, the battery operating status includes at least one of the following: a load operating status, a temperature operating status, a humidity operating status, and a pressure operating status.

[0139] In some embodiments, the current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data, and environmental smoke data.

[0140] Regarding the devices or apparatuses in the above-mentioned embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the method, and will not be elaborated here.

[0141] Through the disclosed embodiment, the current environmental information in the battery compartment is collected through the battery fire extinguishing device, and the current environmental information is used to reflect the battery operating status in the battery compartment; the edge device calls the operating condition prediction model to predict the battery operating condition of the current environmental information, and obtains the predicted battery operating condition. The battery operating condition is used to indicate whether the battery in the battery compartment is subjected to corresponding fire extinguishing warning regulation, and the operating condition prediction model is a model pre-trained with training sample data. In this way, the battery fire extinguishing device can collect more real and accurate current environmental information in the battery compartment, and then the edge device can use the operating condition prediction model to monitor the current environmental information in the battery compartment in real time and predict the operating condition, so as to predict a more precise / accurate battery operating condition, which is conducive to improving the accuracy and convenience of the operating condition prediction. At the same time, it also solves the technical problems existing in the prior art such as the inability to effectively provide corresponding early warnings for batteries.

[0142] Fig.10 FIG. 6 is a block diagram of a communication device 600 according to an exemplary embodiment. Figure 7 The communication device shown can be any one of the following: an edge device 100 in a fire extinguishing warning system, a battery fire extinguishing device 200, a cloud device 300, and a training device 500.

[0143] Reference Fig.10 , the communication device 600 may include one or more of the following components: a processing component 6002 , a memory 6004 , and a communication component 6006 .

[0144] The processing component 6002 can be used to control the overall operation of the communication device 600, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 6002 can include one or more processors 6020 to execute instructions to complete all or part of the steps of the above-mentioned fire extinguishing early warning method. In addition, the processing component 6002 can include one or more modules to facilitate the interaction between the processing component 6002 and other components. For example, the processing component 6002 can include a multimedia module to facilitate the interaction between the multimedia component and the processing component 6002.

[0145] The memory 6004 is configured to store various types of data to support operations on the communication device 600. Examples of such data include instructions for any application or method operating on the communication device 600, contact data, phone book data, messages, pictures, videos, etc. The memory 6004 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0146] The communication component 6006 is configured to facilitate wired or wireless communication between the communication device 600 and other devices. The communication device 600 can access a wireless network based on a communication standard, such as Wi-Fi, 2G, 3G, 4G, 5G, 6G, NB-IOT, eMTC, etc., or a combination thereof. In an exemplary embodiment, the communication component 6006 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 6006 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0147] In an exemplary embodiment, the communication device 600 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned fire extinguishing warning method.

[0148] The communication device 600 can be an independent electronic device or a part of an independent electronic device. For example, in one embodiment, the electronic device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be an IC or a collection of multiple ICs; the chip can include but is not limited to the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip, SoC), etc. The above-mentioned integrated circuit or chip can be used to execute executable instructions (or codes) to implement the above-mentioned fire extinguishing warning method. The executable instructions can be stored in the integrated circuit or chip, or can be obtained from other devices or equipment, such as the integrated circuit or chip includes a processor, a memory, and an interface for communicating with other devices. The executable instruction may be stored in the processor, and when the executable instruction is executed by the processor, the above-mentioned fire extinguishing warning method may be implemented; alternatively, the integrated circuit or chip may receive the executable instruction through the interface and transmit it to the processor for execution, so as to implement the above-mentioned fire extinguishing warning method.

[0149] In an exemplary embodiment, the present disclosure further provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the fire extinguishing warning method provided by the present disclosure. By way of example, the computer-readable storage medium may be a non-temporary computer-readable storage medium including instructions, for example, the above-mentioned memory 6004 including instructions, and the above-mentioned instructions may be executed by the processor 6020 of the communication device 600 to complete the above-mentioned fire extinguishing warning method. For example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0150] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a programmable device. The computer program has a code portion for executing the above-mentioned fire extinguishing early warning method when executed by the programmable device.

[0151] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0152] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A fire extinguishing early warning system, characterized in that: The system comprises at least an edge device and a battery fire extinguishing device, wherein the battery fire extinguishing device is arranged in a battery compartment to be monitored, wherein: The battery fire extinguishing device is used to collect current environmental information in the battery compartment and send the current environmental information to the edge device, wherein the current environmental information is used to reflect the working status of the battery in the battery compartment; The edge device is used to receive the current environmental information, call the operating condition prediction model to predict the battery operating condition of the current environmental information, and obtain a predicted battery operating condition status. The battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

2. The system according to claim 1, characterized in that The system also includes a cloud device, wherein: The edge device is further used to send target key information to the cloud device, wherein the target key information includes the predicted battery operating state and / or environmental abnormality information, and the environmental abnormality information is information in which an abnormality occurs in the current environmental information; The cloud device is used to receive the target key information, and perform corresponding fire extinguishing warning regulation on the edge device and / or the battery fire extinguishing device based on the target key information.

3. The system according to claim 2, characterized in that The cloud device is used for: Sending first control information to the edge device based on the target key information; and / or, Sending second control information to the battery fire extinguishing device based on the target key information; Among them, the first control information is used to instruct the edge device to perform corresponding fire extinguishing warning on the batteries in the battery compartment; the second control information is used to instruct the battery fire extinguishing device to perform corresponding fire extinguishing treatment on the batteries in the battery compartment.

4. The system according to claim 1, characterized in that When the battery operating status is used to indicate that the batteries in the battery compartment need to be regulated for fire extinguishing warning, The edge device is also used to provide a fire extinguishing warning to the batteries in the battery compartment, and / or control the battery fire extinguishing device to extinguish the fire of the batteries in the battery compartment.

5. The system according to claim 1, characterized in that The system further comprises a training device, wherein the training device is used to: Acquire training sample data, wherein the training sample data includes historical environmental information in the battery compartment and corresponding battery operating status; The preset neural network model is trained according to the training sample data to obtain the operating condition prediction model.

6. The system according to claim 5, characterized in that The obtaining of training sample data comprises: Get initial sample data; The initial sample data is preprocessed to obtain the training sample data, wherein the preprocessing includes at least one of the following processes: abnormality elimination, data conversion, data cleaning and feature selection.

7. The system according to any one of claims 1 to 6, characterized in that: The battery operating state includes at least one of the following: a load operating state, a temperature operating state, a humidity operating state, and a pressure operating state.

8. The system according to any one of claims 1 to 6, characterized in that: The current environmental information includes at least one of the following: environmental temperature data, environmental humidity data, environmental pressure data, and environmental smoke data.

9. A fire extinguishing early warning method, characterized in that: Applied to a fire extinguishing early warning system, the system includes at least an edge device and a battery fire extinguishing device, the battery fire extinguishing device is arranged in a battery compartment to be monitored, and the method includes: The battery fire extinguishing device is used to collect current environmental information in the battery compartment, where the current environmental information is used to reflect the working status of the batteries in the battery compartment; The edge device calls the operating condition prediction model to predict the battery operating condition of the current environmental information to obtain a predicted battery operating condition status, and the battery operating condition status is used to indicate whether to perform corresponding fire extinguishing warning regulation on the battery in the battery compartment. The operating condition prediction model is a model pre-trained using training sample data.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to claim 9 are implemented.

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