Risk threshold determination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a risk threshold determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target equipment type and a target site type of target fire-fighting monitoring equipment and current monitoring information in a preset period; the target site type is the type of a site monitored by the target fire-fighting monitoring equipment; based on the target equipment type and the target site type, determining a reference risk threshold prediction model corresponding to the target fire-fighting monitoring equipment from a preset model library; inputting the current monitoring information into a reference risk threshold prediction model, and determining a current risk threshold corresponding to the target fire-fighting monitoring equipment according to the output of the reference risk threshold prediction model; and determining a target risk threshold value corresponding to the target fire-fighting monitoring equipment based on a reference risk threshold value corresponding to the reference risk threshold value prediction model and the current risk threshold value. Through the technical scheme of the embodiment of the invention, the risk threshold value of the fire-fighting monitoring equipment can be accurately and conveniently determined.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of risk management technology, and in particular, to a method, device, electronic device and storage medium for determining a risk threshold. Background Art

[0002] In the operation of fire monitoring equipment, the setting of risk threshold is a crucial link. The risk threshold is directly related to the sensitivity and response speed of the equipment to fire risk, and also affects the accuracy and efficiency of fire prevention.

[0003] At present, risk thresholds are usually set based on human experience. However, this method of manually setting risk thresholds results in the risk thresholds remaining unchanged when the monitoring scene changes, resulting in false alarms or missed alarms, which affects the effectiveness of fire prevention. Summary of the invention

[0004] The embodiments of the present invention provide a risk threshold determination method, device, electronic device and storage medium to accurately and conveniently determine the risk threshold of fire monitoring equipment, thereby improving the efficiency and accuracy of risk threshold determination.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining a risk threshold, comprising:

[0006] Obtaining a target device type, a target site type, and current monitoring information within a preset period of a target fire monitoring device; the target site type is the type of site monitored by the target fire monitoring device; the current monitoring information includes: device status information and application scenario information;

[0007] Based on the target equipment type and the target site type, determining a reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library;

[0008] Inputting the current monitoring information into the reference risk threshold prediction model, and determining the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model;

[0009] Based on the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold, a target risk threshold corresponding to the target fire monitoring equipment is determined.

[0010] Optionally, the method also includes: if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is within the allowable error range of the target fire monitoring equipment, then the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment; if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is not within the allowable error range of the target fire monitoring equipment, then determining the on-site review result, and based on the on-site review result, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information, determining the target risk threshold corresponding to the target fire monitoring equipment.

[0011] Optionally, the method further includes: triggering an on-site review instruction so that a reviewer reviews the application scenario information and feeds back the obtained on-site review result corresponding to the target fire monitoring equipment.

[0012] Optionally, the method also includes: if the on-site review result is a short-term change in the application scenario, then determining the reference risk threshold as the target risk threshold corresponding to the target fire monitoring equipment; if the on-site review result is a long-term change in the application scenario, then training the reference risk threshold prediction model through the current monitoring information to obtain a trained target risk threshold prediction model, and inputting the current monitoring information into the target risk threshold prediction model, and determining the target risk threshold corresponding to the target fire monitoring equipment according to the output of the target risk threshold prediction model.

[0013] Optionally, the method also includes: for each preset period, using the target risk threshold of the previous preset period as the reference risk threshold of the current preset period, and using the reference risk threshold prediction model or the target risk threshold prediction model of the previous preset period as the reference risk threshold prediction model of the current preset period; and verifying the reference risk threshold prediction model of the current preset period based on the current monitoring information and the reference risk threshold in the current preset period.

[0014] Optionally, the method further includes: determining the information uploading priority of the target fire monitoring equipment based on a threshold difference between a target risk threshold corresponding to the target fire monitoring equipment and a reference risk threshold.

[0015] Optionally, the method further includes: the period length of the preset period is negatively correlated with the frequency of change of the application scenario information.

[0016] In a second aspect, an embodiment of the present invention further provides a risk threshold determination device, the device comprising:

[0017] An information acquisition module is used to acquire a target device type, a target site type and current monitoring information within a preset period of a target fire monitoring device; the target site type is the type of site monitored by the target fire monitoring device; the current monitoring information includes: device status information and application scenario information;

[0018] A reference risk threshold prediction model determination module is used to determine a reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library based on the target equipment type and the target site type;

[0019] A current risk threshold determination module, used to input the current monitoring information into the reference risk threshold prediction model, and determine the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model;

[0020] The target risk threshold determination module is used to determine the target risk threshold corresponding to the target fire monitoring equipment based on the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold.

[0021] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0022] one or more processors;

[0023] A memory for storing one or more programs;

[0024] When the one or more programs are executed by the one or more processors, the one or more processors implement the risk threshold determination method provided by any embodiment of the present invention.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a risk threshold determination method as provided in any embodiment of the present invention.

[0026] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the risk threshold determination method provided by any embodiment of the present invention.

[0027] The technical solution of the embodiment of the present invention obtains the target equipment type, target site type and current monitoring information within a preset period of the target fire monitoring equipment; based on the target equipment type and the target site type, determines the reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library; inputs the current monitoring information into the reference risk threshold prediction model, determines the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model, and accurately and conveniently determines the target risk threshold of the site monitored by the target fire monitoring equipment based on the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold, thereby improving the efficiency and accuracy of determining the risk threshold for the monitored site.

[0028] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 is a flow chart of a method for determining a risk threshold provided in Embodiment 1 of the present invention;

[0031] Figure 2 is a flow chart of a method for determining a risk threshold provided in Embodiment 2 of the present invention;

[0032] Figure 3 is a schematic diagram of the structure of a risk threshold determination device provided in Embodiment 3 of the present invention;

[0033] Figure 4 It is a structural schematic diagram of an electronic device for implementing the risk threshold determination method of an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] Embodiment 1

[0037] Figure 1 A flowchart of a method for determining a risk threshold is provided for the first embodiment of the present invention. This embodiment is applicable to the case of determining a risk threshold corresponding to a fire monitoring device. The method can be executed by a risk threshold determination device. The risk threshold determination device can be implemented in the form of hardware and / or software. The risk threshold determination device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0038] S110, obtaining a target device type, a target site type, and current monitoring information within a preset period of a target fire monitoring device.

[0039] Among them, the target site type is the type of site monitored by the target fire monitoring equipment. For example, the target site type may be but is not limited to an office, a particle workshop or a dangerous goods storage warehouse. The current monitoring information includes: equipment status information and application scenario information. The equipment status information may refer to the warning status information of the target fire monitoring equipment at each monitoring time. The application scenario information may refer to the site status information of the monitoring site at each monitoring time. The application scenario information may include a variety of information that may affect the possibility of fire and the severity of the consequences of fire. For example, the application scenario information may include but is not limited to the installation location, environmental conditions, personnel density and potential risk sources of the fire monitoring equipment. The fire monitoring equipment may be used to monitor specific indicators in the site. For example, the fire monitoring equipment may be but is not limited to a smoke detector, a temperature sensor, a flame detector or a gas detector. The specific indicator may be but is not limited to particle concentration, temperature, light radiation or combustible gas concentration. The equipment type may be divided according to the type of monitoring indicator of the fire monitoring equipment. For example, the equipment type of the smoke detector is a smoke monitoring type, and the equipment type of the temperature sensor is a temperature monitoring type. Different types of fire monitoring equipment have different working principles, sensitivities and monitoring ranges. These characteristics determine that each type of fire monitoring equipment performs differently when monitoring fire risks, so it is necessary to set a specific risk threshold for each type of fire monitoring equipment. The preset period may refer to a preset verification period. When the preset period is about to end, the risk threshold prediction model used in the preset period may be verified using the current monitoring information obtained in the preset period.

[0040] Specifically, the target fire monitoring equipment to be installed at the monitoring site is determined, and the target equipment type of the target fire monitoring equipment is obtained. The target fire monitoring equipment is installed at the monitoring site, and the type of the site monitored by the target fire monitoring equipment, that is, the target site type, is obtained. The current monitoring information within a preset period is obtained using the installed target fire monitoring equipment according to the preset monitoring frequency.

[0041] S120. Based on the target equipment type and the target site type, determine a reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library.

[0042] Among them, in order to accurately assess the fire risk, a risk threshold prediction model can be pre-built to achieve risk threshold prediction and setting focused on fire monitoring equipment. The risk threshold prediction model can analyze the key features in the monitoring information and ensure the scientificity and accuracy of the risk threshold prediction. The risk threshold prediction model integrates abnormal monitoring algorithms, pattern recognition, statistical analysis and expert rules to adapt to the characteristics of different equipment, dynamically predict risk thresholds to respond to changes in the fire risk environment (monitoring site), and achieve accurate management and prevention of fire risks. The preset model library includes a variety of pre-trained risk threshold prediction models. Each risk threshold prediction model has a corresponding equipment type and site type. Each risk threshold prediction model can be trained using the historical monitoring information of each fire monitoring equipment at each monitoring site. The reference risk threshold prediction model can be trained using the historical monitoring information of the target fire monitoring equipment at the corresponding monitoring site.

[0043] Specifically, a risk threshold prediction model corresponding to the target equipment type and the target site type is determined from a preset model library, and the risk threshold prediction model is used as a reference risk threshold prediction model corresponding to the target fire monitoring equipment. A predetermined reference risk threshold corresponding to the reference risk threshold prediction model may also be determined. The reference risk threshold may be determined using historical monitoring information of the target fire monitoring equipment and the reference risk threshold prediction model.

[0044] S130. Input the current monitoring information into a reference risk threshold prediction model, and determine the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model.

[0045] The reference risk threshold prediction model can be used to predict the risk threshold of the monitoring site based on the current monitoring information of the monitoring site. In order to use the predicted accurate risk threshold and issue a risk warning for the monitoring site in the next preset cycle. The current risk threshold may refer to the risk threshold of the site monitored by the target fire monitoring equipment to be determined whether it meets the requirements.

[0046] Specifically, the current monitoring information is input into the reference risk threshold prediction model to perform risk threshold prediction, and the result output by the reference risk threshold prediction model is used as the current risk threshold corresponding to the target fire monitoring equipment.

[0047] S140. Determine a target risk threshold corresponding to the target fire monitoring equipment based on a reference risk threshold corresponding to the reference risk threshold prediction model and a current risk threshold.

[0048] The target risk threshold may refer to an accurate risk threshold that meets the site monitored by the target fire monitoring device. The target risk threshold may be used to achieve accurate monitoring of the site monitored by the target fire monitoring device.

[0049] Specifically, if the reference risk threshold is the same as the current risk threshold, the reference risk threshold will continue to be used as the risk threshold for the site monitored by the target fire monitoring device. If the reference risk threshold is different from the current risk threshold but the same as the current risk threshold, the current risk threshold can be used as the risk threshold for the site monitored by the target fire monitoring device on the premise of eliminating equipment failure.

[0050] The technical solution of the embodiment of the present invention obtains the target equipment type, target site type and current monitoring information within a preset period of the target fire monitoring equipment; based on the target equipment type and the target site type, determines the reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library; inputs the current monitoring information into the reference risk threshold prediction model, determines the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model, and accurately and conveniently determines the target risk threshold of the site monitored by the target fire monitoring equipment based on the reference risk threshold and the current risk threshold corresponding to the reference risk threshold prediction model, thereby improving the efficiency and accuracy of determining the risk threshold for the monitored site.

[0051] Based on the above technical solution, the method also includes: determining the information upload priority of the target fire monitoring equipment based on the threshold difference between the target risk threshold corresponding to the target fire monitoring equipment and the reference risk threshold.

[0052] Among them, the greater the threshold difference between the target risk threshold and the reference risk threshold, the more special the site monitored by the target fire monitoring equipment is, and more attention needs to be allocated. For example, in high-risk areas such as chemical plants, due to the presence of flammable and explosive substances, the risk of fire is higher, so it is necessary to set the risk threshold (such as the concentration of combustible gas in the air) lower, so as to detect signs of fire earlier, and increase the information upload priority of fire monitoring equipment in high-risk areas such as chemical plants, so that the information receiving center can receive the information first and respond. In low-risk areas such as libraries, the risk threshold does not need to be lowered, and can be appropriately increased to reduce false alarms. At the same time, it is also necessary to lower the information upload priority of fire monitoring equipment in low-risk areas such as libraries.

[0053] Specifically, based on the threshold difference between the target risk threshold and the reference risk threshold corresponding to the target fire monitoring device, the information upload priority of the target fire monitoring device is determined from a preset priority mapping table. The priority mapping table may include a mapping relationship between the threshold difference and the priority.

[0054] Based on the above technical solution, the period length of the preset period is negatively correlated with the frequency of change of the application scenario information.

[0055] Among them, the frequency of change of application scenario information may refer to the frequency of effective changes in application scenario information. Effective change may refer to the situation where application scenario information needs to be maintained for a long time after a change. When the preset period is about to end, the risk threshold prediction model used in the preset period can be verified using the various current monitoring information obtained in the preset period. By making the period length of the preset period negatively correlated with the frequency of change of application scenario information, the interval for verifying the risk threshold prediction model can be lengthened when the frequency of change is low (information fluctuations are stable), and the interval for verifying the risk threshold prediction model can be shortened when the frequency of change is high, so as to achieve dynamic model verification and retraining, and then achieve dynamic adjustment of the target risk threshold.

[0056] Embodiment 2

[0057] Figure 2 This is a flow chart of a method for determining a risk threshold provided in the second embodiment of the present invention. Based on the above embodiments, this embodiment describes in detail the process of determining the target risk threshold corresponding to the target fire monitoring equipment. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2 As shown, the method includes:

[0058] S210: Obtain the target device type, target site type and current monitoring information within a preset period of the target fire monitoring device.

[0059] The target site type is the type of site monitored by the target fire monitoring device. The current monitoring information includes: device status information and application scenario information.

[0060] S220: Based on the target equipment type and the target site type, determine a reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library.

[0061] S230: Input the current monitoring information into the reference risk threshold prediction model, and determine the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model.

[0062] S240. If the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is within the allowable error range of the target fire monitoring equipment, the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment.

[0063] The error tolerance range may refer to a reasonable deviation range of the risk threshold value pre-set for each fire monitoring device. The threshold difference within the error tolerance range may indicate that the monitoring information fluctuation is stable. The advantage of such a setting is that, through the pre-set error tolerance range, an accurate target risk threshold value is obtained while excluding the normal information fluctuation of the monitoring information, which further improves the accuracy of determining the target risk threshold value.

[0064] For example, in an ordinary office scenario, based on the target device type and target site type of the temperature sensor, the reference risk threshold prediction model corresponding to the temperature sensor is determined from the preset model library. And the reference risk threshold of 35°C corresponding to the reference risk threshold prediction model is determined. The current risk threshold of 32°C is obtained by using the current monitoring information obtained and the reference risk threshold prediction model. If the threshold difference between the reference risk threshold and the current risk threshold is within the error tolerance range of the target fire monitoring equipment, the technician will not be required to conduct on-site review, and the reference risk threshold of 35°C will continue to be used as the target risk threshold, and the changes in the threshold can continue to be monitored to ensure timely follow-up and adjustment when large threshold changes occur in the future.

[0065] S250. If the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is not within the allowable error range of the target fire monitoring equipment, the on-site review result is determined, and based on the on-site review result, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information, the target risk threshold corresponding to the target fire monitoring equipment is determined.

[0066] The on-site review result may refer to the result of reviewing the fluctuation of monitoring information and the actual situation of the site. The on-site review result may include short-term changes in application scenarios and long-term changes in application scenarios.

[0067] Specifically, if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is not within the allowable error range of the target fire monitoring equipment, indicating that the monitoring information fluctuation is unstable (abnormal), it is necessary to determine the on-site review result, and determine the method for determining the target risk threshold based on the on-site review result. If the on-site review result is a short-term change in the application scenario, the target risk threshold corresponding to the target fire monitoring equipment is determined based on the reference risk threshold. If the on-site review result is a long-term change in the application scenario, the target risk threshold corresponding to the target fire monitoring equipment is determined based on the reference risk threshold prediction model and the current monitoring information. Thereby, dynamic adjustment of the target risk threshold for the monitoring site is achieved, further improving the accuracy of determining the target risk threshold.

[0068] On the basis of the above technical solution, "determining the on-site review results" may include: triggering an on-site review instruction so that the reviewer reviews the application scenario information and feeds back the on-site review results corresponding to the target fire monitoring equipment.

[0069] The on-site review instruction may refer to an instruction to notify the reviewer to review the monitoring site. The on-site review instruction may include the location information of the monitoring site and the time period required for review.

[0070] Specifically, if the threshold difference between the reference risk threshold and the current risk threshold is not within the error tolerance range of the target fire monitoring equipment, the on-site review instruction is triggered so that the reviewer reviews the application scenario information and feeds back the on-site review results corresponding to the target fire monitoring equipment. For example, the particle concentration in the monitoring information is 50% higher than that of the previous cycle. If the review finds that the increased monitored particle concentration is due to the addition of a new production line in the particle workshop, and the newly added production line will still be used in the next few preset cycles, the on-site review result will be determined as a long-term change in the application scenario. If the review finds that the increased monitored particle concentration is due to employees in the particle workshop working overtime to rush a batch of expedited products, and the expedited products need to be produced intensively in a short period of time and rarely appear in normal times, the on-site review result will be determined as a short-term change in the application scenario.

[0071] It should be noted that when the on-site review result is a short-term change in the application scenario, the monitoring information with abnormal fluctuations can be marked so that the marked monitoring information does not need to be trained in the risk threshold prediction model.

[0072] On the basis of the above technical scheme, "determining the target risk threshold corresponding to the target fire monitoring equipment based on the on-site review results, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information" may include: if the on-site review result is a short-term change in the application scenario, the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment; if the on-site review result is a long-term change in the application scenario, the reference risk threshold prediction model is trained through the current monitoring information to obtain a trained target risk threshold prediction model, and the current monitoring information is input into the target risk threshold prediction model, and the target risk threshold corresponding to the target fire monitoring equipment is determined according to the output of the target risk threshold prediction model.

[0073] Among them, the target risk threshold prediction model may refer to a prediction model obtained by unsupervised training based on current monitoring information.

[0074] Specifically, if the on-site review result is a short-term change in the application scenario, the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment. If the on-site review result is a long-term change in the application scenario, the reference risk threshold prediction model is retrained through the current monitoring information to obtain a trained target risk threshold prediction model, and the current monitoring information is input into the target risk threshold prediction model for risk threshold prediction, and the target risk threshold corresponding to the target fire monitoring equipment is determined based on the output of the target risk threshold prediction model.

[0075] It should be noted that the above operation can be understood as non-fixed periodic training and model optimization. In view of the dynamic changes in monitoring information, a non-fixed periodic model training method is adopted. This operation aims to continuously collect new monitoring information and update the model in time when the monitoring information meets the change conditions, so as to optimize and adjust the risk threshold of the fire monitoring equipment, and then through the review of monitoring information and model adjustment, ensure that the risk threshold is always close to the actual situation, and further improve the early warning accuracy of the fire protection system connected to the fire monitoring equipment.

[0076] Exemplarily, for the dynamic adjustment of the risk threshold, this adjustment process is non-periodic. For example, a maximum interval (preset period) limit can be set, and the fire monitoring model can be verified for no longer than a preset period to ensure that even if the monitoring information does not change significantly, that is, the fluctuation is stable, the model can be verified and updated regularly. In the scenario of the particle workshop, the risk threshold setting of the smoke concentration will be higher than that of some typical office scenes, and the risk threshold will be automatically adjusted to 2.0 (dB / m). The advantage of this setting is that it can avoid false alarms of smoke detectors. Therefore, the smoke concentration threshold of the particle workshop needs to adjust the higher risk threshold to distinguish between smoke generated by normal production and smoke actually caused by fire hazards.

[0077] The technical solution of the embodiment of the present invention is that if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is within the allowable error range of the target fire monitoring equipment, it indicates that the current monitoring information within a preset period has normal fluctuations, thereby determining the reference risk threshold as the target risk threshold corresponding to the target fire monitoring equipment; if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is not within the allowable error range of the target fire monitoring equipment, it indicates that the current monitoring information within the preset period has abnormal fluctuations, then determining the on-site review result, and then determining the target risk threshold corresponding to the target fire monitoring equipment based on the on-site review result, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information, thereby further improving the accuracy of determining the risk threshold for the monitoring site.

[0078] Based on the above technical solution, the method also includes: for each preset period, using the target risk threshold of the previous preset period as the reference risk threshold of the current preset period, and using the reference risk threshold prediction model or the target risk threshold prediction model of the previous preset period as the reference risk threshold prediction model of the current preset period; verifying the reference risk threshold prediction model of the current preset period based on the current monitoring information and the reference risk threshold in the current preset period.

[0079] It should be noted that the model can be verified and the risk threshold can be adjusted in each preset period to achieve continuous dynamic adjustment of the risk threshold. For example, the risk threshold of each monitoring indicator can be continuously and dynamically adjusted by combining the results of non-fixed periodic training and on-site review. This process is automated and relies on real-time monitoring information and model optimization to ensure that it can respond quickly to changes in monitoring information (environmental changes and equipment status changes), thereby ensuring the accuracy of the target risk threshold determination, and thus maintaining a high level of early warning sensitivity and accuracy.

[0080] The following is an embodiment of a risk threshold determination device provided in an embodiment of the present invention. The device and the risk threshold determination methods of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the risk threshold determination device, please refer to the embodiment of the above-mentioned risk threshold determination method.

[0081] Embodiment 3

[0082] Figure 3 This is a schematic diagram of the structure of a risk threshold determination device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an information acquisition module 310, a reference risk threshold prediction model determination module 320, a current risk threshold determination module 330 and a target risk threshold determination module 340.

[0083] Among them, the information acquisition module 310 is used to obtain the target equipment type, target site type and current monitoring information within a preset period of the target fire monitoring equipment; the target site type is the type of site monitored by the target fire monitoring equipment; the current monitoring information includes: equipment status information and application scenario information; the reference risk threshold prediction model determination module 320 is used to determine the reference risk threshold prediction model corresponding to the target fire monitoring equipment from the preset model library based on the target equipment type and the target site type; the current risk threshold determination module 330 is used to input the current monitoring information into the reference risk threshold prediction model, and determine the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model; the target risk threshold determination module 340 is used to determine the target risk threshold corresponding to the target fire monitoring equipment based on the reference risk threshold and the current risk threshold corresponding to the reference risk threshold prediction model.

[0084] The technical solution of the embodiment of the present invention obtains the target equipment type, target site type and current monitoring information within a preset period of the target fire monitoring equipment; based on the target equipment type and the target site type, determines the reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library; inputs the current monitoring information into the reference risk threshold prediction model, determines the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model, and accurately and conveniently determines the target risk threshold of the site monitored by the target fire monitoring equipment based on the reference risk threshold and the current risk threshold corresponding to the reference risk threshold prediction model, thereby improving the efficiency and accuracy of determining the risk threshold for the monitored site.

[0085] Based on the above technical solution, the target risk threshold determination module 340 may include:

[0086] A first target risk threshold determination submodule is used to determine the reference risk threshold as the target risk threshold corresponding to the target fire monitoring device if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is within the error allowable range of the target fire monitoring device;

[0087] The second target risk threshold determination submodule is used to determine the on-site review result if the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is not within the allowable error range of the target fire monitoring equipment, and determine the target risk threshold corresponding to the target fire monitoring equipment based on the on-site review result, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information.

[0088] Based on the above technical solution, the second target risk threshold determination submodule may include:

[0089] The on-site review result determination unit is used to trigger an on-site review instruction so that the reviewer can review the application scenario information and feedback the on-site review result corresponding to the target fire monitoring equipment.

[0090] Based on the above technical scheme, the second target risk threshold determination submodule is specifically used for: if the on-site review result is a short-term change in the application scenario, the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment; if the on-site review result is a long-term change in the application scenario, the reference risk threshold prediction model is trained through the current monitoring information to obtain the trained target risk threshold prediction model, and the current monitoring information is input into the target risk threshold prediction model, and the target risk threshold corresponding to the target fire monitoring equipment is determined according to the output of the target risk threshold prediction model.

[0091] On the basis of the above technical solution, the device also includes:

[0092] A cycle conversion module is used to use the target risk threshold of the previous preset cycle as the reference risk threshold of the current preset cycle for each preset cycle, and use the reference risk threshold prediction model or the target risk threshold prediction model of the previous preset cycle as the reference risk threshold prediction model of the current preset cycle;

[0093] The model verification module is used to verify the reference risk threshold prediction model of the current preset period based on the current monitoring information and the reference risk threshold in the current preset period.

[0094] On the basis of the above technical solution, the device also includes:

[0095] The information upload priority determination module is used to determine the information upload priority of the target fire monitoring equipment based on the threshold difference between the target risk threshold corresponding to the target fire monitoring equipment and the reference risk threshold.

[0096] Based on the above technical solution, the period length of the preset period is negatively correlated with the frequency of change of the application scenario information.

[0097] The risk threshold determination device provided in the embodiment of the present invention can execute the risk threshold determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the risk threshold determination method.

[0098] It is worth noting that in the above-mentioned embodiment of risk threshold determination, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0099] Embodiment 4

[0100] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0101] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the risk threshold determination method.

[0104] In some embodiments, the risk threshold determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the risk threshold determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the risk threshold determination method in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0110] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0111] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the risk threshold determination method provided in any embodiment of the present application.

[0112] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). The program product and the risk threshold determination method disclosed in each embodiment of the present application belong to the same inventive concept, so it is not repeated here.

[0113] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a risk threshold, characterized in that: include: Obtain the target equipment type, target site type and current monitoring information within a preset period of the target fire monitoring equipment; The target site type is the type of site monitored by the target fire monitoring equipment; The current monitoring information includes: device status information and application scenario information; Based on the target equipment type and the target site type, determining a reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library; Inputting the current monitoring information into the reference risk threshold prediction model, and determining the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model; Based on the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold, a target risk threshold corresponding to the target fire monitoring equipment is determined.

2. The method according to claim 1, characterized in that The determining, based on the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold, the target risk threshold corresponding to the target fire monitoring equipment comprises: If the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is within the allowable error range of the target fire monitoring equipment, the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment; If the threshold difference between the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold is not within the allowable error range of the target fire monitoring equipment, the on-site review result is determined, and based on the on-site review result, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information, the target risk threshold corresponding to the target fire monitoring equipment is determined.

3. The method according to claim 2, characterized in that Determining the on-site review results includes: An on-site review instruction is triggered to enable the reviewer to review the application scenario information and provide feedback on the on-site review result corresponding to the target fire monitoring equipment.

4. The method according to claim 2, characterized in that: The determining, based on the on-site review result, the reference risk threshold, the reference risk threshold prediction model and the current monitoring information, a target risk threshold corresponding to the target fire monitoring equipment comprises: If the on-site review result is a short-term change in the application scenario, the reference risk threshold is determined as the target risk threshold corresponding to the target fire monitoring equipment; If the on-site review result is a long-term change in the application scenario, the reference risk threshold prediction model is trained using the current monitoring information to obtain a trained target risk threshold prediction model, and the current monitoring information is input into the target risk threshold prediction model. The target risk threshold corresponding to the target fire monitoring equipment is determined based on the output of the target risk threshold prediction model.

5. The method according to claim 1, characterized in that The method further comprises: For each preset period, the target risk threshold of the previous preset period is used as the reference risk threshold of the current preset period, and the reference risk threshold prediction model or the target risk threshold prediction model of the previous preset period is used as the reference risk threshold prediction model of the current preset period; The reference risk threshold prediction model for the current preset period is verified based on the current monitoring information and the reference risk threshold within the current preset period.

6. The method according to claim 1, characterized in that The method further comprises: Based on the threshold difference between the target risk threshold corresponding to the target fire monitoring equipment and the reference risk threshold, the information uploading priority of the target fire monitoring equipment is determined.

7. The method according to claim 1, characterized in that The period length of the preset period is negatively correlated with the frequency of change of the application scenario information.

8. A risk threshold determination device, characterized in that: The device comprises: An information acquisition module is used to acquire a target device type, a target site type and current monitoring information within a preset period of a target fire monitoring device; the target site type is the type of site monitored by the target fire monitoring device; the current monitoring information includes: device status information and application scenario information; A reference risk threshold prediction model determination module is used to determine a reference risk threshold prediction model corresponding to the target fire monitoring equipment from a preset model library based on the target equipment type and the target site type; A current risk threshold determination module, used to input the current monitoring information into the reference risk threshold prediction model, and determine the current risk threshold corresponding to the target fire monitoring equipment according to the output of the reference risk threshold prediction model; The target risk threshold determination module is used to determine the target risk threshold corresponding to the target fire monitoring equipment based on the reference risk threshold corresponding to the reference risk threshold prediction model and the current risk threshold.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the risk threshold determination method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the risk threshold determination method as described in any one of claims 1 to 7 is implemented.

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