Work order closed-loop management method and system for automatic fault disposal of fire-fighting Internet of Things equipment

Abstract

The invention relates to the technical field of faults of the Internet of Things, in particular to a work order closed-loop management method and system for automatically handling faults of fire-fighting Internet of Things equipment. The method comprises the following steps: obtaining the equipment type, the located area code and the linkage fire-fighting equipment number, and calculating a fault risk index; the partial derivatives of the weight coefficients are solved according to the objective function, the gradient is formed according to the partial derivatives, and the weight coefficients are updated according to the partial derivatives in the reverse direction of the gradient through the principle of the gradient descent method. And the updated weight coefficient is combined with the maximum accommodation number of people corresponding to the region, the maximum linkage number of single equipment, the equipment type weight corresponding to the equipment type, the real-time number of people corresponding to the region and the linkage fire-fighting equipment number to recalculate the fault risk index, and after the weight is dynamically adjusted, influence factors are comprehensively considered, so that the fault risk index is obtained. More reasonable risk assessment is provided for each fault, risk misjudgment caused by unreasonable weight is avoided, and the accuracy of quantitative assessment of fault influence is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things fault, in particular to a work order closed loop management method and system for automatic disposal of fire-fighting Internet of Things equipment fault. BACKGROUND

[0002] In the field of fire-fighting Internet of Things equipment management, the existing automatic disposal technology for fire-fighting Internet of Things equipment fault has obvious deficiencies. The current overdue disposal mechanism is "fixed time limit triggering reminder". For example, in the document embodiment two, it is mentioned that the "unified push reminder after overdue" is used. This mechanism does not distinguish the influence range and risk level of the fault equipment. For example, the "mall atrium smoke detector fault" which covers 500 people and is associated with 10 linkage alarm devices is a high-risk fault, and the "community corridor temperature detector fault" which covers only 20 people and is associated with 2 linkage devices is a low-risk fault. Both of them use the same "2-hour overdue reminder" rule.

[0003] This rule without distinguishing the influence of the fault directly leads to a series of problems. On the one hand, low-risk faults may occupy too many maintenance resources, while high-risk faults are delayed in disposal due to not being responded to in priority, greatly increasing the fire safety hazards. This essentially reflects the defect that the "overdue rule is decoupled from the risk of fault", which makes it impossible to reasonably adjust the overdue rule according to the actual risk level of the fault.

[0004] On the other hand, although the existing automatic disposal technology for fire-fighting Internet of Things equipment fault can realize the automatic generation and overdue reminder of work orders, it lacks the cooperation of "quantitative evaluation of fault influence" and "dynamic priority adjustment". Since the fault influence cannot be accurately quantitatively evaluated, it is impossible to give a reasonable risk assessment for each fault, which may lead to risk misjudgment due to unreasonable weight. At the same time, the priority of the work order cannot be dynamically adjusted according to the actual situation, so that the existing automatic disposal technology for fire-fighting Internet of Things equipment fault cannot meet the differentiated closed loop needs in different scenarios. For example, the sensitivity and processing requirements of shopping malls and communities to fire-fighting equipment faults are different. The existing management method is difficult to adapt to these differences, resulting in low efficiency of fault handling and ineffective protection of fire safety. Therefore, there is an urgent need for a work order closed loop management method and system for automatic disposal of fire-fighting Internet of Things equipment fault. SUMMARY

[0005] The purpose of the present application is to provide a work order closed loop management method and system for automatic disposal of fire-fighting Internet of Things equipment fault to solve the problems raised in the background.

[0006] To achieve the above purpose, one of the purposes of the present application is to provide a work order closed loop management method for automatic disposal of fire-fighting Internet of Things equipment fault, comprising the following method steps:

[0007] S1, obtain the device type, the area code, and the number of linkage fire-fighting devices to calculate the fault risk index;

[0008] S2, calculate the dynamic work order overdue threshold through the fault risk index Combine the dynamic work order overdue threshold with the work order to obtain work order information with a dynamic work order overdue threshold;

[0009] Obtain the work order creation time, calculate the remaining time of the work order using the work order creation time and the dynamic work order overdue threshold, and determine whether to trigger a pre-warning reminder process and an upgrade process through the remaining time of the work order;

[0010] When the remaining time of the work order is , automatically send a pre-warning reminder to the manager; when the remaining time of the work order is 0, perform an upgrade process according to the fault risk index, and mark the fault risk index and the device type as historical closed-loop data;

[0011] S3, extract the historical closed-loop data for processing and sorting to obtain sorted historical closed-loop data to calculate the predicted disposal time;

[0012] Calculate the error function through the predicted disposal time and the real-time disposal time, use the error function as the objective function, respectively take the partial derivative of the weight coefficient according to the objective function, and obtain the gradient composed of the partial derivative;

[0013] Update the weight coefficient through the gradient to obtain the updated weight coefficient, and recalculate the fault risk index in combination with the number of linkage fire-fighting devices.

[0014] As a further improvement of the technical solution, the calculation of the fault risk index includes the following method steps:

[0015] Use the maximum number of people accommodated in the area , the maximum number of linkages of a single device , the device type weight corresponding to the device type , in combination with the real-time number of people in the area and the number of linkage fire-fighting devices to calculate the fault risk index , and the algorithm formula is: ; wherein, , , refers to the weight coefficient, and the fault risk index has a value range of 0-1.

[0016] As a further improvement of the technical solution, the calculation of the dynamic work order overdue threshold and the obtaining of the work order information include the following method steps:

[0017] Set a basic overdue threshold. and failure risk index Calculate the overdue threshold for dynamic work orders The specific algorithm formula is as follows: ,in, This refers to the adjustment coefficient;

[0018] By combining the dynamic work order overdue threshold with the work order itself, work order information with the dynamic work order overdue threshold is obtained. Through work order allocation technology, the module automatically sends the work order information to the manager corresponding to the area code.

[0019] As a further improvement to this technical solution, the processing includes:

[0020] Scenario 1: When the fault risk index is greater than or equal to the fault risk index threshold, it is upgraded to a high-risk situation. The work order is immediately pushed to the corresponding management department at the higher level, and the real-time handling time is recorded.

[0021] Scenario 2: When the fault risk index is less than the fault risk index threshold, first remind the manager. If the manager does not respond within 10 minutes, then escalate the work order and record the real-time handling time.

[0022] As a further improvement to this technical solution, the marking of historical closed-loop data includes the following method steps:

[0023] Obtain equipment status, mark work orders as closed loops, and store real-time handling time, fault risk index, and fault type data as historical closed loop data in the database.

[0024] As a further improvement to this technical solution, the calculation error function includes the following steps:

[0025] Set a fixed time point each month, and automatically extract historical closed-loop data from the database for the past 30 days at that fixed time point each month. Then, process and organize the extracted historical closed-loop data to obtain organized historical closed-loop data.

[0026] The failure risk index and its corresponding weighting coefficient are obtained from the collected historical closed-loop data. , , By using the failure risk index, combined with weighting coefficients , , Calculate the predicted handling time;

[0027] The error function is calculated by predicting the handling time and the real-time handling time.

[0028] As a further improvement to this technical solution, the recalculation of the fault risk index includes the following methods and steps:

[0029] Using the error function as the objective function, adjust the weight coefficients according to the objective function. , , Calculate the partial derivatives, obtain the partial derivatives, and then construct the gradient based on the partial derivatives;

[0030] Based on the principle of gradient descent, the weight coefficients are updated along the opposite direction of the gradient using the partial derivatives, and the number of iterations is recorded.

[0031] The iteration count and an iteration count threshold are used to determine whether to stop the iteration. The iteration stops when the preset number of iterations is reached.

[0032] When the number of iterations does not reach the preset number of iterations, the updated weight coefficients will be combined with the maximum capacity of the area, the maximum number of devices that can be linked, the weight of the device type, the real-time number of people in the area, and the number of linked fire-fighting devices to recalculate the fault risk index.

[0033] The second objective of this invention is to provide a system for a closed-loop management method of work order for automatically handling faults of fire protection IoT devices including any of the above-mentioned features, comprising a fault risk unit, a work order processing unit, and an update calculation unit.

[0034] The fault risk unit obtains the equipment type, the area code, and the number of linked fire-fighting equipment to calculate the fault risk index.

[0035] The work order processing unit receives the fault risk index from the fault risk unit and calculates the dynamic work order overdue threshold using the fault risk index. By combining the dynamic work order overdue threshold with the work order, work order information with the dynamic work order overdue threshold is obtained.

[0036] Obtain the work order creation time, calculate the remaining time of the work order based on the dynamic work order overdue threshold, determine whether to trigger early warning reminder processing and escalation processing based on the remaining time of the work order, and mark the fault risk index and equipment type as historical closed-loop data;

[0037] The update calculation unit is used to receive historical closed-loop data from the work order processing unit, calculate the predicted handling time through the historical closed-loop data, calculate the error function through the predicted handling time, use the error function as the objective function, and calculate the partial derivatives of the weight coefficients according to the objective function to obtain the gradient composed of the partial derivatives to recalculate the fault risk index.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1. In the closed-loop management method and system for automatic fault handling of fire protection IoT equipment, a dynamic work order overdue threshold is calculated using a fault risk index. The system combines the dynamic work order overdue threshold with the work order itself to obtain work order information with the dynamic work order overdue threshold attached. It obtains the work order creation time, calculates the remaining time of the work order using the creation time and the dynamic work order overdue threshold, and determines whether to trigger an early warning or escalation process based on the remaining time. When the remaining time of the work order is... When the time remaining on a work order is 0, the system automatically sends an early warning to the administrator. When the remaining time on a work order is 0, the system upgrades the processing based on the fault risk index. The fault risk index and equipment type are marked as historical closed-loop data, which avoids low-risk faults from consuming too many resources while high-risk faults are not handled in a timely manner, thus improving resource utilization efficiency. At the same time, the system performs differentiated upgrade processing based on the fault risk index to ensure that problems can be resolved in a timely manner, thus improving the efficiency of fault handling.

[0040] 2. In the closed-loop management method and system for automatic fault handling of fire protection IoT equipment, the error function is used as the objective function. Partial derivatives are calculated for each weight coefficient based on the objective function. Then, a gradient is formed from these partial derivatives. Using the principle of gradient descent, the weight coefficients are updated along the inverse direction of the gradient based on the partial derivatives. The updated weight coefficients are then combined with the maximum capacity of the area, the maximum number of simultaneous actions per device, the device type weight corresponding to the device type, and the real-time number of users in the area. The fault risk index is recalculated based on the number of linked fire-fighting equipment. After dynamically adjusting the weights, the influencing factors are comprehensively considered to provide a more reasonable risk assessment for each fault. This avoids misjudgment of risk due to unreasonable weights and improves the accuracy of the quantitative assessment of fault impact. Attached Figure Description

[0041] Fig. 1 This is a flowchart illustrating the overall method steps of the present invention;

[0042] Fig. 2 This is a system structure block diagram of the present invention.

[0043] The meanings of the labels in the diagram are as follows:

[0044] 1. Fault risk unit; 2. Work order processing unit; 3. Update calculation unit. Detailed Implementation

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1

[0047] Please see Figs. 1-2 As shown, one of the objectives of this embodiment is to provide a closed-loop management method for automatic handling of faults in fire protection IoT devices, including the following method steps:

[0048] S1. Obtain the equipment type, area code, and number of linked fire-fighting equipment to calculate the fault risk index;

[0049] S1 includes the following method steps:

[0050] Because both the smoke detector in the shopping mall atrium (covering 500 people and linked to 10 alarm devices) and the temperature detector in the residential building corridor (covering 20 people and linked to 2 devices) were malfunctioning, the same 2-hour overdue reminder rule was applied. This resulted in delays in handling high-risk malfunctions (such as those in the shopping mall) due to a lack of priority response, increasing fire safety hazards. Therefore, the device type was retrieved from the Redis database. (Different types of equipment may have different impacts in terms of scope and manner when they malfunction; for example, a power equipment malfunction may affect the power supply area, while a fire-fighting equipment malfunction may affect fire safety.) The system also includes the area code (identifying the area where the malfunctioning equipment is located), and then uses a population statistics system to obtain the real-time population count for that area. (Knowing the real-time population of the area where the failure occurred is crucial for assessing the potential casualties and social impact of the failure; for example, a failure in a densely populated area could affect more people.)

[0051] The number of linked fire-fighting equipment is obtained through the IoT gateway. (The number of linked fire-fighting devices associated with the faulty device) is used to obtain the maximum capacity of the corresponding area using the area information table pre-stored in the Redis database. Then, the maximum number of devices that can be linked at once is obtained by using the device-related information table pre-stored in the Redis database. And obtain the device type weight corresponding to the device type based on the device type weight table pre-stored in the Redis database. ;

[0052] Use the maximum capacity of the area Maximum number of devices that can be linked Equipment type weights corresponding to equipment types Combined with the real-time number of people in the corresponding area And the number of linked fire-fighting equipment Calculate the failure risk index The specific algorithm formula is as follows: ;in, The fault risk index ranges from 0 to 1, with a higher index indicating a higher fault risk. This formula is used to calculate the real-time number of people in a given area. And the number of linked fire-fighting equipment The raw data is transformed into a calculable failure risk index.

[0053] S2. Calculate the dynamic work order overdue threshold using the fault risk index. By combining the dynamic work order overdue threshold with the work order, work order information with the dynamic work order overdue threshold is obtained.

[0054] Obtain the work order creation time, calculate the remaining time of the work order using the work order creation time and dynamic work order overdue threshold, and determine whether to trigger early warning reminder processing and escalation processing based on the remaining time of the work order;

[0055] When the remaining time of the work order is When the time limit for a work order is 0, an early warning reminder will be automatically sent to the administrator. When the time limit for a work order is 0, the fault risk index and equipment type will be marked as historical closed-loop data based on the escalation of the processing.

[0056] S2 includes the following method steps:

[0057] S2.1 Setting a basic overdue threshold By setting a basic overdue threshold and failure risk index Calculate the overdue threshold for dynamic work orders The specific algorithm formula is as follows: ,in, This refers to the adjustment coefficient, which is used to adjust the degree of influence of the fault risk index on the dynamic overdue threshold.

[0058] When the dynamic work order overdue threshold is obtained, the dynamic work order overdue threshold is combined with the work order to obtain the work order information with the dynamic work order overdue threshold. Through the work order allocation technology, the module automatically sends the work order information to the manager corresponding to the region code according to the region code.

[0059] S2.2 Record the current time, then obtain the work order creation time. Add the work order creation time to the dynamic work order overdue threshold to obtain the work order's expiration time. Subtract the current time from the work order's expiration time to obtain the work order's remaining time. Determine whether to trigger an early warning or escalation process based on the remaining time of the work order. When the remaining time of the work order is... When the time limit is reached, an automatic warning reminder is sent to the administrator to inform them in advance that half of the work order processing time has passed and the processing progress needs to be expedited. When the remaining time of the work order is 0 (overdue), a fault risk index threshold is set, and the processing status is escalated based on the fault risk index and the fault risk index threshold. Specific processing statuses include:

[0060] Scenario 1: When the fault risk index is greater than or equal to the fault risk index threshold, the situation is upgraded to high-risk. The work order is immediately pushed to the relevant superior management department, and the work order processing order and real-time processing time are recorded. This is to ensure that high-risk faults can be handled in a timely and efficient manner;

[0061] Scenario 2: When the fault risk index is less than the fault risk index threshold, the administrator is first notified. If the administrator does not respond within 10 minutes, the work order is escalated. This tiered processing method differentiates the handling based on the fault risk index, and records the processed commands and real-time processing time of the work order. This improves the targeting and efficiency of fault handling;

[0062] Once a work order has been processed, the equipment status is retrieved. When the equipment status returns to normal, the work order is marked as closed-loop, and the real-time processing time is recorded. The data on fault risk index and fault type are marked as historical closed-loop data and stored in the database. This historical data will provide a basis for subsequent process optimization and form a complete closed-loop management process.

[0063] S3. Extract historical closed-loop data, process and organize it, and calculate the predicted disposal time based on the organized historical closed-loop data.

[0064] The error function is calculated by predicting the treatment time and the real-time treatment time. The error function is used as the objective function. The partial derivatives of the weight coefficients are calculated according to the objective function, and the partial derivatives form the gradient.

[0065] The updated weight coefficients are obtained by updating the weight coefficients through gradient updates, and the failure risk index is recalculated in combination with the number of linked fire-fighting equipment.

[0066] S3 includes the following steps:

[0067] S3.1. Set a fixed time point each month to automatically extract historical closed-loop data from the database for the past 30 days. To ensure the validity and optimization of the extracted historical closed-loop data, the number of data extracted should be... The number of records must be greater than or equal to 100. Then, process the extracted historical closed-loop data, removing invalid or abnormal records to obtain processed historical closed-loop data. Ensure data quality by organizing the processed historical closed-loop data to obtain organized historical closed-loop data. From the organized historical closed-loop data, obtain the first... The first work order corresponding to the Individual failure risk index Then according to the first Individual failure risk index Get the corresponding number The device type weight corresponding to each device type Real-time number of people in the corresponding area And the number of linked fire-fighting equipment and the corresponding weighting coefficients , , Through the first Individual failure risk index Equipment type weights corresponding to equipment types Real-time number of people in the corresponding area And the number of linked fire-fighting equipment Combined with weighting coefficients , , Calculate the predicted treatment time The specific algorithm formula includes: ;

[0068] By predicting the disposal time Real-time processing time and extraction quantity Calculate the error function The specific algorithm formula includes: The error function measures the overall deviation between the predicted and actual handling times. The smaller the error function value, the more accurate the prediction.

[0069] S3.2. Using the error function as the objective function, adjust the weight coefficients according to the objective function. , , By taking the partial derivative, we obtain the partial derivative. , , The partial derivative represents the rate of change of the objective function in a certain direction of the variable. Then, based on the partial derivative... , , Composition gradient Specific algorithm formula: This formula represents the objective function with the current weight coefficients. , , The rate of change and direction of change are determined using the principle of gradient descent, along the opposite direction of the gradient, by applying the partial derivatives. , , Update the weight coefficients to obtain the updated weight coefficients. , , To gradually reduce the objective function value, this process iterates multiple times, recording the number of iterations. An iteration threshold is then set, and the iteration count and threshold are used to determine whether to stop the iteration. When the preset iteration count is reached, iteration can stop; otherwise, the updated weight coefficients are used. , , Combined with the maximum capacity of the area Maximum number of devices that can be linked Equipment type weights corresponding to equipment types Real-time number of people in the corresponding area And the number of linked fire-fighting equipment Recalculate the failure risk index The specific algorithm formula includes: ;

[0070] Updated weight coefficients , , Specific algorithm formula:

[0071] ;

[0072] ;

[0073] ;

[0074] in, This refers to the learning rate, which is a pre-set positive number that controls the step size when updating the weight coefficients.

[0075] The second objective of this invention is to provide a system for operating the above-mentioned automatic fault handling work order closed-loop management method for fire protection IoT equipment, including a fault risk unit 1, a work order processing unit 2, and an update calculation unit 3.

[0076] The fault risk unit 1 obtains the equipment type, the area code, and the number of linked fire-fighting equipment to calculate the fault risk index.

[0077] The work order processing unit 2 is used to receive the fault risk index from the fault risk unit 1, and calculate the dynamic work order overdue threshold using the fault risk index. By combining the dynamic work order overdue threshold with the work order, work order information with the dynamic work order overdue threshold is obtained.

[0078] Obtain the work order creation time, calculate the remaining time of the work order based on the dynamic work order overdue threshold, determine whether to trigger early warning reminder processing and escalation processing based on the remaining time of the work order, and mark the fault risk index and equipment type as historical closed-loop data;

[0079] The update calculation unit 3 is used to receive historical closed-loop data from the work order processing unit 2, calculate the predicted handling time through the historical closed-loop data, calculate the error function through the predicted handling time, use the error function as the objective function, and calculate the partial derivatives of the weight coefficients according to the objective function to obtain the gradient composed of the partial derivatives to recalculate the fault risk index.

[0080] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A closed-loop management method for automatic fault handling of fire protection IoT equipment, characterized by: The methods and steps include the following: S1. Obtain the equipment type, area code, and number of linked fire-fighting equipment to calculate the fault risk index; S2. Calculate the dynamic work order overdue threshold using the fault risk index. By combining the dynamic work order overdue threshold with the work order, work order information with the dynamic work order overdue threshold is obtained. Obtain the work order creation time, calculate the remaining time of the work order using the work order creation time and dynamic work order overdue threshold, and determine whether to trigger early warning reminder processing and escalation processing based on the remaining time of the work order; When the remaining time of the work order is When the time limit for a work order is 0, an early warning reminder will be automatically sent to the administrator. When the time limit for a work order is 0, the fault risk index and equipment type will be marked as historical closed-loop data based on the escalation of the processing. S3. Extract historical closed-loop data, process and organize it, and calculate the predicted disposal time based on the organized historical closed-loop data. The error function is calculated by predicting the treatment time and the real-time treatment time. The error function is used as the objective function. The partial derivatives of the weight coefficients are calculated according to the objective function, and the partial derivatives form the gradient. The updated weight coefficients are obtained by updating the weight coefficients through gradient updates, and the failure risk index is recalculated by combining the number of linked fire-fighting equipment.

2. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 1, characterized in that: S1 includes the following method steps: The system retrieves the device type and its location code from the Redis database, obtains the real-time population of the area through the population statistics system, and then obtains the number of linked fire-fighting devices through the IoT gateway. It also retrieves the maximum capacity of the area, the maximum number of devices that can be linked at once, and the device type weight corresponding to the device type.

3. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 1, characterized in that: The calculation of the failure risk index includes the following steps: Use the maximum capacity of the area Maximum number of devices that can be linked Equipment type weights corresponding to equipment types Combined with the real-time number of people in the corresponding area And the number of linked fire-fighting equipment Calculate the failure risk index The algorithm formula is: ;in, , , This refers to the weighting coefficient. The failure risk index ranges from 0 to 1.

4. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 1, characterized in that: The calculation of the dynamic work order overdue threshold and the retrieval of work order information include the following steps: Set a basic overdue threshold. and failure risk index Calculate the overdue threshold for dynamic work orders The specific algorithm formula is as follows: ,in, This refers to the adjustment coefficient; By combining the dynamic work order overdue threshold with the work order itself, work order information with the dynamic work order overdue threshold is obtained. Through work order allocation technology, the module automatically sends the work order information to the manager corresponding to the area code.

5. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 1, characterized in that: The processing includes: Scenario 1: When the fault risk index is greater than or equal to the fault risk index threshold, it is upgraded to a high-risk situation. The work order is immediately pushed to the corresponding management department at the higher level, and the real-time handling time is recorded. Scenario 2: When the fault risk index is less than the fault risk index threshold, first remind the manager. If the manager does not respond within 10 minutes, then escalate the work order and record the real-time handling time.

6. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 5, characterized in that: The data marked as historical closed-loop data includes the following method steps: Obtain equipment status, mark work orders as closed loops, and store real-time handling time, fault risk index, and fault type data as historical closed loop data in the database.

7. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 1, characterized in that: The calculation of the error function includes the following steps: Set a fixed time point each month, and automatically extract historical closed-loop data from the database for the past 30 days at that fixed time point each month. Then, process and organize the extracted historical closed-loop data to obtain organized historical closed-loop data. The failure risk index and its corresponding weighting coefficient are obtained from the collected historical closed-loop data. , , By using the failure risk index, combined with weighting coefficients , , Calculate the predicted handling time; The error function is calculated by predicting the handling time and the real-time handling time.

8. The work order closed-loop management method for automatic fault handling of fire protection IoT equipment according to claim 1, characterized in that: Recalculating the failure risk index involves the following steps: Using the error function as the objective function, adjust the weight coefficients according to the objective function. , , Calculate the partial derivatives, obtain the partial derivatives, and then construct the gradient based on the partial derivatives; Based on the principle of gradient descent, the weight coefficients are updated along the opposite direction of the gradient using the partial derivatives, and the number of iterations is recorded. The iteration count and an iteration count threshold are used to determine whether to stop the iteration. The iteration stops when the preset number of iterations is reached. When the number of iterations does not reach the preset number of iterations, the updated weight coefficients will be combined with the maximum capacity of the area, the maximum number of devices that can be linked, the weight of the device type, the real-time number of people in the area, and the number of linked fire-fighting devices to recalculate the fault risk index.

9. A system for a closed-loop management method of work orders for automatic fault handling of fire protection IoT devices according to any one of claims 1, characterized in that: Fault risk unit (1), work order processing unit (2), work order processing unit (2); The fault risk unit (1) obtains the equipment type, the code of the area where it is located, and the number of linked fire-fighting equipment to calculate the fault risk index; The work order processing unit (2) is used to receive the fault risk index from the fault risk unit (1) and calculate the dynamic work order overdue threshold through the fault risk index. By combining the dynamic work order overdue threshold with the work order, work order information with the dynamic work order overdue threshold is obtained. Obtain the work order creation time, calculate the remaining time of the work order based on the dynamic work order overdue threshold, determine whether to trigger early warning reminder processing and escalation processing based on the remaining time of the work order, and mark the fault risk index and equipment type as historical closed-loop data; The update calculation unit (3) is used to receive historical closed-loop data in the work order processing unit (2), calculate the predicted disposal time through the historical closed-loop data, calculate the error function through the predicted disposal time, use the error function as the objective function, calculate the partial derivatives of the weight coefficients according to the objective function, and obtain the gradient of the partial derivatives to recalculate the fault risk index.

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