State detection method, device and equipment of fire detector and medium

Abstract

The invention is suitable for the technical field of equipment detection, and particularly relates to a state detection method and device of a fire detector, equipment and a medium. N initial detection values and K initial detection value sets are respectively fitted through an environment state selection preset fitting method; the reliability degree of the target fire detector is evaluated by using the obtained first target detection function and the plurality of second target detection functions, comprehensive consideration is carried out from multiple angles of the whole and the local, and the evaluation accuracy of the reliability degree is improved through mutual verification of the plurality of functions; determining an abnormal detection value and determining a state monitoring result of the target fire detector by integrating multiple factors such as the reliability degree, a second reference detection value curve corresponding to the environment state, the initial detection value and the first target detection function, and constraining and judging the initial detection value from different factors and aspects; the screening accuracy of the abnormal detection value is improved, and the accuracy of the state detection result of the target fire detector is further improved.

Description

Technical Field

[0001] The present invention is applicable to the technical field of equipment detection, and in particular relates to a state detection method, device, equipment and medium for a fire detector. Background Art

[0002] The state detection methods for fire detectors in the prior art include hardware self-detection methods, manual inspection methods and communication detection methods.

[0003] The hardware self-test method performs self-test through a simple built-in circuit, which can only detect relatively obvious hardware damage such as whether the power supply is normal and whether the sensor can respond to basic information. It is difficult to detect subtle performance degradation such as the gradual reduction of sensor sensitivity. The manual inspection method arranges staff to regularly perform appearance inspections and functional tests on fire detectors. This not only consumes a lot of manpower and time costs, but it is also difficult to detect sudden failures of fire detectors between two inspection cycles in a timely manner. It is also impossible to detect hidden failures in the software algorithms inside the fire detectors, which greatly reduces the reliability of the fire safety system.

[0004] The communication detection method can compare the detection data such as smoke concentration and temperature value returned by the fire detector with a pre-set fixed threshold. If the detection data exceeds the threshold range, it is determined that the fire detector is faulty, or the data currently returned by the fire detector is simply compared with the historical data of the past period of time. If the current data deviates greatly from the historical data, it is determined that the fire detector is faulty. However, the above communication detection method does not take into account the reduced reliability of the detection data caused by factors such as equipment aging, resulting in missed or misjudged faults, making it impossible to accurately and efficiently judge the true working status of the fire detector, reducing the reliability and stability of the fire detection system.

[0005] Therefore, when performing status detection of fire detectors, how to improve the accuracy of detection results becomes an urgent problem to be solved. Summary of the invention

[0006] In view of this, embodiments of the present invention provide a method, device, equipment and medium for detecting the state of a fire detector to solve the problem of low accuracy of the state detection result of the fire detector.

[0007] In a first aspect, an embodiment of the present invention provides a method for detecting a state of a fire detector, the method comprising: N initial detection values ​​corresponding to the target fire detector at N preset time points are obtained, and the N initial detection values ​​are divided into K initial detection value sets, where N and K are both integers greater than 1.

[0008] According to the first reference detection value curve corresponding to the location of the target fire detector and the N initial detection values, the environmental state corresponding to the location of the target fire detector is obtained, wherein the environmental state is a normal state or a fire state.

[0009] According to the preset fitting method corresponding to the environmental state, the N initial detection values ​​and the K initial detection value sets are fitted respectively to obtain the first target detection function of the target fire detector corresponding to the N initial detection values ​​and the second target detection function corresponding to each initial detection value set.

[0010] According to the first target detection function and the K second target detection functions, the reliability corresponding to the target fire detector is obtained.

[0011] According to the reliability, a second reference detection value curve corresponding to the environmental state, N initial detection values ​​and a first target detection function, a number of abnormal detection values ​​corresponding to the target fire detector are obtained.

[0012] According to all abnormal detection values, the detection status corresponding to the target fire detector is obtained.

[0013] In a second aspect, an embodiment of the present invention provides a state detection device for a fire detector, the state detection device for a fire detector comprising: The data acquisition module is used to obtain N initial detection values ​​corresponding to the target fire detector at N preset time points, and divide the N initial detection values ​​into K initial detection value sets, where N and K are both integers greater than 1.

[0014] The environmental state judgment module is used to obtain the environmental state corresponding to the location of the target fire detector according to the first reference detection value curve and N initial detection values ​​corresponding to the location of the target fire detector, wherein the environmental state is a normal state or a fire state.

[0015] The detection function fitting module is used to fit N initial detection values ​​and K initial detection value sets respectively according to a preset fitting method corresponding to the environmental state, so as to obtain a first target detection function corresponding to the N initial detection values ​​of the target fire detector, and a second target detection function corresponding to each initial detection value set.

[0016] The reliability analysis module is used to obtain the reliability corresponding to the target fire detector according to the first target detection function and the K second target detection functions.

[0017] The abnormal detection value screening module is used to obtain a number of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, N initial detection values ​​and the first target detection function.

[0018] The detection status acquisition module is used to obtain the detection status corresponding to the target fire detector according to all abnormal detection values.

[0019] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the fire detector status detection method of the first aspect when executing the computer program.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the state detection method of a fire detector according to the first aspect is implemented.

[0021] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: by selecting a preset fitting method through the environmental state, N initial detection values ​​and K initial detection value sets are fitted respectively, and a first target detection function is obtained to perform an overall feature analysis on the detection data, and a second target detection function is obtained to perform a local feature analysis on the detection data, which can better adapt to the data characteristics under different dimensions, and the targeted fitting method can more accurately describe the data change trend, and improve the accuracy of the characterization of the working state of the target fire detector; the reliability of the target fire detector is evaluated by using the first target detection function and multiple second target detection functions, and the reliability is comprehensively considered from multiple perspectives of the whole and the local, and the limitations of the single function evaluation are avoided by mutual verification of multiple functions, thereby improving the accuracy of the reliability evaluation; the abnormal detection value is determined by combining multiple factors such as the reliability, the second reference detection value curve corresponding to the environmental state, the initial detection value and the first target detection function, and the initial detection value is constrained and judged from different factors and aspects, thereby improving the screening accuracy of the abnormal detection value, and being able to more comprehensively identify the real abnormal situation; finally, the detection state of the target fire detector is determined according to all the abnormal detection values, thereby improving the accuracy of the state detection result of the target fire detector, and providing clear guidance for the operation and maintenance of the target fire detector. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative labor.

[0023] Figure 1 This is a schematic diagram of an application environment of a fire detector status detection method provided by Embodiment 1 of the present invention; Figure 2It is a flow chart of a method for detecting the state of a fire detector provided in the first embodiment of the present invention; Figure 3 It is a structural schematic diagram of a state detection device of a fire detector provided in Embodiment 2 of the present invention; Figure 4 It is a structural diagram of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0025] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0026] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0027] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0028] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0030] The embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0031] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0032] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0033] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0034] A fire detector status detection method provided in the first embodiment of the present invention can be applied in the following aspects: Figure 1 In the application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs), and other computer devices. The server can be implemented with an independent server or a server cluster consisting of multiple servers.

[0035] See also Figure 2, is a flow chart of a method for detecting the state of a fire detector provided in the first embodiment of the present invention. The method for detecting the state of a fire detector can be applied to Figure 1 In the client, the fire detector status detection method may include the following steps: S1, obtaining N initial detection values ​​corresponding to the target fire detector at N preset time points, and dividing the N initial detection values ​​into K initial detection value sets, where N and K are both integers greater than 1.

[0036] In a large building or area, multiple fire detectors may be installed, and the target fire detector is a specific fire detector whose status is monitored and analyzed in the fire detection system.

[0037] The preset time points are a series of pre-set time nodes, which can be evenly distributed at certain time intervals, or flexibly set according to actual detection needs, such as encrypting time points during periods of high fire incidence.

[0038] At each preset time point, the detection value corresponding to the target fire detector is obtained and divided into K initial detection value sets, which is helpful to analyze the characteristics and rules of the initial detection values ​​in more detail. The division method can be average grouping according to time order.

[0039] Optionally, N initial detection values ​​corresponding to the target fire detector at N preset time points are obtained, and the N initial detection values ​​are divided into K initial detection value sets, including: S11, obtaining identification information of a main control module, a driving module, a detection module and a target fire detector, wherein the driving module and the target fire detector are respectively connected to a loop bus.

[0040] S12, controlling the driving module to output test information to the loop bus during a first preset period of time through the main control module, wherein the test information includes identification information of the target fire detector.

[0041] S13, obtaining response information of the target fire detector from the loop bus in a second preset time period through the detection module, wherein the response information includes N initial detection values ​​corresponding to the target fire detector at N preset time points.

[0042] The first preset period is a pre-set time period, during which the main control module controls the drive module to output test information to the loop bus, limiting the time range for sending the test information, ensuring that the system performs the test operation at an appropriate time, and avoiding interference with normal fire monitoring work. The first preset period can be adjusted according to the working characteristics of the system and actual needs.

[0043] The second preset period is another preset time period, during which the detection module obtains the response information of the target fire detector from the loop bus. The second preset period includes N preset time points, thereby obtaining N initial detection values.

[0044] As described above, obtaining the initial detection values ​​of the target fire detector at multiple preset time points and dividing them into K initial detection value sets helps to analyze the characteristics and laws of the initial detection values ​​more carefully, and provides a data basis for further analysis of the working status and environmental status of the target fire detector.

[0045] S2, obtaining the environment state corresponding to the location of the target fire detector according to the first reference detection value curve corresponding to the location of the target fire detector and the N initial detection values, wherein the environment state is a normal state or a fire state, The first reference detection value curve is a curve drawn based on the historical detection data corresponding to the normal state of the environment at the location of the target fire detector. These historical detection data record the changes in the normal detection values ​​of the location at different times. For example, the normal fluctuation range of parameters such as temperature, humidity, and smoke concentration at different times of the day can be obtained by statistically analyzing and fitting a large amount of historical data, which serves as an important reference for judging the current environmental state.

[0046] Compare and analyze the first reference detection value curve at the location of the target fire detector with the N initial detection values ​​obtained. Since the first reference detection value curve represents the detection value change trend at the location under normal conditions, and the initial detection value is the current actual detection result, if the deviation between the initial detection value and the reference detection value curve is within a reasonable range, it means that the change in the current environmental parameters is in line with normal conditions, and it can be judged that the location of the target fire detector is in a normal state. On the contrary, if there is a large deviation between the initial detection value and the reference detection value curve, which exceeds the normal fluctuation range, such as a sharp rise in the detected temperature, a sudden increase in the smoke concentration, etc., it can be judged that the location is in a fire state.

[0047] As described above, the first reference detection value curve and the current initial detection value are combined to judge the environmental status, which fully considers the historical environmental characteristics of the location, avoids the misjudgment that may be caused by a single threshold judgment, and can adapt to various environmental changes in different places, different seasons, etc., thereby improving the reliability of the status detection results.

[0048] S3, fitting the N initial detection values ​​and the K initial detection value sets respectively according to the preset fitting method corresponding to the environmental state, and obtaining the first target detection function of the target fire detector corresponding to the N initial detection values, and the second target detection function corresponding to each initial detection value set.

[0049] Among them, the preset fitting method is a pre-set method for fitting the detection value data. Different fitting methods will be used according to different environmental conditions, which will help to more clearly understand the differences in the detection value change trends of the fire detector under different working conditions and environmental parameters, and provide a more reliable basis for the subsequent accurate judgment of the reliability of the detector and the detection of abnormal values.

[0050] The first target detection function is a function obtained by fitting N initial detection values ​​using a preset fitting method corresponding to the environmental state. It can comprehensively describe the detection value change trend of the target fire detector at multiple time points and reflect the comprehensive working condition of the target fire detector.

[0051] The second target detection function is a function fitted by using a preset fitting method corresponding to the environmental state for each set of initial detection values. Each second target detection function describes the changing pattern of the data in a specific set and can more deeply analyze the characteristics of different parts of the data.

[0052] As mentioned above, different preset fitting methods are selected according to different environmental conditions, and the data are fitted to deeply explore the data change rules of N initial detection values ​​and each set of initial detection values. This can be more in line with the actual situation and capture the differences in the change trends of the detection values ​​in a targeted manner, providing a more reliable basis for subsequent accurate judgment of the reliability of the target fire detector and detection of abnormal values.

[0053] Optionally, N initial detection values ​​and K initial detection value sets are fitted respectively according to a preset fitting method corresponding to the environmental state to obtain a first target detection function corresponding to the N initial detection values ​​of the target fire detector and a second target detection function corresponding to each initial detection value set, including: S31, fit N initial detection values ​​with an m-order polynomial to obtain a first reference detection function corresponding to the target fire detector and a first gradient set corresponding to the first reference detection function, wherein m is an integer greater than 1, and the first gradient set includes first gradient values ​​corresponding to N preset time points.

[0054] S32, update m=m-1, return to execute the step of fitting N initial detection values ​​with an m-order polynomial to obtain a first reference detection function corresponding to the target fire detector and a first gradient set corresponding to the first reference detection function, until the first gradient set satisfies the first preset condition corresponding to the environmental state or the updated m satisfies the second preset condition, and the first reference detection function corresponding to the condition being met is determined as the first target detection function corresponding to the target fire detector, and the number corresponding to the first target detection function is determined as the reference number c, where c is an integer greater than 0.

[0055] S33, for any initial detection value set, fit the current initial detection value set with a c-order polynomial to obtain the second reference detection function of the target fire detector corresponding to the current initial detection value set and the second gradient set corresponding to the second reference detection function, wherein the number of gradient values ​​in the second gradient set corresponding to the current initial detection value set is consistent with the number of initial detection values ​​in the current initial detection value set.

[0056] S34, update c=c-1, return to execute the step of fitting the current initial detection value set with a c-order polynomial to obtain the second reference detection function corresponding to the current initial detection value set of the target fire detector and the second gradient set corresponding to the second reference detection function, until the second gradient set satisfies the first preset condition corresponding to the environmental state or the updated c satisfies the second preset condition, and the corresponding second reference detection function when the condition is met is determined as the second target detection function corresponding to the target fire detector for the current initial detection value set.

[0057] S35, traversing all initial detection value sets to obtain a second target detection function corresponding to each initial detection value set of the target fire detector.

[0058] Among them, the m-order polynomial refers to a polynomial of the form y=a0+a1×x+ a2×x 2 +……+ a m × m The mathematical expression of m is the coefficient, x is the independent variable, y is the dependent variable, m is the degree of the polynomial, and m is an integer greater than 1. In this step, x can represent time, y can represent the detection value of the target fire detector at the corresponding time, and the m-order polynomial is used to perform a fitting operation on the N initial detection values. Through mathematical calculation, a suitable m-order polynomial function is found to make it as close as possible to the N data points, thereby obtaining a first reference detection function.

[0059] As m changes and the fitting operation is repeated, the first reference detection function is continuously adjusted, and finally a suitable first reference detection function is selected as the first target detection function through preset conditions.

[0060] The first gradient set includes the first gradient values ​​corresponding to N preset time points. The gradient value represents the rate of change of the function at a certain point. Therefore, the first gradient set reflects the change of the first reference detection function at each preset time point, and is further used to characterize the detection of the target fire detector to the environment.

[0061] The first preset condition is a judgment condition preset according to the environmental state, and is used to judge whether the first gradient set meets the requirement. The second preset condition is used to judge whether the updated m meets the requirement.

[0062] The reference degree c is the degree corresponding to the first target detection function, which is subsequently used as the degree basis of the polynomial fitted to each initial detection value set.

[0063] The second reference detection function is obtained by fitting each initial detection value set with a c-order polynomial. As c continues to change and the fitting operation is repeated, the second reference detection function is continuously adjusted. Finally, a suitable second reference detection function is selected as the second target detection function through preset conditions.

[0064] The second gradient set corresponds to the second reference detection function. The number of gradient values ​​in the second gradient set is consistent with the number of initial detection values ​​in the corresponding initial detection value set, reflecting the change of the second reference detection function at the time point corresponding to the current initial detection value set.

[0065] As described above, by continuously adjusting the degree of the polynomial, the final target detection function is determined according to whether the first gradient set, the second gradient set and the degree of the polynomial meet the preset conditions, and the fitting function that best suits the data characteristics can be adaptively found, so that the first target detection function can more accurately reflect the working status of the target fire detector as a whole, and the second target detection function can help to discover local features and abnormal conditions in the detection data. Different environmental conditions and data distributions are best fitted by polynomials of different degrees, which can improve the accuracy and adaptability of the fitting, and improve the accuracy and reliability of subsequent state analysis of the target fire detector.

[0066] Optionally, the first preset condition includes: If the environment state is normal, the first gradient set satisfies the first preset condition corresponding to the environment state, which means that there is a first gradient value in the first gradient set that is less than the first preset gradient value or greater than the second preset gradient value.

[0067] If the environmental state is a fire state, the first gradient set satisfies the first preset condition corresponding to the environmental state, which means that there is a first gradient value in the first gradient set that is less than a third preset gradient value.

[0068] The second precondition includes: The updated m satisfies the second preset condition, which means that the updated m is less than the preset polynomial degree.

[0069] Among them, in data fitting, the degree of the polynomial determines the complexity of the fitting curve. A higher degree polynomial can provide a more flexible curve shape and better capture subtle changes and local features in the data. However, when the degree of the polynomial is too high, the fitting function may follow the data points too closely, including noise or abnormal fluctuations in some data, resulting in overfitting and poor representation of the overall data trend.

[0070] When the environmental state is determined to be normal, the temperature in the environment does not change much, and the first preset gradient value and the second preset gradient value are set accordingly as judgment thresholds. If there is a first gradient value in the first gradient set that is less than the first preset gradient value or greater than the second preset gradient value, it means that the fluctuation degree of the first reference detection function currently obtained by fitting with the m-order polynomial is too high, and there is overfitting, which cannot well reflect the changing trend of the data under normal conditions and does not meet the function requirements under normal conditions.

[0071] When the environmental state is determined to be a fire state, the temperature in the environment continues to rise, and a third preset gradient value is set accordingly as a judgment threshold. If there is a first gradient value less than the third preset gradient value in the first gradient set, it means that the change rate of the first reference detection function currently obtained by fitting with the m-order polynomial is too small or negative at certain time points, and the trend of rapid or steady temperature rise under the fire state is not accurately captured, resulting in the temperature change curve presented by the fitting function being inconsistent with the actual temperature rise when the fire occurs. There are abnormal values ​​in the N initial detection values, and it is necessary to reduce the degree of analysis of local data and increase the attention to overall data.

[0072] Therefore, the polynomial function is readjusted by lowering the value of m to obtain the first target detection function. Since lowering m is equivalent to reducing the number of parameters in the fitting function, the flexibility of the function is limited, making the function more inclined to capture the main trends in the data rather than fitting every detail, thereby balancing the overall representation and detailed analysis of the N initial detection values ​​by the first target detection function, and improving the representation accuracy while ensuring the representation degree of the working status of the target fire detector.

[0073] The specific values ​​of the first preset gradient value, the second preset gradient value and the third preset gradient value can be set by the implementer according to the actual situation. For example, the first preset gradient value can be set to be less than 0, the second preset gradient value can be set to be equal to 0, and the third preset gradient value can be set to be greater than 0.

[0074] In the process of continuously updating the polynomial degree, check whether the updated polynomial degree is less than the preset polynomial degree. If the preset lower limit of the polynomial degree has been reached, the degree will no longer be reduced for fitting, and the current first reference detection function will be determined as the first target detection function to reduce unnecessary calculations and improve calculation efficiency.

[0075] As mentioned above, the characteristics of data changes in normal state and fire state are different. Different first preset conditions are set according to different environmental states, so that the fitting function can better adapt to different environments, and can more accurately judge the effectiveness of the fitting function under different circumstances. The adjustment range of the polynomial degree is limited by setting a preset polynomial degree, which improves the characterization accuracy on the basis of ensuring the characterization degree of the working state of the target fire detector, thereby improving the accuracy of subsequent state detection of the fire detector.

[0076] S4, obtaining the reliability corresponding to the target fire detector according to the first target detection function and the K second target detection functions.

[0077] Among them, the smaller the difference between the first target detection function and the K second target detection functions, the more similar the change trends of the detection values ​​of the first target detection function and the K second target detection functions at different time points are, indicating that the analysis of the environmental state changes by multiple functions is similar, that is, the change trends of the multiple target detection functions can verify each other, and the multiple target detection functions are obtained under different fitting dimensions, thereby characterizing the reliability of the detection results of the target fire detector.

[0078] Optionally, obtaining the reliability corresponding to the target fire detector according to the first target detection function and the K second target detection functions includes: S41, for any second target detection function, according to the preset time points corresponding to the initial detection value set corresponding to the current second target detection function, obtain the time point range corresponding to the current second target detection function.

[0079] S42, calculating and obtaining the difference between the current second target detection function and the first target detection function within a time point range.

[0080] S43, traverse K second target detection functions to obtain the difference between each second target detection function and the first target detection function.

[0081] S44, calculating the reliability of the target fire detector according to the K difference degrees.

[0082] Among them, the preset time points corresponding to the initial detection values ​​used when fitting each second target detection function are used to form a time point range as the basis for evaluating the degree of difference between each second target detection function and the first target detection function, which can improve the accuracy of the evaluation results.

[0083] For preset time points within the time point range, the output values ​​of the first target detection function and each second target detection function are calculated respectively, and then the difference between the output values ​​is calculated, such as the mean absolute error, mean square error and other features between the two sets of output values, to characterize the degree of difference between each second target detection function and the first target detection function within the time point range.

[0084] The reliability level corresponding to the target fire detector is calculated based on the K difference levels. For example, the average value of the K difference levels is used as the reliability level of the target fire detector, or a weight is assigned to each second target detection function based on factors such as the importance or accuracy of each second target detection function, so as to obtain the reliability level by taking a weighted average of the K difference levels, thereby improving the calculation accuracy of the reliability level.

[0085] As described above, evaluating the reliability by combining the first target detection function and the second target detection function is equivalent to comparing and analyzing the detection results of the target fire detector from multiple dimensions, both overall and local, thereby avoiding misjudgment that may be caused by a single data or a single perspective, thereby more accurately judging whether the target fire detector is working reliably and improving the accuracy of subsequent status detection of the fire detector.

[0086] S5, obtaining a number of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function.

[0087] Among them, when the environmental state is normal, the second reference detection value curve is consistent with the first reference detection value curve. When the environmental state is a fire state, the second reference detection value curve is a curve drawn based on the historical detection data corresponding to the environmental state of the location where the target fire detector is located when the environmental state is a fire state. These historical detection data record the changes in the fire detection values ​​of the location at different times. For example, the fluctuation range of parameters such as temperature, humidity, and smoke concentration at different times of the day can be obtained by statistically analyzing and fitting a large amount of historical data, and the second reference detection value curve can be used as the basis for screening abnormal detection values ​​in the target fire detector.

[0088] Optionally, according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function, a number of abnormal detection values ​​corresponding to the target fire detector are obtained, including: S51, if the environmental state is normal, then according to the reliability and the second reference detection value curve, a first fluctuation detection value curve and a second fluctuation detection value curve are obtained, wherein the detection value corresponding to each preset time point in the first fluctuation detection value curve is greater than the detection value corresponding to the second reference detection value curve, and the detection value corresponding to each preset time point in the second fluctuation detection value curve is less than the detection value corresponding to the second reference detection value curve.

[0089] S52: For any initial detection value, if the current initial detection value is not within the range corresponding to the first fluctuation detection value curve and the second fluctuation detection value curve, the current initial detection value is determined as an abnormal detection value.

[0090] S53, traverse N initial detection values ​​to obtain all abnormal detection values.

[0091] Among them, the second reference detection value curve represents the ideal situation of the detection value under normal conditions. The first fluctuation detection value curve and the second fluctuation detection value curve are based on the second reference detection value curve, and a range boundary of normal fluctuation is determined according to the reliability, which serves as the threshold basis for screening out abnormal detection values ​​from the initial detection values.

[0092] Under normal conditions, the initial detection value should be within a reasonable fluctuation range determined by the reliability and the second reference detection value curve. If the initial detection value is greater than the value of the first fluctuation detection value curve at the corresponding preset time point, or is less than the value of the second fluctuation detection value curve at the corresponding preset time point, that is, it is not within the range defined by the two curves, it means that the target fire detector is likely to have a fault or is subject to abnormal conditions such as sudden interference in the environment, and the initial detection value is judged to be an abnormal detection value.

[0093] As mentioned above, by combining the reliability and the second reference detection value curve to determine the first fluctuation detection value curve and the second fluctuation detection value curve, it can better adapt to the natural fluctuation of the detection value under normal environmental conditions, thereby more accurately identifying abnormal detection values ​​and improving the accuracy of subsequent status detection of fire detectors.

[0094] Optionally, obtaining a plurality of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function also includes: S54, if the environmental state is a fire state, a reference gradient set corresponding to the second reference detection value curve and a third gradient set corresponding to the first target detection function are obtained, wherein the reference gradient set includes reference gradient values ​​corresponding to N preset time points, and the third gradient set includes third gradient values ​​corresponding to N preset time points.

[0095] S55. According to the reliability and the reference gradient set, a first fluctuation gradient set and a second fluctuation gradient set are obtained, wherein the first fluctuation gradient set includes first fluctuation gradient values ​​corresponding to N preset time points, and the second fluctuation gradient set includes second fluctuation gradient values ​​corresponding to N preset time points, the first fluctuation gradient value corresponding to each preset time point is greater than the corresponding reference gradient value, and the second fluctuation gradient value corresponding to each preset time point is less than the corresponding reference gradient value.

[0096] S56, for the third gradient value corresponding to any preset time point, if the third gradient value corresponding to the current preset time point is not within the range of the first fluctuation gradient value and the second fluctuation gradient value corresponding to the preset time point, the initial detection value corresponding to the current preset time point is determined as the abnormal detection value.

[0097] S57, traverse N preset time points to obtain all abnormal detection values.

[0098] Among them, the reference gradient set corresponding to the second reference detection value curve represents the ideal change trend of the detection value under the fire state. The first fluctuation gradient set and the second fluctuation gradient set are based on the reference gradient set, and a range boundary of normal fluctuation is determined according to the reliability, which serves as the threshold basis for screening out abnormal detection values ​​from the initial detection values.

[0099] Under a fire condition, the third gradient value corresponding to the preset time point should be within a reasonable fluctuation range determined according to the reliability and the reference gradient set. If the third gradient value corresponding to the preset time point is greater than the corresponding first fluctuation gradient value, or is less than the corresponding second fluctuation gradient value, that is, it is not within the defined gradient value range, it means that the target fire detector is likely to have a fault or is subject to abnormal conditions such as sudden interference in the environment. In this case, the initial detection value corresponding to the preset time point is determined to be an abnormal detection value.

[0100] As mentioned above, by combining the reliability and the reference gradient set corresponding to the second reference detection value curve to determine the first fluctuation gradient set and the second fluctuation gradient set, it can better adapt to the natural change trend of the detection value under the fire environment state, thereby more accurately identifying abnormal detection values ​​and improving the accuracy of subsequent status detection of fire detectors.

[0101] S6, obtaining the detection status corresponding to the target fire detector according to all abnormal detection values.

[0102] Among them, the number, distribution, frequency of occurrence of all abnormal detection values ​​are analyzed. For example, if the number of abnormal detection values ​​is small and the distribution is relatively scattered, it may mean that the detector is occasionally disturbed, but the overall working state is still relatively stable. If the number of abnormal detection values ​​is large and they appear in a certain period of time, it is likely that the detector has a serious fault.

[0103] The detection status can also be further judged by combining the degree to which the abnormal detection value deviates from the normal range. If the abnormal detection value deviates only slightly from the normal range, it may indicate that the detector is in a slightly abnormal state and requires further observation. If the abnormal detection value deviates seriously from the normal range, it may indicate that the detector is no longer working properly and needs to be repaired or replaced immediately.

[0104] Therefore, by analyzing the number, distribution, frequency of occurrence and other aspects of all abnormal detection values, the detection status corresponding to the target fire detector is finally determined, where the detection status can include different status categories such as normal, slight abnormality, serious abnormality, failure, etc., so as to perform preventive maintenance or replacement of the fire detector to avoid the problem from developing to the extent that seriously affects the normal operation of the fire detector, thereby improving the reliability and stability of the fire monitoring system.

[0105] The embodiment of the present invention selects a preset fitting method through the environmental state to fit N initial detection values ​​and K initial detection value sets respectively, obtains a first target detection function to perform overall feature analysis on the detection data, and obtains a second target detection function to perform local feature analysis on the detection data, which can better adapt to the data characteristics under different dimensions, and the targeted fitting method can more accurately describe the data change trend, and improve the accuracy of the characterization of the working state of the target fire detector; the first target detection function and multiple second target detection functions are used to evaluate the reliability of the target fire detector, and the reliability is comprehensively considered from multiple perspectives of the whole and the local. The limitations of the single function evaluation are avoided by mutual verification of multiple functions, and the accuracy of the reliability evaluation is improved; the abnormal detection value is determined by combining multiple factors such as the reliability, the second reference detection value curve corresponding to the environmental state, the initial detection value and the first target detection function, and the initial detection value is constrained and judged from different factors and aspects, thereby improving the screening accuracy of the abnormal detection value, and being able to more comprehensively identify the real abnormal situation; finally, the detection state of the target fire detector is determined according to all abnormal detection values, thereby improving the accuracy of the state detection result of the target fire detector, and providing clear guidance for the operation and maintenance of the target fire detector.

[0106] Corresponding to the state detection method of the fire detector in the above embodiment, Figure 3A structural block diagram of a state detection device for a fire detector provided in the second embodiment of the present invention is given. For the convenience of description, only the parts related to the embodiment of the present invention are shown.

[0107] See also Figure 3 , the state detection device of the fire detector comprises: The data acquisition module 31 is used to obtain N initial detection values ​​corresponding to the target fire detector at N preset time points, and divide the N initial detection values ​​into K initial detection value sets, where N and K are both integers greater than 1.

[0108] The environmental state judgment module 32 is used to obtain the environmental state corresponding to the location of the target fire detector according to the first reference detection value curve and N initial detection values ​​corresponding to the location of the target fire detector, wherein the environmental state is a normal state or a fire state.

[0109] The detection function fitting module 33 is used to fit the N initial detection values ​​and the K initial detection value sets respectively according to the preset fitting method corresponding to the environmental state, so as to obtain the first target detection function corresponding to the N initial detection values ​​of the target fire detector, and the second target detection function corresponding to each initial detection value set.

[0110] The reliability analysis module 34 is used to obtain the reliability corresponding to the target fire detector according to the first target detection function and the K second target detection functions.

[0111] The abnormal detection value screening module 35 is used to obtain a number of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function.

[0112] The detection status acquisition module 36 is used to obtain the detection status corresponding to the target fire detector according to all abnormal detection values.

[0113] Optionally, the detection function fitting module 33 includes: The first function fitting submodule is used to fit N initial detection values ​​with an m-order polynomial to obtain a first reference detection function corresponding to the target fire detector and a first gradient set corresponding to the first reference detection function, wherein m is an integer greater than 1, and the first gradient set includes first gradient values ​​corresponding to N preset time points.

[0114] The first function determination submodule is used to update m=m-1, and return to execute the first function fitting submodule until the first gradient set satisfies the first preset condition corresponding to the environmental state or the updated m satisfies the second preset condition, and the first reference detection function corresponding to the condition is determined as the first target detection function corresponding to the target fire detector, and the number corresponding to the first target detection function is determined as the reference number c, where c is an integer greater than 0.

[0115] The second function fitting submodule is used to fit the current initial detection value set with a c-order polynomial for any initial detection value set, to obtain a second reference detection function corresponding to the current initial detection value set and a second gradient set corresponding to the second reference detection function of the target fire detector, wherein the number of gradient values ​​in the second gradient set corresponding to the current initial detection value set is consistent with the number of initial detection values ​​in the current initial detection value set.

[0116] The second function determination submodule is used to update c=c-1, return to execute the second function fitting submodule until the second gradient set meets the first preset condition corresponding to the environmental state or the updated c meets the second preset condition, and the corresponding second reference detection function when the condition is met is determined as the second target detection function corresponding to the target fire detector for the current initial detection value set.

[0117] The set traversal submodule is used to traverse all initial detection value sets to obtain the second target detection function corresponding to each initial detection value set of the target fire detector.

[0118] Optionally, in the first function determination submodule, the first preset condition includes: If the environment state is normal, the first gradient set satisfies the first preset condition corresponding to the environment state, which means that there is a first gradient value in the first gradient set that is less than the first preset gradient value or greater than the second preset gradient value; If the environmental state is a fire state, the first gradient set satisfies the first preset condition corresponding to the environmental state, which means that there is a first gradient value in the first gradient set that is less than a third preset gradient value; The second precondition includes: The updated m is smaller than the preset polynomial degree.

[0119] Optionally, the reliability analysis module 34 includes: The time point range acquisition submodule is used to obtain the time point range corresponding to the current second target detection function according to the preset time points corresponding to the initial detection value set corresponding to the current second target detection function for any second target detection function.

[0120] The difference degree calculation submodule is used to calculate the difference degree between the current second target detection function and the first target detection function within a time point range.

[0121] The function traversal submodule is used to traverse K second target detection functions to obtain the degree of difference between each second target detection function and the first target detection function.

[0122] The reliability calculation submodule is used to calculate the reliability corresponding to the target fire detector according to K difference degrees.

[0123] Optionally, the abnormal detection value screening module 35 includes: The fluctuation curve acquisition submodule is used to obtain a first fluctuation detection value curve and a second fluctuation detection value curve according to the reliability and the second reference detection value curve if the environmental state is normal, wherein the detection value corresponding to each preset time point in the first fluctuation detection value curve is greater than the detection value corresponding to the second reference detection value curve, and the detection value corresponding to each preset time point in the second fluctuation detection value curve is less than the detection value corresponding to the second reference detection value curve.

[0124] The first abnormal value screening submodule is used to determine, for any initial detection value, the current initial detection value as an abnormal detection value if the current initial detection value is not within the range corresponding to the first fluctuation detection value curve and the second fluctuation detection value curve.

[0125] The initial detection value traversal submodule is used to traverse N initial detection values ​​to obtain all abnormal detection values.

[0126] Optionally, the abnormal detection value screening module 35 includes: The gradient set acquisition submodule is used to obtain a reference gradient set corresponding to the second reference detection value curve and a third gradient set corresponding to the first target detection function if the environmental state is a fire state, wherein the reference gradient set includes reference gradient values ​​corresponding to N preset time points, and the third gradient set includes third gradient values ​​corresponding to N preset time points.

[0127] The fluctuation gradient value acquisition submodule is used to obtain a first fluctuation gradient set and a second fluctuation gradient set according to the reliability and the reference gradient set, wherein the first fluctuation gradient set includes first fluctuation gradient values ​​corresponding to N preset time points, and the second fluctuation gradient set includes second fluctuation gradient values ​​corresponding to N preset time points, and the first fluctuation gradient value corresponding to each preset time point is greater than the corresponding reference gradient value, and the second fluctuation gradient value corresponding to each preset time point is less than the corresponding reference gradient value.

[0128] The second abnormal value screening submodule is used to determine the initial detection value corresponding to the current preset time point as the abnormal detection value for the third gradient value corresponding to any preset time point if the third gradient value corresponding to the current preset time point is not within the range of the first fluctuation gradient value and the second fluctuation gradient value corresponding to the preset time point.

[0129] The preset time point traversal submodule is used to traverse N preset time points to obtain all abnormal detection values.

[0130] Optionally, the data acquisition module 31 includes the following steps: The data acquisition submodule is used to obtain the identification information of the main control module, the drive module, the detection module and the target fire detector, wherein the drive module and the target fire detector are respectively connected to the loop bus.

[0131] The test information output submodule is used to control the driving module to output test information to the loop bus during a first preset period through the main control module, wherein the test information includes identification information of the target fire detector.

[0132] The response information acquisition submodule is used to acquire the response information of the target fire detector from the loop bus in the second preset time period through the detection module, wherein the response information includes N initial detection values ​​corresponding to the target fire detector at N preset time points.

[0133] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0134] Figure 4 This is a schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned fire detector status detection method embodiments are implemented.

[0135] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0136] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0137] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0138] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0139] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0140] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0141] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0142] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for detecting the state of a fire detector, characterized in that: The state detection method of the fire detector comprises: Obtaining N initial detection values ​​corresponding to the target fire detector at N preset time points, and dividing the N initial detection values ​​into K initial detection value sets, where N and K are both integers greater than 1; According to the first reference detection value curve corresponding to the location of the target fire detector and the N initial detection values, an environmental state corresponding to the location of the target fire detector is obtained, wherein the environmental state is a normal state or a fire state; Fitting the N initial detection values ​​and the K initial detection value sets respectively according to a preset fitting method corresponding to the environmental state to obtain a first target detection function corresponding to the N initial detection values ​​of the target fire detector and a second target detection function corresponding to each initial detection value set; Obtaining a reliability level corresponding to the target fire detector according to the first target detection function and the K second target detection functions; Obtaining a number of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function; According to all abnormal detection values, the detection status corresponding to the target fire detector is obtained.

2. The method for detecting the state of a fire detector according to claim 1, characterized in that: The N initial detection values ​​and the K initial detection value sets are fitted respectively according to the preset fitting method corresponding to the environmental state to obtain a first target detection function corresponding to the N initial detection values ​​of the target fire detector and a second target detection function corresponding to each initial detection value set, including: Fitting the N initial detection values ​​with an m-order polynomial to obtain a first reference detection function corresponding to the target fire detector and a first gradient set corresponding to the first reference detection function, wherein m is an integer greater than 1, and the first gradient set includes first gradient values ​​corresponding to N preset time points; Update m=m-1, return to the step of fitting the N initial detection values ​​with an m-order polynomial to obtain a first reference detection function corresponding to the target fire detector and a first gradient set corresponding to the first reference detection function, until the first gradient set satisfies the first preset condition corresponding to the environmental state or the updated m satisfies the second preset condition, determine the first reference detection function corresponding to the condition as the first target detection function corresponding to the target fire detector, and determine the number corresponding to the first target detection function as the reference number c, where c is an integer greater than 0; For any initial detection value set, a c-order polynomial is used to fit the current initial detection value set to obtain a second reference detection function corresponding to the current initial detection value set and a second gradient set corresponding to the second reference detection function of the target fire detector, wherein the number of gradient values ​​in the second gradient set corresponding to the current initial detection value set is consistent with the number of initial detection values ​​in the current initial detection value set; Update c=c-1, return to the step of fitting the current initial detection value set with the c-order polynomial to obtain the second reference detection function corresponding to the target fire detector for the current initial detection value set and the second gradient set corresponding to the second reference detection function, until the second gradient set satisfies the first preset condition corresponding to the environmental state or the updated c satisfies the second preset condition, and the second reference detection function corresponding to the condition being met is determined as the second target detection function corresponding to the target fire detector for the current initial detection value set; All initial detection value sets are traversed to obtain a second target detection function corresponding to each initial detection value set of the target fire detector.

3. The method for detecting the state of a fire detector according to claim 2, characterized in that: The first preset condition includes: If the environmental state is a normal state, the first gradient set satisfies the first preset condition corresponding to the environmental state means that there is a first gradient value in the first gradient set that is less than a first preset gradient value or greater than a second preset gradient value; If the environmental state is a fire state, the first gradient set satisfies the first preset condition corresponding to the environmental state, which means that there is a first gradient value in the first gradient set that is less than a third preset gradient value; The second preset condition includes: The updated m is smaller than the preset polynomial degree.

4. The method for detecting the state of a fire detector according to claim 1, characterized in that: The step of obtaining the reliability corresponding to the target fire detector according to the first target detection function and the K second target detection functions includes: For any second target detection function, according to the preset time points corresponding to the initial detection value set corresponding to the current second target detection function, a time point range corresponding to the current second target detection function is obtained; Calculate the difference between the current second target detection function and the first target detection function within the time point range; Traversing K second target detection functions, and obtaining the degree of difference between each second target detection function and the first target detection function; The reliability level corresponding to the target fire detector is calculated based on the K difference levels.

5. The method for detecting the state of a fire detector according to claim 1, characterized in that: The step of obtaining a plurality of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function comprises: If the environmental state is a normal state, a first fluctuation detection value curve and a second fluctuation detection value curve are obtained according to the reliability and the second reference detection value curve, wherein the detection value corresponding to each preset time point in the first fluctuation detection value curve is greater than the detection value corresponding to the second reference detection value curve, and the detection value corresponding to each preset time point in the second fluctuation detection value curve is less than the detection value corresponding to the second reference detection value curve; For any initial detection value, if the current initial detection value is not within the range corresponding to the first fluctuation detection value curve and the second fluctuation detection value curve, the current initial detection value is determined as an abnormal detection value; Traverse N initial detection values ​​to obtain all abnormal detection values.

6. The method for detecting the state of a fire detector according to claim 1, characterized in that: The method of obtaining a plurality of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function further includes: If the environmental state is a fire state, a reference gradient set corresponding to the second reference detection value curve and a third gradient set corresponding to the first target detection function are obtained, wherein the reference gradient set includes reference gradient values ​​corresponding to N preset time points, and the third gradient set includes third gradient values ​​corresponding to N preset time points; According to the reliability and the reference gradient set, a first fluctuation gradient set and a second fluctuation gradient set are obtained, wherein the first fluctuation gradient set includes first fluctuation gradient values ​​corresponding to N preset time points, and the second fluctuation gradient set includes second fluctuation gradient values ​​corresponding to N preset time points, and the first fluctuation gradient value corresponding to each preset time point is greater than the corresponding reference gradient value, and the second fluctuation gradient value corresponding to each preset time point is less than the corresponding reference gradient value; For the third gradient value corresponding to any preset time point, if the third gradient value corresponding to the current preset time point is not within the range of the first fluctuation gradient value and the second fluctuation gradient value corresponding to the preset time point, the initial detection value corresponding to the current preset time point is determined as the abnormal detection value; Traverse N preset time points to obtain all anomaly detection values.

7. The method for detecting the state of a fire detector according to claim 1, characterized in that: The obtaining of N initial detection values ​​corresponding to the target fire detector at N preset time points, and dividing the N initial detection values ​​into K initial detection value sets, includes: Obtaining identification information of a main control module, a driving module, a detection module and the target fire detector, wherein the driving module and the target fire detector are respectively connected to a loop bus; Controlling the driving module to output test information to the loop bus during a first preset period of time through the main control module, wherein the test information includes identification information of the target fire detector; The detection module obtains response information of the target fire detector from the loop bus in a second preset time period, wherein the response information includes N initial detection values ​​corresponding to the target fire detector at N preset time points.

8. A fire detector status detection device, characterized in that: The state detection device of the fire detector comprises: A data acquisition module, used to acquire N initial detection values ​​corresponding to the target fire detector at N preset time points, and divide the N initial detection values ​​into K initial detection value sets, wherein N and K are both integers greater than 1; An environmental state judgment module, used to obtain the environmental state corresponding to the location of the target fire detector according to the first reference detection value curve corresponding to the location of the target fire detector and the N initial detection values, wherein the environmental state is a normal state or a fire state; a detection function fitting module, used to fit the N initial detection values ​​and the K initial detection value sets respectively according to a preset fitting method corresponding to the environmental state, to obtain a first target detection function corresponding to the N initial detection values ​​of the target fire detector, and a second target detection function corresponding to each initial detection value set; A reliability analysis module, used for obtaining the reliability corresponding to the target fire detector according to the first target detection function and K second target detection functions; an abnormal detection value screening module, used to obtain a number of abnormal detection values ​​corresponding to the target fire detector according to the reliability, the second reference detection value curve corresponding to the environmental state, the N initial detection values ​​and the first target detection function; The detection status acquisition module is used to obtain the detection status corresponding to the target fire detector according to all abnormal detection values.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for detecting the state of a fire detector according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for detecting the state of a fire detector according to any one of claims 1 to 7 is implemented.

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