Probe calibration method, calibration device, battery, vehicle and storage medium

Abstract

The application relates to a detector calibration method, a calibration device, a battery, a vehicle and a storage medium. The method comprises the following steps: collecting N particulate matter concentration information in a current environment in a preset period; determining a concentration change trend according to the N particulate matter concentration information; determining a noise value based on the concentration change trend and the N particulate matter concentration information, so that the detector can be accurately calibrated according to the noise value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of detectors, in particular to a detector calibration method, a calibration device, a battery, a vehicle and a storage medium. BACKGROUND

[0002] A smoke detector is a detection device for fire prevention and explosion prevention by monitoring smoke or particulate matter concentration. Most of the smoke detectors on the market currently use photoelectric smoke detectors. The photoelectric smoke detector is internally composed of a light emitting diode, a light receiving diode and other structures. Under normal circumstances, only a small amount of light emitted by the light emitting diode can be received by the light receiving diode. When smoke enters the photoelectric smoke detector, the light emitted by the light emitting diode will be affected by the scattering and absorption of aerosols such as smoke, causing diffuse reflection phenomenon, so that the amount of light received by the light receiving diode increases. When the smoke concentration reaches a certain concentration, the photoelectric smoke detector will issue an alarm signal.

[0003] The current photoelectric smoke detector will have false alarm or missed alarm problems after being used for a period of time, which will directly affect the stability and accuracy of the detector function. The reason for false alarm or missed alarm is generally that the sensitivity of the smoke detector to smoke particles has changed. The sensitivity of the smoke detector will be affected by the attachment of smoke dust in its use environment, but there is no solution to the problem of smoke dust attachment at present. SUMMARY

[0004] Therefore, it is necessary to provide a detector calibration method, a calibration device, a battery, a vehicle and a storage medium in view of the above technical problems.

[0005] In a first aspect, a detector calibration method is provided, and the method comprises:

[0006] Collecting N particulate matter concentration information in a current environment within a preset period of time; wherein N is a positive integer;

[0007] Determining a concentration change trend according to the N particulate matter concentration information;

[0008] Determining a noise value based on the concentration change trend and the N particulate matter concentration information;

[0009] Calibrating the detector based on the noise value.

[0010] Optionally, the concentration change trend is a growth linear function.

[0011] Optionally, the concentration information includes collection time and concentration value.

[0012] The concentration change trend is determined according to the N particle concentration information, and the concentration change trend comprises:

[0013] The slope k of the growth straight line function and the vertical intercept m of the growth straight line function are calculated according to the collection time and the concentration value;

[0014] The growth straight line function is constructed according to the slope k and the vertical intercept m, and the growth straight line function is C Ti =k*Ti+m; wherein, C Ti is a particle concentration value.

[0015] Optionally, the noise value is determined based on the concentration change trend and the N particle concentration information, and the noise value comprises:

[0016] It is determined whether the N particle concentration information satisfies a preset distribution state based on the growth straight line function and the N particle concentration information.

[0017] When the preset distribution state is satisfied, the noise value is determined according to the N particle concentration values.

[0018] Optionally, it is determined whether the N particle concentration information satisfies a preset distribution state based on the growth straight line function and the N particle concentration information, and the method comprises:

[0019] It is determined that each particle concentration value C Ti corresponds to a collection time Ti.

[0020] It is determined that a function value X Ti of the growth straight line function at the collection time Ti.

[0021] The concentration mean square deviation and the concentration average value of N concentration values C Ti are obtained, and the time mean square deviation and the time average value of N collection times Ti are obtained.

[0022] It is determined whether the N particle concentration information satisfies a preset distribution state according to the concentration mean square deviation, the concentration average value, the time mean square deviation and the time average value.

[0023] Optionally, it is determined whether the N particle concentration information satisfies a preset distribution state according to the concentration mean square deviation, the concentration average value, the time mean square deviation and the time average value, and the method comprises:

[0024] A correlation coefficient of a concentration value C Ti and a collection time Ti is determined according to the concentration mean square deviation, the concentration average value, the time mean square deviation and the time average value.

[0025] According to the correlation coefficient, it is determined whether the N particulate matter concentration information meets a preset distribution state.

[0026] Optionally, the noise value is determined according to the N particulate matter concentration values, including:

[0027] The mean of the N particulate matter concentration values is taken as the noise value.

[0028] In a second aspect, a detector calibration device is provided, and the detector calibration device includes:

[0029] A collection module is configured to collect N particulate matter concentration information in a current environment within a preset time period, where N is a positive integer.

[0030] A trend determination module is configured to determine a concentration change trend according to the N particulate matter concentration information.

[0031] A noise value determination module is configured to determine a noise value based on the concentration change trend and the N particulate matter concentration information.

[0032] A calibration module is configured to calibrate the detector based on the noise value.

[0033] In a third aspect, a battery is provided, and the battery includes the detector calibration device of the second aspect.

[0034] In a fourth aspect, a vehicle is provided, and the vehicle includes the battery of the third aspect.

[0035] In a fifth aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the method of the first aspect.

[0036] In a sixth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method of the first aspect.

[0037] The detector calibration method, device, computer device and storage medium, the method comprises: collecting N particulate matter concentration information in a current environment in a preset period, determining a concentration change trend according to the N particulate matter concentration information, determining a noise value based on the concentration change trend and the N particulate matter concentration information, and calibrating the detector based on the noise value. Wherein, the concentration change trend is determined according to the collected particulate matter concentration information, and the concentration change trend is caused by the attachment of the use environment dust, so that the noise value containing the noise value caused by the dust can be determined according to the concentration change trend and the particulate matter concentration information, so that the detector can be accurately calibrated according to the noise value, and the noise value is removed when the smoke (aerosol) concentration is calculated. In the long-term operation process of the detector, the noise will continue to change, and the continuously changing noise can be removed through the above scheme, so as to realize self-calibration of reducing manual operation and maintenance, guarantee longer and reliable operation and service life of the detector, and greatly reduce the operation and maintenance investment. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A flowchart of a detector calibration method in an embodiment is shown;

[0039] Figure 2 A particulate matter concentration distribution state diagram in an embodiment is shown;

[0040] Figure 3 A structural block diagram of a detector calibration device in an embodiment is shown;

[0041] Figure 4 An internal structure diagram of a computer device in an embodiment is shown. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0043] In an embodiment, as shown in Figure 1 A detector calibration method is provided, comprising the following steps:

[0044] Step S101: collecting N particulate matter concentration information in a current environment in a preset period; wherein N is a positive integer;

[0045] Wherein, the particulate matter concentration information in the current environment of the detector is collected every preset period, for example, the particulate matter concentration information in the current environment of the detector is collected at 18:00 every day, and a total of 15 days are collected, and 15 particulate matter concentration information can be obtained.

[0046] Step S102, determining a concentration change trend according to the N particle concentration information;

[0047] Optionally, the concentration change trend is a growth linear function.

[0048] The N particle concentration information is calculated to determine a slowly growing linear function, and the growing linear function is taken as the concentration change trend. The growing linear function is a linearly related regression straight line.

[0049] Step S103, determining a noise value based on the concentration change trend and the N particle concentration information;

[0050] The relationship between the N particle concentration information and the growing linear function is analyzed to determine the noise value.

[0051] Step S104, calibrating the detector based on the noise value.

[0052] In the embodiment of the present application, N particle concentration information in the current environment in a preset period is collected; a concentration change trend is determined according to the N particle concentration information; a noise value is determined based on the concentration change trend and the N particle concentration information; and the detector is calibrated based on the noise value. The concentration change trend is determined according to the collected particle concentration information, and the concentration change trend is caused by the attachment of the smoke dust in the use environment. Therefore, the noise value caused by the smoke dust can be determined according to the concentration change trend and the particle concentration information, so that the detector can be accurately calibrated according to the noise value, and the noise value is removed when the smoke (aerosol) concentration is calculated. During the long-term operation of the detector, the noise will continue to change, and the continuously changing noise can be removed by the above scheme, so as to realize self-calibration of reducing manual operation and maintenance, guarantee longer and reliable operation and service life of the detector, and greatly reduce the operation and maintenance investment.

[0053] Optionally, one embodiment, the concentration information includes collection time and concentration value;

[0054] The concentration change trend is determined according to the N particle concentration information, including:

[0055] The slope k of the growing linear function and the vertical intercept m of the growing linear function are calculated according to the collection time and the concentration value;

[0056] The growing linear function is constructed as C Ti = k * Ti + m; wherein, C Ti is the particle concentration value.

[0057] Wherein, the concentration information (T, C) comprises the collection time T and the concentration value C. The collection time T and the concentration value C are two variables of linear correlation.

[0058] As described above, 15 concentration information are collected, T = Ti, i is a positive integer from 1 to 15. The concentration value collected at Ti is C Ti .

[0059] According to the collection time and the concentration value, the slope k of the growth straight line function and the vertical intercept m of the growth straight line function are calculated; and the growth straight line function is constructed according to the slope k and the vertical intercept m as C Ti = k * Ti + m.

[0060] Wherein, the slope k = r * σ C / σ T , and the vertical intercept m = μ C - a * μ T . Wherein, σ C is the mean square deviation of C1, C2, …, C 15 , σ T is the mean square deviation of T1, T2, …, T15. μ C is the average value of C1, C2, …, C 15 , μ T is the average value of T1, T2, …, T15, and r is the correlation coefficient of the concentration value C Ti and the collection time Ti. Wherein, the calculation methods of the mean square deviation, the average value and the correlation coefficient belong to the known technology in the art, and are not described herein.

[0061] Wherein, it is to be noted that, without considering specific scenarios and objects, the regression straight line of linear correlation is determined according to two variables of linear correlation, which belongs to the existing algorithm. However, the algorithm is not used for calibration in the technical field of detector calibration.

[0062] For the convenience of understanding, an example is described as follows, assuming that n concentration information are collected, which are respectively:

[0063] (x1, y1), (x2, y2), (x3, y3), …, (x n , y n ) Wherein, x represents the collection time, y represents the concentration value; x and y are two variables of linear correlation;

[0064] Let the regression equation (i.e. the growth straight line function) to be solved be (i = 1, 2, 3, …, n). Obviously, the signs of each deviation above are positive and negative, and if they are added, they will cancel out part of each other, so their sum cannot represent the overall closeness of a point to the regression line. Therefore, the sum of the squares of each deviation is used to represent the overall closeness of a point to the corresponding line (regression line), i.e.:

[0065]

[0066] When the minimum value is taken, the values of a and b are obtained, and the regression equation is obtained. Specifically:

[0067] First, two formulas used in the transformation need to be proved.

[0068] Formula (1) where,

[0069] Proof:

[0070]

[0071]

[0072] Formula (2)

[0073] Proof:

[0074]

[0075]

[0076] After that, the derivation is carried out. Each term of the expression of is expanded first, and then combined and transformed.

[0077]

[0078]

[0079] In the above formula, there are four terms, and the last two terms are independent of a and b, which are constants; the first two terms are the sum of two non-negative numbers, so in order to make Q take the minimum value, the values of the first two terms must be 0. Therefore:

[0080]

[0081] Through the above method, a and b can be obtained, that is, the slope k and the vertical intercept m are obtained. b is the slope k, and a is the vertical intercept m.

[0082] Optionally, in an embodiment, the determining the noise value based on the concentration change trend and the N particle concentration information comprises:

[0083] determine whether the N particulate concentration information satisfies a preset distribution state based on the growth linear function and the N particulate concentration information;

[0084] When the preset distribution state is satisfied, determine the noise value according to the N particulate concentration values.

[0085] In the embodiment of the present application, the growth linear function is obtained by the N particulate concentration values. It is analyzed whether the N particulate concentration values are randomly and discretely distributed around the growth linear function. If the N particulate concentration values are randomly and discretely distributed around the growth linear function (as shown in the figure), it is determined that the preset distribution state is satisfied, that is, the N particulate concentration values are randomly distributed, which conforms to the law of the attachment of environmental smoke dust. Therefore, the noise value is determined according to the N particulate concentration values. Figure 2

[0086] Alternatively, if the N particulate concentration values are randomly and discretely distributed around the growth linear function, it is determined that the preset distribution state is satisfied, and the average of the N particulate concentration values is taken as the noise value. The noise value changes every day, and the change is very small. Every day, the detector will correct the detected concentration value according to the noise value calculated on that day, and remove the noise value when calculating the smoke (aerosol) concentration. In the long-term operation and use process of the detector, the noise will continue to change, and through the above scheme, the continuously changing noise can be removed, thereby realizing self-calibration of reducing manual operation, ensuring longer and reliable operation and service life of the detector, and greatly reducing operation and maintenance investment.

[0087] In an alternative embodiment, the determination of whether the N particulate concentration information satisfies a preset distribution state based on the growth linear function and the N particulate concentration information comprises:

[0088] determining each particulate concentration value C Ti corresponding to the collection time Ti;

[0089] determining the function value X Ti of the growth linear function at the collection time Ti;

[0090] obtaining the concentration mean square deviation and the concentration average of the N concentration values C Ti , and obtaining the time mean square deviation and the time average of the N collection times Ti;

[0091] determining whether the N particulate concentration information satisfies a preset distribution state based on the concentration mean square deviation, the concentration average, the time mean square deviation, and the time average.

[0092] ​Further, according to the concentration mean square deviation, the concentration average value, the time mean square deviation and the time average value, a concentration value C is determined Ti and a correlation coefficient of the collection time Ti; according to the correlation coefficient, it is determined whether the N particle concentration information meets a preset distribution state.

[0093] In the embodiment of the present application, the correlation coefficient r of the concentration value of the N particles and the collection time is calculated, if r is less than a preset threshold value (for example, 0.1), it can be considered that the concentration value of the N particles and the time have no relationship, and are randomly distributed, that is, it is determined that the N particle concentration information meets the preset distribution state.

[0094] The above detector calibration method collects N particle concentration information in the current environment within a preset period of time; the concentration information includes collection time and concentration value; according to the N particle concentration information, a growth straight line function is determined. It is analyzed whether the N particle concentration values are randomly and discretely distributed around the growth straight line function, if the N particle concentration values are randomly and discretely distributed around the growth straight line function, it is determined that the preset distribution state is met, that is, the N particle concentration values are randomly distributed, which conforms to the rule of the attachment of environmental smoke dust. The mean value of the N particle concentration values is taken as a noise value, which contains the noise value brought by the smoke dust, so that the detector can be accurately calibrated according to the noise value, and the noise value is removed when calculating the smoke (aerosol) concentration. In the long-term operation and use process of the detector, the noise will continuously change, and the continuously changing noise can be removed through the above scheme, so as to realize self-calibration of reducing manual operation and maintenance, protect the longer and more reliable operation and service life of the detector, and greatly reduce the operation and maintenance investment.

[0095] It should be understood that, although Figure 1 The steps in the flowchart of Figure 1 At least part of the steps in

[0096] In one embodiment, as shown in Figure 3 a detector calibration device is provided, comprising:

[0097] The collection module is configured to collect N particle concentration information in a current environment within a preset time period, wherein N is a positive integer.

[0098] The trend determination module is configured to determine a concentration change trend according to the N particle concentration information.

[0099] The noise value determination module is configured to determine a noise value based on the concentration change trend and the N particle concentration information.

[0100] The calibration module is configured to calibrate the detector based on the noise value.

[0101] In one of the optional embodiments, the concentration information includes a collection time and a concentration value; the trend determination module is configured to calculate a slope k of the growth linear function and a vertical intercept m of the growth linear function according to the collection time and the concentration value; and construct the growth linear function as C Ti = k * Ti + m; wherein C Ti is a particle concentration value.

[0102] In one of the optional embodiments, the noise value determination module is configured to determine whether the N particle concentration information satisfies a preset distribution state based on the growth linear function and the N particle concentration information; and determine the noise value according to the N particle concentration values when the preset distribution state is satisfied.

[0103] In one of the optional embodiments, the noise value determination module is configured to determine a collection time Ti corresponding to each particle concentration value C Ti ; determine a function value X Ti of the growth linear function at the collection time Ti; obtain a concentration mean square deviation and a concentration average value of the N concentration values C Ti ; and obtain a time mean square deviation and a time average value of the N collection times Ti; and determine whether the N particle concentration information satisfies a preset distribution state according to the concentration mean square deviation, the concentration average value, the time mean square deviation and the time average value.

[0104] In one of the optional embodiments, the noise value determination module is configured to determine a correlation coefficient of the concentration value C Ti and the collection time Ti according to the concentration mean square deviation, the concentration average value, the time mean square deviation and the time average value; and determine whether the N particle concentration information satisfies a preset distribution state according to the correlation coefficient.

[0105] In one of the optional embodiments, the noise value determination module is configured to take a mean value of the N particle concentration values as the noise value.

[0106] The specific definitions of the detector calibration device can refer to the definitions of the detector calibration method above, which will not be repeated here. Each module in the above detector calibration device can be implemented by software, hardware, and a combination thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor invokes and executes the operations corresponding to each of the above modules.

[0107] In one embodiment, a battery is provided, which includes the detector calibration device as described in the above embodiments.

[0108] In the embodiments of the present application, the battery includes a battery shell and a detector calibration device, which is placed inside the battery shell.

[0109] In one embodiment, a vehicle is provided, which includes the battery as described in the above embodiments.

[0110] In one embodiment, a computer device is provided, which can be a server, and its internal structure diagram can be as shown in Figure 4 The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store particulate matter concentration information and other related data. The network interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a detector calibration method.

[0111] Those skilled in the art can understand that Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0113] Collect N particulate matter concentration information in the current environment within a preset time period; wherein N is a positive integer;

[0114] According to the N particle concentration information, a concentration change trend is determined;

[0115] Based on the concentration change trend and the N particle concentration information, a noise value is determined;

[0116] The detector is calibrated based on the noise value.

[0117] In one embodiment, the processor executing the computer program also implements the following steps:

[0118] According to the collection time and the concentration value, a slope k of the growth linear function and a vertical intercept m of the growth linear function are calculated;

[0119] According to the slope k and the vertical intercept m, the growth linear function is constructed as C Ti = k * Ti + m; wherein, C Ti is a particle concentration value.

[0120] In one embodiment, the processor executing the computer program also implements the following steps:

[0121] Based on the growth linear function and the N particle concentration information, it is determined whether the N particle concentration information meets a preset distribution state;

[0122] When the preset distribution state is met, the noise value is determined according to the N particle concentration values.

[0123] In one embodiment, the processor executing the computer program also implements the following steps:

[0124] Each particle concentration value C Ti corresponding collection time Ti is determined;

[0125] The function value X Ti of the growth linear function at the collection time Ti is determined.

[0126] The concentration mean square deviation and the concentration average of N concentration values C Ti are obtained, and the time mean square deviation and the time average of N collection times Ti are obtained;

[0127] According to the concentration mean square deviation, the concentration average, the time mean square deviation, and the time average, it is determined whether the N particle concentration information meets a preset distribution state.

[0128] In one embodiment, the processor executing the computer program also implements the following steps:

[0129] According to the concentration mean square deviation, the concentration average, the time mean square deviation, and the time average, a concentration value CTi and a correlation coefficient of the acquisition time Ti;

[0130] According to the correlation coefficient, it is determined whether the N particulate matter concentration information meets a preset distribution state. In one embodiment, the processor executes the computer program to further implement the following steps:

[0131] The mean value of the N particulate matter concentration values is taken as the noise value.

[0132] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the following steps:

[0133] Acquire N particulate matter concentration information in a current environment within a preset period of time; wherein N is a positive integer;

[0134] According to the N particulate matter concentration information, a concentration change trend is determined;

[0135] Based on the concentration change trend and the N particulate matter concentration information, a noise value is determined;

[0136] Based on the noise value, the detector is calibrated.

[0137] In one embodiment, the computer program is executed by the processor to further implement the following steps:

[0138] According to the acquisition time and the concentration value, the slope k of the growth straight line function and the vertical intercept m of the growth straight line function are calculated;

[0139] According to the slope k and the vertical intercept m, the growth straight line function is constructed as C Ti =k*Ti+m; wherein C Ti is a particulate matter concentration value.

[0140] In one embodiment, the computer program is executed by the processor to further implement the following steps:

[0141] Based on the growth straight line function and the N particulate matter concentration information, it is determined whether the N particulate matter concentration information meets a preset distribution state;

[0142] When the preset distribution state is met, according to the N particulate matter concentration values, the noise value is determined.

[0143] In one embodiment, the computer program is executed by the processor to further implement the following steps:

[0144] Determine the acquisition time Ti corresponding to each particulate matter concentration value C Ti ;

[0145] determining a function value X on the growth linear function at the collection time Ti Ti ;

[0146] obtaining a concentration mean square error and a concentration mean value of the N concentration values C Ti , and obtaining a time mean square error and a time mean value of the N collection times Ti;

[0147] determining, according to the concentration mean square error, the concentration mean value, the time mean square error and the time mean value, whether the N particulate matter concentration information satisfies a preset distribution state.

[0148] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0149] determining, according to the concentration mean square error, the concentration mean value, the time mean square error and the time mean value, a correlation coefficient of the concentration value C Ti and the collection time Ti;

[0150] determining, according to the correlation coefficient, whether the N particulate matter concentration information satisfies a preset distribution state. In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0151] taking a mean value of the N particulate matter concentration values as the noise value.

[0152] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to a memory, storage, database or other medium in the embodiments provided by the present application can include at least one of a non-volatile and volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory or an optical memory. The volatile memory can include a random access memory (RAM) or an external cache memory. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM).

[0153] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described above, however, as long as the combination of the technical features does not exist in contradiction, it shall be considered within the scope of the present disclosure.

[0154] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A detector calibration method, applied to a photoelectric smoke detector, characterized in that, The method includes: Collect N particulate matter concentration data points in the current environment within a preset time period; where N is a positive integer; Based on the N particulate matter concentration information, the concentration change trend is determined, wherein the concentration information includes the collection time and concentration value, and the concentration change trend is a linear function of increase; Based on the growth linear function and the N particulate matter concentration information, calculate the correlation coefficient between the concentration values ​​of the N particulate matter and the collection time, and determine whether the N particulate matter concentration information satisfies a random distribution state based on the correlation coefficient; When the random distribution state is satisfied, the noise value is determined based on the N particulate matter concentration values; The detector is calibrated based on the noise value.

2. The detector calibration method according to claim 1, characterized in that, The step of determining the concentration change trend based on the N particulate matter concentration information includes: Based on the collection time and the concentration value, the slope k and the ordinate m of the growth line function are calculated. The growth linear function is constructed based on the slope k and the ordinate m as CTi = k * Ti + m; where CTi is the particulate matter concentration value, and Ti is the acquisition time corresponding to each particulate matter concentration value CTi.

3. The detector calibration method according to claim 1, characterized in that, Based on the growth linear function and the N particulate matter concentration information, the correlation coefficient between the concentration values ​​of the N particulate matter and the collection time is calculated. Based on the correlation coefficient, it is determined whether the N particulate matter concentration information satisfies a random distribution state, including: Determine the acquisition time Ti corresponding to each particulate matter concentration value CTi; Determine the function value XTi of the growth linear function at the acquisition time Ti; Obtain the standard deviation and average concentration of N concentration values ​​CTi, and obtain the standard deviation and average time of N acquisition times Ti; The correlation coefficient between the concentration value CTi and the acquisition time Ti is determined based on the concentration mean square error, the concentration mean square error, the time mean square error, and the time mean square error. When the correlation coefficient is less than a preset threshold, it is determined that the N particulate matter concentration information meets the preset distribution state.

4. The detector calibration method according to claim 1, characterized in that, Determining the noise value based on the N particulate matter concentration values ​​includes: The average of the N particulate matter concentration values ​​is used as the noise value.

5. A detector calibration device, characterized in that, The detector calibration device includes: The data acquisition module is used to collect N particulate matter concentration data in the current environment within a preset time period; where N is a positive integer. The trend determination module is used to determine the concentration change trend based on the N particulate matter concentration information, wherein the concentration information includes the collection time and concentration value, and the concentration change trend is a linear function of increase; The noise value determination module is used to calculate the correlation coefficient between the concentration values ​​of the N particles and the acquisition time based on the growth linear function and the N particle concentration information, and to determine whether the N particle concentration information meets the random distribution state based on the correlation coefficient; if the random distribution state is met, the noise value is determined based on the N particle concentration values. A calibration module is used to calibrate the detector based on the noise value.

6. A battery, characterized in that, The battery includes the detector calibration device as described in claim 5.

7. A vehicle, characterized in that, The vehicle includes the battery as described in claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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