Intelligent store intelligent inspection method

Abstract

The invention discloses an intelligent store inspection method, and relates to the technical field of monitoring analysis, and the method comprises the steps: receiving and executing a to-be-inspected store instruction, confirming a to-be-inspected store, and marking the to-be-inspected store as a target store; obtaining basic information and operation information corresponding to each intelligent device in the target store; processing and analyzing the basic information and the operation information corresponding to each intelligent device in the target store, performing potential safety hazard analysis on the target store according to a processing and analysis result, and formulating an early warning strategy according to a potential safety hazard analysis result; and executing the early warning strategy, and outputting an alarm signal to a worker. The method has the effect of improving the inspection efficiency.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to an intelligent store inspection method. Background Art

[0002] With the advancement of digital equipment management, the status of smart devices in stores is directly related to the quality of products. Unplanned downtime of smart devices can cause equipment damage and other losses, and post-maintenance of equipment also increases the maintenance cost of the equipment.

[0003] In the related technologies, the traditional inspection method of smart devices in stores is mostly to check them one by one manually or to perform maintenance only after the smart devices fail, which causes certain safety hazards. In addition, the manual inspection method is inefficient and there is room for improvement. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present application provides an intelligent store inspection method.

[0005] In the first aspect, the present application provides an intelligent store inspection method, which adopts the following technical solutions:

[0006] An intelligent store inspection method includes the following steps:

[0007] Receive and execute instructions for stores to be inspected, confirm stores to be inspected, and mark stores to be inspected as target stores;

[0008] Obtain basic information and operation information corresponding to each smart device in the target store;

[0009] Process and analyze the basic information and operation information corresponding to each smart device in the target store, analyze the safety hazards of the target store based on the results of the processing and analysis, and formulate early warning strategies based on the results of the safety hazard analysis;

[0010] Execute early warning strategies and output warning signals to staff.

[0011] Preferably, the basic information corresponding to each smart device in the target store includes type information, maintenance information and tag information, and the maintenance information includes the number of maintenance times and the maintenance duration corresponding to each maintenance time;

[0012] The operating information corresponding to each smart device in the target store includes an operating voltage value, an operating current value and an operating time.

[0013] Preferably, the basic information and operation information corresponding to each smart device in the target store are processed and analyzed, specifically including:

[0014] Based on the basic information corresponding to each smart device in the target store, the type information corresponding to each smart device in the target store is confirmed, and then the type influence coefficient μ corresponding to each smart device in the target store is extracted from the cloud database according to the type information corresponding to each smart device in the target store. i , where i represents the number of each smart device in the target store, i = 1, 2, 3.....j;

[0015] Confirm the risk factor m of the target store having potential safety hazards r ;

[0016] Among them, s represents the number of maintenance times, t is It is represented by the maintenance time corresponding to the s-th maintenance of the smart device numbered i in the target store, T i It is represented by the working time of the smart device numbered i in the target store, n i It is represented by the number of times the smart device numbered i in the target store is marked, and e is a natural constant;

[0017] Compare the risk factor m of potential safety hazards in the target store with the preset potential safety hazard risk threshold m′;

[0018] If the target store has a risk factor of safety hazards m≤m′, there is no need to conduct safety hazard analysis on the target store;

[0019] If the risk coefficient of safety hazards in the target store is m>m′, a safety hazard analysis is required for the target store.

[0020] Preferably, a safety hazard analysis is conducted for the target store, specifically including:

[0021] Select a time window, confirm the operation information corresponding to each smart device in the target store, and collect the working voltage value, working current value and working time corresponding to each smart device in the target store in real time according to the operation information;

[0022] The working voltage values ​​corresponding to each smart device in the target store are collected in real time to form a time series, and then the working voltage values ​​collected in real time are converted into a function V based on the time series. i (t) indicates;

[0023] By formula Confirm the voltage evaluation coefficient α corresponding to each smart device in the target store i , where v iIt represents the standard voltage corresponding to each smart device in the target store when it is in working state, [t1, t2] represents the time period when the working voltage value corresponding to each smart device in the target store is greater than the standard voltage, and [t3, t4] represents the time period when the working voltage value corresponding to each smart device in the target store is less than the standard voltage;

[0024] Similarly, the working current values ​​corresponding to each smart device in the target store are collected in real time to form a time series, and then the working current values ​​collected in real time are converted into a function I based on the time series. i (t) represents and confirms the current evaluation coefficient β corresponding to each smart device in the target store i ;

[0025] By formula Confirm the operation evaluation coefficient γ corresponding to the target store, where ω1 and ω2 represent the weight coefficients corresponding to the voltage evaluation coefficient and the current evaluation coefficient, respectively. i ′ represents the allowed working time corresponding to the smart device numbered i in the target store, ΔT represents the difference in allowed working time, and ψ1 and ψ2 represent the preset correlation coefficients respectively;

[0026] Compare the operation evaluation coefficient γ corresponding to the target store with the preset operation evaluation threshold γ′;

[0027] If the operation evaluation coefficient γ ≤ γ′ corresponding to the target store, there is no need to formulate an early warning strategy;

[0028] If the operation evaluation coefficient γ corresponding to the target store is greater than γ′, an early warning strategy needs to be formulated for the target store.

[0029] Preferably, an early warning strategy is formulated for the target stores, specifically including:

[0030]

[0031] The reference evaluation coefficient W of the target store's potential safety hazards is calculated through the above formula. yj ;

[0032] in, They are respectively represented as preset correlation coefficients;

[0033] The reference evaluation coefficient W of the target store with potential safety hazards yj Compare with the preset reference evaluation threshold W′;

[0034] If the target store has safety hazards, the reference assessment coefficient W yj ≤W′, then output the first alarm signal;

[0035] If the target store has safety hazards, the reference assessment coefficient W yj>W′, then the second alarm signal is output and at the same time, the smart devices of the target store are adjusted.

[0036] Preferably, the process of obtaining the tag information corresponding to each smart device in the target store specifically includes:

[0037] The temperature data and vibration data corresponding to each smart device in the target store are collected based on the detection device, and the temperature data change curve U over time is confirmed based on the temperature data and vibration data corresponding to each smart device in the target store. i (t) and the vibration data changing with time curve E i (t);

[0038] A comprehensive judgment is made on the temperature data change curve over time and the vibration data change curve over time corresponding to each smart device in the target store, and each smart device in the target store is marked based on the judgment result.

[0039] Preferably, a comprehensive judgment is made on the temperature value change data corresponding to each smart device in the target store over time, specifically including:

[0040]

[0041] The above formula is used to calculate and confirm the comprehensive evaluation coefficient G corresponding to each smart device in the target store. i ;

[0042] Among them, u i (t), e i (t) respectively represent the preset standard temperature data variation curve over time and the preset standard vibration data variation curve over time corresponding to the smart device numbered i in the target store, u i 、e i They respectively represent the reference temperature data and reference vibration data corresponding to the smart device numbered i in the target store, and ρ1 and ρ2 respectively represent the preset weight coefficients;

[0043] The comprehensive evaluation coefficient G corresponding to each smart device in the target store i Compared with the preset comprehensive evaluation threshold G i 'Compare;

[0044] If there is a comprehensive evaluation coefficient G corresponding to the smart device in the target store i ≥G i ', then the smart device is marked once.

[0045] In the second aspect, the present application provides an intelligent store inspection system, which adopts the following technical solutions:

[0046] An intelligent store inspection system, comprising:

[0047] An instruction receiving module is used to receive and execute instructions for stores to be inspected, confirm the stores to be inspected, and mark the stores to be inspected as target stores;

[0048] The information acquisition module is used to obtain the basic information and operation information corresponding to each smart device in the target store;

[0049] The information analysis module is used to process and analyze the basic information and operation information corresponding to each smart device in the target store, analyze the safety hazards of the target store based on the results of the processing and analysis, and formulate early warning strategies based on the results of the safety hazard analysis;

[0050] The early warning module is used to execute early warning strategies and output alarm signals to staff.

[0051] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any one of the intelligent store inspection methods described above.

[0052] In summary, the present application includes at least one of the following beneficial technical effects:

[0053] 1. The present invention provides an intelligent store inspection method, which identifies the target store through the store instructions to be inspected, obtains the basic information and operation information corresponding to each intelligent device in the target store, and then processes and analyzes the basic information and operation information corresponding to each intelligent device in the target store, analyzes the safety hazards of the target store according to the results of the processing and analysis, formulates an early warning strategy according to the results of the safety hazard analysis, and then executes the early warning strategy, and outputs an alarm signal to the staff, thereby effectively reducing the occurrence of safety hazards, thereby effectively improving the inspection efficiency;

[0054] 2. By collecting the temperature data and vibration data corresponding to each smart device in the target store, and then confirming the temperature data change curve over time and the vibration data change curve over time corresponding to each smart device in the target store, further analysis and judgment are carried out to confirm the comprehensive evaluation coefficient corresponding to each smart device in the target store, and then the comprehensive evaluation coefficient corresponding to each smart device in the target store is compared with the preset comprehensive evaluation threshold, and then the smart device whose comprehensive evaluation coefficient exceeds the preset comprehensive evaluation threshold is marked once, so as to accurately confirm the marking number of each smart device, thereby effectively providing a real data source for the risk factor of safety hazards in the target store, and further improving the inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 It is a flow chart of the method for intelligent store inspection in an embodiment of the present application.

[0057] Figure 2 It is a system diagram of the intelligent store smart inspection in the embodiment of the present application. DETAILED DESCRIPTION

[0058] The following is combined with Figure 1-2 This application is described in further detail.

[0059] Example 1

[0060] The embodiment of the present application discloses an intelligent store inspection method.

[0061] Reference Figure 1 , an intelligent store intelligent inspection method, comprising the following steps:

[0062] S1. Receive and execute instructions for stores to be inspected, confirm stores to be inspected, and mark stores to be inspected as target stores;

[0063] S2. Obtain the basic information and operation information of each smart device in the target store;

[0064] S3. Process and analyze the basic information and operation information corresponding to each smart device in the target store, analyze the safety hazards of the target store based on the results of the processing and analysis, and formulate an early warning strategy based on the results of the safety hazard analysis;

[0065] S4. Execute the early warning strategy and output an alarm signal to the staff.

[0066] Furthermore, the basic information corresponding to each smart device in the target store includes type information, maintenance information and tag information, and the maintenance information includes the number of maintenance times and the maintenance duration corresponding to each maintenance time;

[0067] The operating information corresponding to each smart device in the target store includes an operating voltage value, an operating current value and an operating time.

[0068] It should be noted that the basic information and operation information corresponding to each smart device in the target store is processed and analyzed, including:

[0069] Based on the basic information corresponding to each smart device in the target store, the type information corresponding to each smart device in the target store is confirmed, and then the type influence coefficient μ corresponding to each smart device in the target store is extracted from the cloud database according to the type information corresponding to each smart device in the target store. i , where i represents the number of each smart device in the target store, i = 1, 2, 3.....j;

[0070] Specifically, μ i It is represented as the type influence coefficient corresponding to each smart device in the target store. The above coefficient can be set according to the probability and severity of failure of each smart device in the target store, and can be obtained by fitting historical data;

[0071] By formula Confirm the risk factor m of the target store having safety hazards r ;

[0072] Among them, s represents the number of maintenance times, t is It is represented by the maintenance time corresponding to the sth maintenance of the smart device numbered i in the target store, T i It is represented by the working time of the smart device numbered i in the target store, n i It is represented by the number of times the smart device numbered i in the target store is marked, and e is a natural constant;

[0073] Specifically, i It is represented by the number of markings corresponding to each smart device in the target store. That is, the more times a smart device is marked, the greater the probability that the smart device has a security risk.

[0074] Compare the risk factor m of the potential safety hazard of the target store with the preset potential safety hazard risk threshold m′;

[0075] If the target store has a risk factor of safety hazards m≤m′, there is no need to conduct safety hazard analysis on the target store;

[0076] If the risk coefficient of safety hazards in the target store is m>m′, a safety hazard analysis is required for the target store.

[0077] It should be noted that the safety hazard analysis for the target stores includes:

[0078] Select a time window, confirm the operation information corresponding to each smart device in the target store, and collect the working voltage value, working current value and working time corresponding to each smart device in the target store in real time according to the operation information;

[0079] The working voltage values ​​corresponding to each smart device in the target store are collected in real time to form a time series, and then the working voltage values ​​collected in real time are converted into a function V based on the time series. i (t) indicates;

[0080] By formula Confirm the voltage evaluation coefficient α corresponding to each smart device in the target store i , where v i It represents the standard voltage corresponding to each smart device in the target store when it is in working state, [t1, t2] represents the time period when the working voltage value corresponding to each smart device in the target store is greater than the standard voltage, and [t3, t4] represents the time period when the working voltage value corresponding to each smart device in the target store is less than the standard voltage;

[0081] Specifically, by collecting and analyzing the working voltage of each smart device in the target store when it is in working state, the voltage stability corresponding to each smart device in the target store is analyzed, and then the voltage evaluation coefficient corresponding to each smart device in the target store is confirmed.

[0082] Similarly, the working current values ​​corresponding to each smart device in the target store are collected in real time to form a time series, and then the working current values ​​collected in real time are converted into a function I based on the time series. i (t) represents and confirms the current evaluation coefficient β corresponding to each smart device in the target store i ;

[0083] Specifically, through the formula Confirm the current evaluation coefficient β corresponding to each smart device in the target store i , where z i It represents the standard current corresponding to each smart device in the target store when it is in working state, [t1, t2] represents the time period when the working current value corresponding to each smart device in the target store is greater than the standard current, and [t3, t4] represents the time period when the working current value corresponding to each smart device in the target store is less than the standard current;

[0084] By collecting and analyzing the working current of each smart device in the target store when it is in working state, the current stability corresponding to each smart device in the target store is analyzed, and then the current evaluation coefficient corresponding to each smart device in the target store is confirmed;

[0085] By formula Confirm the operation evaluation coefficient γ corresponding to the target store, where ω1 and ω2 represent the weight coefficients corresponding to the voltage evaluation coefficient and the current evaluation coefficient, respectively. i ′ represents the allowed working time corresponding to the smart device numbered i in the target store, ΔT represents the difference in allowed working time, and ψ1 and ψ2 represent the preset correlation coefficients respectively;

[0086] Compare the operation evaluation coefficient γ corresponding to the target store with the preset operation evaluation threshold γ′;

[0087] If the operation evaluation coefficient γ ≤ γ′ corresponding to the target store, there is no need to formulate an early warning strategy;

[0088] If the operation evaluation coefficient γ corresponding to the target store is greater than γ′, an early warning strategy needs to be formulated for the target store.

[0089] Furthermore, early warning strategies are formulated for target stores, including:

[0090]

[0091] The reference evaluation coefficient W of the target store's potential safety hazards is calculated through the above formula. yj ;

[0092] in, They are respectively represented as preset correlation coefficients;

[0093] The reference evaluation coefficient W of the target store with potential safety hazards yj Compare with the preset reference evaluation threshold W′;

[0094] If the target store has safety hazards, the reference assessment coefficient W yj ≤W′, then output the first alarm signal;

[0095] If the target store has safety hazards, the reference assessment coefficient W yj >W′, then the second alarm signal is output and at the same time, the smart devices of the target store are adjusted.

[0096] Specifically, adjustment measures include but are not limited to repair or replacement.

[0097] It should be noted that the process of obtaining the corresponding tag information of each smart device in the target store specifically includes:

[0098] The temperature data and vibration data corresponding to each smart device in the target store are collected based on the detection device, and the temperature data change curve U over time is confirmed based on the temperature data and vibration data corresponding to each smart device in the target store. i (t) and the vibration data changing with time curve E i (t);

[0099] A comprehensive judgment is made on the temperature data change curve over time and the vibration data change curve over time corresponding to each smart device in the target store, and each smart device in the target store is marked based on the judgment result.

[0100] Furthermore, a comprehensive judgment is made on the temperature value change data corresponding to each smart device in the target store over time, including:

[0101]

[0102] The above formula is used to calculate and confirm the comprehensive evaluation coefficient G corresponding to each smart device in the target store. i ;

[0103] Among them, u i (t), e i (t) respectively represent the preset standard temperature data variation curve over time and the preset standard vibration data variation curve over time corresponding to the smart device numbered i in the target store, u i 、e i They respectively represent the reference temperature data and reference vibration data corresponding to the smart device numbered i in the target store, and ρ1 and ρ2 respectively represent the preset weight coefficients;

[0104] The comprehensive evaluation coefficient G corresponding to each smart device in the target store i Compared with the preset comprehensive evaluation threshold G i 'Compare;

[0105] If there is a comprehensive evaluation coefficient G corresponding to the smart device in the target store i ≥G i ', then the smart device is marked once.

[0106] Through the above technical solution, through the formula Confirm the comprehensive evaluation coefficient G corresponding to each smart device in the target store i That is, the higher the comprehensive evaluation coefficient, the greater the probability of failure of the smart device. The comprehensive evaluation coefficient G corresponding to each smart device in the target store i Compared with the preset comprehensive evaluation threshold G i ′ for comparison, if there is a comprehensive evaluation coefficient G corresponding to the smart device in the target store i ≥G i ′, it indicates that the corresponding temperature data and vibration data of the smart device are abnormal when it is running, and the target device is marked once.

[0107] Example 2

[0108] The embodiment of the present application also discloses an intelligent store inspection system.

[0109] Reference Figure 2 , an intelligent store inspection system, comprising:

[0110] An instruction receiving module is used to receive and execute instructions for stores to be inspected, confirm the stores to be inspected, and mark the stores to be inspected as target stores;

[0111] The information acquisition module is used to obtain the basic information and operation information corresponding to each smart device in the target store;

[0112] The information analysis module is used to process and analyze the basic information and operation information corresponding to each smart device in the target store, analyze the safety hazards of the target store based on the results of the processing and analysis, and formulate early warning strategies based on the results of the safety hazard analysis;

[0113] The early warning module is used to execute early warning strategies and output alarm signals to staff.

[0114] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent store inspection method, characterized in that: The following steps are involved: Receive and execute instructions for stores to be inspected, confirm stores to be inspected, and mark stores to be inspected as target stores; Obtain basic information and operation information corresponding to each smart device in the target store; Process and analyze the basic information and operation information corresponding to each smart device in the target store, analyze the safety hazards of the target store based on the results of the processing and analysis, and formulate early warning strategies based on the results of the safety hazard analysis; Execute early warning strategies and output warning signals to staff.

2. According to claim 1, an intelligent store inspection method is characterized by: The basic information corresponding to each smart device in the target store includes type information, maintenance information and tag information, and the maintenance information includes the number of maintenance times and the maintenance time corresponding to each maintenance time; The operating information corresponding to each smart device in the target store includes an operating voltage value, an operating current value and an operating time.

3. According to claim 2, an intelligent store inspection method is characterized in that: Process and analyze the basic information and operation information of each smart device in the target store, including: Based on the basic information corresponding to each smart device in the target store, the type information corresponding to each smart device in the target store is confirmed, and then the type influence coefficient μ corresponding to each smart device in the target store is extracted from the cloud database according to the type information corresponding to each smart device in the target store. i , where i represents the number of each smart device in the target store, i = 1, 2, 3.....j; By formula Confirm the risk factor m of the target store having safety hazards r ; Among them, s represents the number of maintenance times, t is It is represented by the maintenance time corresponding to the sth maintenance of the smart device numbered i in the target store, T i It is represented by the working time of the smart device numbered i in the target store, n i It is represented by the number of times the smart device numbered i in the target store is marked, and e is a natural constant; Compare the risk factor m of the potential safety hazard of the target store with the preset potential safety hazard risk threshold m′; If the target store has a risk factor of safety hazards m≤m′, there is no need to conduct safety hazard analysis on the target store; If the risk coefficient of safety hazards in the target store is m>m′, a safety hazard analysis is required for the target store.

4. The intelligent store inspection method according to claim 3 is characterized in that: Conduct safety hazard analysis on target stores, including: Select a time window, confirm the operation information corresponding to each smart device in the target store, and collect the working voltage value, working current value and working time corresponding to each smart device in the target store in real time according to the operation information; Collect the working voltage values ​​corresponding to each smart device in the target store in real time to form a time series, and then represent the working voltage values ​​collected in real time with the function Vi(t) based on the time series; By formula Determine the voltage assessment coefficient αi corresponding to each smart device in the target store, where vi represents the standard voltage corresponding to each smart device in the target store when it is in working state, [t1, t2] represents the time period when the working voltage value corresponding to each smart device in the target store is greater than the standard voltage, and [t3, t4] represents the time period when the working voltage value corresponding to each smart device in the target store is less than the standard voltage; Similarly, the working current values ​​corresponding to each smart device in the target store are collected in real time to form a time series, and then the working current values ​​collected in real time are converted into a function I based on the time series. i (t) represents and confirms the current evaluation coefficient β corresponding to each smart device in the target store i ; By formula Confirm the operation evaluation coefficient γ corresponding to the target store, where ω1 and ω2 represent the weight coefficients corresponding to the voltage evaluation coefficient and the current evaluation coefficient, respectively. i ′ represents the allowed working time corresponding to the smart device numbered i in the target store, ΔT represents the difference in allowed working time, and ψ1 and ψ2 represent the preset correlation coefficients respectively; Compare the operation evaluation coefficient γ corresponding to the target store with the preset operation evaluation threshold γ′; If the operation evaluation coefficient γ ≤ γ′ corresponding to the target store, there is no need to formulate an early warning strategy; If the operation evaluation coefficient γ corresponding to the target store is greater than γ′, an early warning strategy needs to be formulated for the target store.

5. The intelligent store inspection method according to claim 4 is characterized in that: Develop early warning strategies for target stores, including: The reference evaluation coefficient Wyj of the potential safety hazard of the target store is calculated by the above formula; in, They are respectively represented as preset correlation coefficients; The reference evaluation coefficient W of the target store with potential safety hazards yj Compare with the preset reference evaluation threshold W′; If the target store has safety hazards, the reference assessment coefficient W yj ≤W′, then output the first alarm signal; If the target store has safety hazards, the reference assessment coefficient W yj >W′, then the second alarm signal is output and at the same time, the smart devices of the target store are adjusted.

6. The intelligent store inspection method according to claim 5 is characterized in that: The process of obtaining the corresponding tag information of each smart device in the target store specifically includes: The temperature data and vibration data corresponding to each smart device in the target store are collected based on the detection device, and the temperature data change curve U over time is confirmed based on the temperature data and vibration data corresponding to each smart device in the target store. i (t) and the vibration data changing with time curve E i (t); A comprehensive judgment is made on the temperature data change curve over time and the vibration data change curve over time corresponding to each smart device in the target store, and each smart device in the target store is marked based on the judgment result.

7. The intelligent store inspection method according to claim 6 is characterized in that: Comprehensively judge the temperature value change over time of each smart device in the target store, including: The above formula is used to calculate and confirm the comprehensive evaluation coefficient G corresponding to each smart device in the target store. i ; Among them, u i (t), e i (t) respectively represent the preset standard temperature data variation curve over time and the preset standard vibration data variation curve over time corresponding to the smart device numbered i in the target store, u i 、e i They respectively represent the reference temperature data and reference vibration data corresponding to the smart device numbered i in the target store, and ρ1 and ρ2 respectively represent the preset weight coefficients; The comprehensive evaluation coefficient G corresponding to each smart device in the target store i Compared with the preset comprehensive evaluation threshold G i 'Compare; If there is a comprehensive evaluation coefficient G corresponding to the smart device in the target store i ≥G i ', then the smart device is marked once.

8. An intelligent store inspection system, applied to an intelligent store inspection method according to claims 1 to 7, characterized in that: include: An instruction receiving module is used to receive and execute instructions for stores to be inspected, confirm the stores to be inspected, and mark the stores to be inspected as target stores; The information acquisition module is used to obtain the basic information and operation information corresponding to each smart device in the target store; The information analysis module is used to process and analyze the basic information and operation information corresponding to each smart device in the target store, analyze the safety hazards of the target store based on the results of the processing and analysis, and formulate early warning strategies based on the results of the safety hazard analysis; The early warning module is used to execute early warning strategies and output alarm signals to staff.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes an intelligent store inspection method as described in any one of claims 1-7.

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