A data analysis and management system and method based on cloud-edge collaboration

Abstract

The present invention relates to the field of data analysis management technology, and specifically to a data analysis management system based on cloud-edge collaboration and a method thereof, which deploys multiple edge nodes, obtains environmental data in a device production process, processes the environmental data of the device, obtains an environmental score, and determines whether there is a risk in the device production environment; deploys multiple edge nodes, obtains device operation data, processes the device operation data, obtains a device score, and determines whether there is a risk in the device; sends environmental change risk signals and device change risk signals analyzed by the edge nodes to the cloud, evaluates their linkage risks, and obtains high-degree signals with the same frequency; when high-degree signals with the same frequency are obtained, determines the production warning time of the equipment; the present invention performs timely warning processing on production safety and reduces the risk of equipment production.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and management, and particularly relates to a data analysis and management system and method based on cloud-edge collaboration. Background Art

[0002] Cloud-edge collaboration combines the elasticity of cloud computing and the real-time nature of edge computing to better meet the requirements in different scenarios. Cloud-edge collaboration enables Internet of Things devices to better connect to cloud applications and services, realizing remote monitoring and management of the devices.

[0003] The traditional methods for data analysis and management involved in device production often rely on manual inspections and simple automated monitoring systems. However, such methods have problems such as slow response and incomplete data analysis. Summary of the Invention

[0004] The purpose of the present invention is to provide a data analysis and management system and method based on cloud-edge collaboration to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A data analysis and management method based on cloud-edge collaboration includes the following steps:

[0007] Deploy multiple edge nodes, obtain the environmental data during the device production process, process the environmental data of the device to obtain an environmental score, and determine whether there is a risk in the production environment of the device;

[0008] Among them, the environmental data includes the environmental temperature value;

[0009] Deploy multiple edge nodes, obtain the device operation data, process the device operation data to obtain a device score, and determine whether there is a risk in the device;

[0010] Among them, the operation data includes the device operation temperature;

[0011] Send the environmental change risk signal and the device change risk signal analyzed by the edge nodes to the cloud, evaluate their associated risks, and obtain a high-degree signal of the same frequency;

[0012] When obtaining a high-degree signal of the same frequency, determine the production warning time of the device.

[0013] As a further technical solution of the present invention: the process of obtaining the environmental score is as follows:

[0014] Compare the environmental temperature amplitude value of the analysis subunit with the environmental temperature amplitude threshold;

[0015] When the environmental temperature amplitude value is greater than or equal to the environmental temperature amplitude threshold, mark the analysis subunit as a high-change analysis subunit;

[0016] Sum up the environmental temperature amplitude values of all high-change analysis subunits to obtain an environmental score.

[0017] As a further technical solution of the present invention: The process of obtaining the environmental temperature amplitude value of the analysis subunit is as follows:

[0018] Obtain the environmental temperature curve, extract the peak points and valley points in the environmental temperature curve, take the adjacent peak point and valley point as an analysis subunit, obtain the coordinates corresponding to the peak point of the analysis subunit, and the coordinates corresponding to two adjacent valley points, and mark them as the set temperature peak point coordinates and the set temperature valley point coordinates respectively. Calculate the set temperature width amplitude and the set temperature vertical amplitude through the set temperature peak point coordinates and the set temperature valley point coordinates;

[0019] Take the ratio of the set temperature vertical amplitude to the set temperature width amplitude to obtain the environmental temperature amplitude value of the analysis subunit.

[0020] As a further technical solution of the present invention: The process of judging whether there is a risk in the production environment is as follows:

[0021] If the environmental score is greater than or equal to the environmental score threshold, generate an environmental change high-risk signal;

[0022] If the environmental score is less than the environmental score threshold, generate an environmental change low-risk signal.

[0023] As a further technical solution of the present invention: The process of obtaining the equipment score is as follows:

[0024] Compare the operating temperature amplitude value of the analysis subunit with the operating temperature amplitude threshold;

[0025] When the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold, mark the analysis subunit as a high-change analysis subunit;

[0026] Sum up the operating temperature amplitude values of all high-change analysis subunits to obtain the equipment score.

[0027] As a further technical solution of the present invention: The process of obtaining the operating temperature amplitude value of the analysis subunit is as follows:

[0028] Obtain the operating temperature curve, extract the peak points and valley points in the operating temperature curve, take the adjacent peak point and valley point as an analysis subunit, obtain the coordinates corresponding to the peak point of the analysis subunit, and the coordinates corresponding to two adjacent valley points, and mark them as the set temperature peak point coordinates and the set temperature valley point coordinates respectively. Calculate the set temperature width amplitude and the set temperature vertical amplitude through the set temperature peak point coordinates and the set temperature valley point coordinates;

[0029] Divide the set temperature vertical amplitude value by the set temperature width amplitude value to obtain the operating temperature amplitude value of the analysis sub-unit.

[0030] As a further technical solution of the present invention: The process of judging whether there is a risk in the device is as follows:

[0031] If the device score is greater than or equal to the device score threshold, generate a high-risk signal for device change;

[0032] If the device score is less than the device score threshold, generate a low-risk signal for device change.

[0033] As a further technical solution of the present invention: The process of obtaining the high-degree signal of the same frequency is as follows:

[0034] If a high-risk signal for environmental change and a high-risk signal for device change are obtained simultaneously, or a low-risk signal for environmental change and a low-risk signal for device change, generate a linkage analysis signal;

[0035] When the linkage analysis signal is obtained, analyze based on the environmental temperature curve and the operating temperature curve, calculate the same-frequency degree value. If the same-frequency degree value is less than the same-frequency degree threshold, then generate a high-degree signal of the same frequency.

[0036] As a further technical solution of the present invention: The process of determining the production warning time is as follows:

[0037] When the high-degree signal of the same frequency is obtained, compare the high-change analysis sub-units in the operating temperature curve with the high-change analysis sub-units in the environmental temperature curve one by one, obtain the minimum set temperature valley point of the high-change analysis sub-unit in the environmental temperature curve, and the minimum set temperature valley point of the high-change analysis sub-unit in the operating temperature curve, and perform a difference calculation to obtain the environmental set temperature influence time difference;

[0038] If the environmental set temperature influence time difference is greater than or equal to the environmental set temperature influence time difference threshold, then generate a large environmental influence signal, and mark the high-change analysis sub-unit corresponding to the large environmental influence signal as the influence analysis sub-unit;

[0039] Obtain the number of influence analysis sub-units, and perform a ratio process with the high-change analysis sub-units to obtain the proportion of the influence analysis sub-units;

[0040] If the proportion of the influence analysis sub-units is greater than or equal to the proportion threshold of the influence analysis sub-units, calculate the average value of the environmental temperature influence time differences of all the influence analysis sub-units to obtain the warning time.

[0041] A data analysis and management system based on cloud-edge collaboration, the system includes:

[0042] Edge - side environmental assessment module: Deploy multiple edge nodes, obtain environmental data during the equipment production process, process the environmental data of the equipment to obtain an environmental score, and determine whether there is a risk in the production environment of the equipment;

[0043] Among them, the environmental data includes the environmental temperature value;

[0044] Edge - side equipment assessment module: Deploy multiple edge nodes, obtain equipment operation data, process the equipment operation data to obtain an equipment score, and determine whether there is a risk in the equipment;

[0045] Among them, the operation data includes the equipment operation temperature;

[0046] Cloud - side associated assessment module: Send the environmental change risk signals and equipment change risk signals analyzed by the edge nodes to the cloud, evaluate their linkage risks, and obtain high - degree signals with the same frequency;

[0047] Cloud - side risk warning module: When obtaining high - degree signals with the same frequency, determine the production warning time of the equipment.

[0048] Advantages of the present invention:

[0049] (1) The present invention obtains the environmental data during the equipment production process, processes the environmental data to obtain an environmental score, and determines whether there is a risk in the production environment; obtains the equipment operation data, processes the equipment operation data to obtain an equipment score, and determines whether there is a risk in the equipment. The present invention independently analyzes and evaluates the environment and equipment during the production process through edge processing, realizing a more comprehensive safety assessment of the production process. Further, through intelligent analysis from two aspects of the temperature change time and the temperature change value, a more accurate safety assessment of the production process is realized;

[0050] (2) The present invention evaluates the linkage risks according to the generated environmental change risk signals and equipment change risk signals, obtains high - degree signals with the same frequency, and when obtaining high - degree signals with the same frequency, determines the production warning time of the equipment. During the production process, the present invention evaluates the degree of risk association between the environment and the equipment, further realizing the comprehensiveness of the safety assessment during the production process, and based on the results of intelligent analysis, the warning time can be determined stage - by - stage, so as to conduct timely warning processing on production safety and reduce the risk of equipment production. Description of the drawings

[0051] The present invention will be further described below with reference to the drawings.

[0052] Figure 1 It is the flowchart of Embodiment 1 of the present invention;

[0053] Figure 2It is the flowchart of Embodiment 2 of the present invention;

[0054] Figure 3 It is the system block diagram of Embodiment 3 of the present invention. Detailed implementation manners

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0056] Embodiment 1, please refer to Figure 1 As shown, the present invention is a data analysis and management method based on cloud-edge collaboration, including the following steps:

[0057] S1: Deploy multiple edge nodes, obtain the environmental data during the device production process, process the environmental data of the device to obtain an environmental score, and determine whether there is a risk in the production environment of the device;

[0058] Among them, the environmental data includes the environmental temperature value;

[0059] In some implementation schemes, temperature sensors are deployed on multiple edge nodes of the target device. During the monitoring period, the environmental temperature value of the device production is collected in real time by the temperature sensors according to the time series (such as every minute);

[0060] Taking time as the X-axis and the environmental temperature value as the Y-axis, construct a time-environment temperature two-dimensional coordinate system, substitute the real-time collected environmental temperature value of the production into the two-dimensional coordinate system, and draw an environmental temperature curve, and analyze the environmental temperature curve to determine the degree of change of the environmental temperature;

[0061] Specifically, the determination process of the degree of change of the environmental temperature includes the following steps:

[0062] Obtain the environmental temperature curve, extract the peak points and valley points in the environmental temperature curve, take the adjacent peak points and valley points as an analysis sub-unit, obtain the coordinates corresponding to the peak points of the analysis sub-unit, and the coordinates corresponding to the adjacent two valley points, and mark them as the set temperature peak point coordinates and the set temperature valley point coordinates respectively. Through the set temperature peak point coordinates and the set temperature valley point coordinates, calculate the set temperature width amplitude and the set temperature vertical amplitude, and take the ratio of the set temperature vertical amplitude to the set temperature width amplitude to obtain the environmental temperature amplitude value of the analysis sub-unit;

[0063] Compare the environmental temperature amplitude value of the analysis sub-unit with the environmental temperature amplitude threshold;

[0064] If the environmental temperature amplitude value is greater than or equal to the environmental temperature amplitude threshold, it indicates that there is a large change in the environmental temperature corresponding to the analysis subunit, and the analysis subunit is marked as a high-change analysis subunit;

[0065] If the environmental temperature amplitude value is less than the environmental temperature amplitude threshold, it indicates that there is a small change in the environmental temperature corresponding to the analysis subunit, and the analysis subunit is marked as a low-change analysis subunit;

[0066] Sum up the environmental temperature amplitude values of all high-change analysis subunits to obtain an environmental score;

[0067] Compare the environmental score with the environmental score threshold;

[0068] If the environmental score is greater than or equal to the environmental score threshold, it indicates that during the monitoring period, the environmental temperature changes greatly during the production process (evaluated comprehensively from the aspects of temperature change time and temperature change value), and exceeds the preset environmental temperature conditions required for production, which will cause greater interference to production, affect the normal operation of production, there is a greater production safety risk, and an environmental change high-risk signal is generated;

[0069] If the environmental score is less than the environmental score threshold, it indicates that during the monitoring period, the environmental temperature changes little during the production process (evaluated comprehensively from the aspects of temperature change time and temperature change value), and does not exceed the preset environmental temperature conditions required for production, which will not cause greater interference to production, does not affect the normal operation of production, there is no greater production safety risk, and an environmental change low-risk signal is generated;

[0070] First preferably, the calculation process of the temperature setting width amplitude is as follows:

[0071] Obtain the abscissa in the temperature setting valley point coordinates, calculate the difference between the abscissas of the two temperature setting valley points adjacent to the peak point to obtain the temperature setting width amplitude;

[0072] Second preferably, the calculation process of the temperature setting vertical amplitude is as follows:

[0073] Obtain the ordinate in the temperature setting peak point coordinates, calculate the differences between the ordinates of the two temperature setting valley points adjacent to the peak point respectively to obtain two adjacent temperature setting vertical amplitudes, and then perform an average calculation to obtain the temperature setting vertical amplitude;

[0074] S2: Deploy multiple edge nodes, obtain device operation data, process the device operation data to obtain a device score, and determine whether there is a risk in the device;

[0075] Among them, the operation data includes the device operation temperature;

[0076] In some embodiments, temperature sensors are deployed at multiple other edge nodes of the target device. During the monitoring period, the operating temperature values of the device are collected in real time by the temperature sensors according to a time series (e.g., every minute).

[0077] Taking time as the X-axis and the operating temperature value as the Y-axis, a two-dimensional time-operating temperature coordinate system is constructed. The operating temperature values collected in real time are substituted into the two-dimensional coordinate system, and an operating temperature curve is plotted. The operating temperature curve is analyzed to determine the degree of change in the operating temperature.

[0078] Specifically, the process of determining the degree of change in the operating temperature includes the following steps:

[0079] Obtain the operating temperature curve, and extract the peak points and valley points in the operating temperature curve. Take the adjacent peak point and valley point as an analysis sub-unit. Obtain the coordinates corresponding to the peak point of the analysis sub-unit and the coordinates corresponding to two adjacent valley points, and mark them as the set temperature peak point coordinates and the set temperature valley point coordinates respectively. Through the set temperature peak point coordinates and the set temperature valley point coordinates, calculate the set temperature width amplitude and the set temperature vertical amplitude, and take the ratio of the set temperature vertical amplitude to the set temperature width amplitude to obtain the operating temperature amplitude value of the analysis sub-unit.

[0080] Compare the operating temperature amplitude value of the analysis sub-unit with the operating temperature amplitude threshold.

[0081] If the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold, it indicates that there is a large change in the operating temperature corresponding to this analysis sub-unit. Mark the analysis sub-unit as a high-change analysis sub-unit.

[0082] If the operating temperature amplitude value is less than the operating temperature amplitude threshold, it indicates that there is a small change in the operating temperature corresponding to this analysis sub-unit. Mark the analysis sub-unit as a low-change analysis sub-unit.

[0083] Sum up the operating temperature amplitude values of all high-change analysis sub-units to obtain the device score.

[0084] Compare the device score with the device score threshold.

[0085] If the device score is greater than or equal to the device score threshold, it indicates that during the monitoring period, the degree of change in the operating temperature during the production process is large (evaluated comprehensively from both the temperature change time and the temperature change value), and it exceeds the preset operating temperature conditions required for production, which will cause a large interference to the production, affect the normal operation of the production, and there is a large production safety risk. Generate a high-risk signal for device change.

[0086] If the device score is less than the device score threshold, it means that during the monitoring period, the degree of change in the operating temperature during the production process is small (evaluated comprehensively from two aspects: the temperature change time and the temperature change value), and it does not exceed the preset operating temperature conditions required for production. It will not cause great interference to production, will not affect the normal operation of production, and there is no major production safety risk, generating a low-risk signal for device changes;

[0087] First preferably, the calculation process of the temperature setting width amplitude is as follows:

[0088] Obtain the abscissa in the temperature setting valley point coordinates, calculate the difference between the abscissas of the two temperature setting valley points adjacent to the peak point, and obtain the temperature setting width amplitude;

[0089] Second preferably, the calculation process of the temperature setting vertical amplitude is as follows:

[0090] Obtain the ordinate in the temperature setting peak point coordinates, calculate the differences between the ordinates of the two temperature setting valley points adjacent to the peak point respectively to obtain two adjacent temperature setting vertical amplitudes, and then perform an average calculation to obtain the temperature setting vertical amplitude;

[0091] The technical solution of the embodiment of the present invention: Obtain the environmental data during the device production process, process the environmental data to obtain an environmental score, and judge whether there is a risk in the production environment; obtain the device operation data, process the device operation data to obtain a device score, and judge whether there is a risk in the device; the present invention independently analyzes and evaluates the environment and the device during the production process through edge processing, realizes a more comprehensive safety assessment of the production process. Further, through intelligent analysis from two aspects of the temperature change time and the temperature change value, a more accurate safety assessment of the production process is realized.

[0092] Example 2, please refer to Figure 2 As shown, the present invention is a data analysis and management method based on cloud-edge collaboration, including the following steps:

[0093] Step 3: Send the environmental change risk signal and the device change risk signal analyzed by the edge node to the cloud, evaluate their linkage risks, and obtain a high-degree signal of the same frequency;

[0094] In some implementation schemes, send the environmental change risk signal and the device change risk signal analyzed by the edge node to the cloud, and perform cross-analysis on the high-risk signal of environmental change, the low-risk signal of environmental change, the high-risk signal of device change, and the low-risk signal of device change;

[0095] If a high-risk signal of environmental change and a high-risk signal of device change, or a low-risk signal of environmental change and a low-risk signal of device change are obtained simultaneously, a linkage analysis signal is generated;

[0096] If high-risk signals of environmental changes and low-risk signals of equipment changes are obtained simultaneously, or low-risk signals of environmental changes and high-risk signals of equipment changes are obtained, a non-linkage analysis signal is generated;

[0097] It should be noted that: the non-linkage analysis signal indicates that there is a risk degree of mismatch between the temperature change situation generated by the environment and the temperature change situation generated by the equipment, that is, it can indicate that the current production environment has a relatively small impact on equipment production.

[0098] When a linkage analysis signal is obtained, analysis is performed based on the environmental temperature curve and the operating temperature curve, and the co-frequency degree value is calculated and compared with the co-frequency degree threshold;

[0099] If the co-frequency degree value is less than the co-frequency degree threshold, a high co-frequency degree signal is generated;

[0100] If the co-frequency degree value is greater than or equal to the co-frequency degree threshold, a low co-frequency degree signal is generated;

[0101] Among them, the calculation process of the co-frequency degree value is as follows:

[0102] In chronological order, the high-change analysis sub-units in the operating temperature curve are compared one by one with the high-change analysis sub-units in the environmental temperature curve (that is, the high-change analysis sub-unit in the i-th operating temperature curve is compared with the high-change analysis sub-unit in the i-th environmental temperature curve), and the ratio of the operating temperature amplitude value of the high-change analysis sub-unit to the environmental temperature amplitude value is calculated to obtain the influence ratio of the high-change analysis sub-unit;

[0103] The variance of the influence ratios of all high-change analysis sub-units is calculated to obtain the influence fluctuation value of the high-change analysis sub-unit, and the influence fluctuation value of the high-change analysis sub-unit is recorded as the co-frequency degree value;

[0104] It should be noted that when a high co-frequency degree signal is obtained, it indicates that there is a relatively large influence between the environmental temperature change and the operating temperature change during the production process;

[0105] When a low co-frequency degree signal is obtained, it indicates that the influence of the environmental temperature on the operating temperature is relatively small during the production process. Therefore, when a low co-frequency degree signal is obtained, it is necessary to check the risk states of the environment and equipment during the production process;

[0106] Step 4: When a high co-frequency degree signal is obtained, determine the production warning time of the equipment;

[0107] In some embodiments, when a co-frequency high-degree signal is obtained, the high-change analysis sub-units in the operating temperature curve are compared one by one with the high-change analysis sub-units in the ambient temperature curve to obtain the minimum set temperature valley point of the high-change analysis sub-unit in the ambient temperature curve and the minimum set temperature valley point of the high-change analysis sub-unit in the operating temperature curve, which are respectively marked as the minimum valley point of the ambient temperature sub-unit and the minimum valley point of the set temperature sub-unit;

[0108] Calculate the difference between the minimum valley point of the ambient temperature sub-unit and the minimum valley point of the set temperature sub-unit to obtain the time difference affected by the ambient-set temperature;

[0109] Compare the time difference affected by the ambient-set temperature with the time difference threshold affected by the ambient-set temperature;

[0110] If the time difference affected by the ambient-set temperature is greater than or equal to the time difference threshold affected by the ambient-set temperature, an ambient impact large signal is generated, and the high-change analysis sub-unit corresponding to the ambient impact large signal is marked as the impact analysis sub-unit;

[0111] If the time difference affected by the ambient-set temperature is less than the time difference threshold affected by the ambient-set temperature, an ambient impact small signal is generated;

[0112] Obtain the number of impact analysis sub-units, and process the ratio of the number of impact analysis sub-units to the high-change analysis sub-units to obtain the proportion of impact analysis sub-units;

[0113] If the proportion of impact analysis sub-units is greater than or equal to the proportion threshold of impact analysis sub-units, calculate the average value of the ambient temperature impact time differences of all impact analysis sub-units to obtain the average ambient temperature impact time difference, and mark the average ambient temperature impact time difference as the warning time; so that during the production process, if an abnormal ambient temperature situation occurs (that is, a high-change analysis sub-unit is detected in the ambient temperature), the temperatures of the equipment and the environment can be repaired in a timely manner within the warning time;

[0114] If the proportion of impact analysis sub-units is less than the proportion threshold of impact analysis sub-units, check the risk states of the environment and equipment during the production process;

[0115] The technical solution of the embodiment of the present invention: According to the generated environmental change risk signal and equipment change risk signal, evaluate the associated risk to obtain a co-frequency high-degree signal. When the co-frequency high-degree signal is obtained, determine the production warning time of the equipment; in the production process of the present invention, evaluate the risk correlation degree between the environment and the equipment, further realize the comprehensiveness of the safety assessment during the production process, and based on the intelligent analysis results of Embodiment 1, the warning time can be determined stage by stage, so as to give a timely warning for production safety and reduce the risk of equipment production.

[0116] Example 3, please refer to Figure 3As shown, the present invention is a data analysis and management system based on cloud-edge collaboration, including the following modules:

[0117] Edge environment assessment module: deploy multiple edge nodes to obtain environmental data during the equipment production process, process the equipment's environmental data, obtain environmental scores, and determine whether there are risks in the equipment's production environment;

[0118] Wherein, the environmental data includes an environmental temperature value;

[0119] Edge equipment assessment module: deploy multiple edge nodes, obtain equipment operation data, process the equipment operation data, obtain equipment scores, and determine whether the equipment has risks;

[0120] Among them, the operating data includes the operating temperature of the equipment;

[0121] Cloud-side correlation assessment module: sends the environmental change risk signal and equipment change risk signal obtained by edge node analysis to the cloud, evaluates their linkage risk, and obtains high-degree signals of the same frequency;

[0122] Cloud-side risk warning module: When a high-level signal with the same frequency is obtained, the production warning time of the equipment is determined.

[0123] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A data analysis and management method based on cloud-edge collaboration, characterized in that, The following steps are involved: Deploy multiple edge nodes to obtain environmental data during the equipment production process, process the equipment environmental data, obtain environmental scores, and determine whether there are risks in the equipment production environment; Wherein, the environmental data includes an environmental temperature value; Deploy multiple edge nodes, obtain device operation data, process the device operation data, obtain device scores, and determine whether the device has risks; Among them, the operating data includes the operating temperature of the equipment; The environmental change risk signal and the equipment change risk signal obtained by the edge node analysis are sent to the cloud to evaluate their linkage risk. If a high-risk environmental change signal and a high-risk equipment change signal, or a low-risk environmental change signal and a low-risk equipment change signal are obtained at the same time, a linkage analysis signal is generated; When the linkage analysis signal is obtained, an analysis is performed based on the ambient temperature curve and the operating temperature curve to calculate the frequency-coherence value. If the frequency-coherence value is less than the frequency-coherence threshold, a frequency-coherence high-degree signal is generated. When a high-degree signal of the same frequency is obtained, the high-variation analysis subunit in the operating temperature curve is compared with the high-variation analysis subunit in the ambient temperature curve one by one, and the minimum set temperature valley point of the high-variation analysis subunit in the ambient temperature curve and the minimum set temperature valley point of the high-variation analysis subunit in the operating temperature curve are obtained, and the difference is calculated to obtain the ambient temperature influence time difference; the high-variation analysis subunit is the analysis subunit when the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold; If the ambient temperature impact time difference is greater than or equal to the ambient temperature impact time difference threshold, a large environmental impact signal is generated, and the high-variation analysis subunit corresponding to the large environmental impact signal is marked as an impact analysis subunit; Obtain the number of impact analysis sub-units, perform ratio processing with the high change analysis sub-units, and obtain the proportion of impact analysis sub-units; If the proportion of the impact analysis sub-units is greater than or equal to the threshold of the proportion of the impact analysis sub-units, the average of the ambient temperature impact time differences of all the impact analysis sub-units is calculated to determine the production warning time of the equipment.

2. The data analysis management method based on cloud-edge collaboration according to claim 1, wherein The process of obtaining the environmental score is as follows: comparing the ambient temperature amplitude value of the analysis subunit with an ambient temperature amplitude threshold value; If the ambient temperature amplitude value is greater than or equal to the ambient temperature amplitude threshold, the analysis subunit is marked as a high-variation analysis subunit; The ambient temperature amplitude values ​​of all high-variation analysis subunits are summed up to obtain the ambient score.

3. A data analysis and management method based on cloud-edge collaboration according to claim 2, characterized in that, The process of obtaining the ambient temperature amplitude value of the analysis subunit is as follows: Obtain the ambient temperature curve, and extract the peak points and trough points in the ambient temperature curve, take the adjacent peak points and trough points as an analysis subunit, obtain the coordinates corresponding to the peak point of the analysis subunit, and the coordinates corresponding to two adjacent trough points, and mark them as the set temperature peak point coordinates and the set temperature trough point coordinates, respectively, and calculate the set temperature width and the set temperature vertical amplitude through the set temperature peak point coordinates and the set temperature trough point coordinates; The vertical amplitude value of the set temperature is compared with the wide amplitude value of the set temperature to obtain the ambient temperature amplitude value of the analysis subunit.

4. A data analysis and management method based on cloud-edge collaboration according to claim 3, characterized in that The process of determining whether there are risks in the production environment is as follows: If the environmental score is greater than or equal to the environmental score threshold, a high-risk signal for environmental change is generated; If the environmental score is less than the environmental score threshold, a low-risk signal of environmental change is generated.

5. The data analysis and management method based on cloud-edge collaboration according to claim 4, characterized in that The process of obtaining the device score is as follows: Compare the operating temperature amplitude value of the analysis subunit with the operating temperature amplitude threshold; If the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold, mark the analysis subunit as a high-change analysis subunit; Sum up the operating temperature amplitude values of all high-change analysis subunits to obtain the device score.

6. The data analysis and management method based on cloud-edge collaboration according to claim 5, characterized in that The process of obtaining the operating temperature amplitude value of the analysis subunit is as follows: Obtain the operating temperature curve, extract the peak points and valley points in the operating temperature curve, take the adjacent peak points and valley points as an analysis subunit, obtain the coordinates corresponding to the peak points of the analysis subunit, and the coordinates corresponding to two adjacent valley points, and mark them as the set temperature peak point coordinates and the set temperature valley point coordinates respectively. Calculate the set temperature width amplitude and the set temperature vertical amplitude through the set temperature peak point coordinates and the set temperature valley point coordinates; Take the ratio of the set temperature vertical amplitude to the set temperature width amplitude to obtain the operating temperature amplitude value of the analysis subunit.

7. A data analysis and management method based on cloud-edge collaboration according to claim 6, characterized in that The process of judging whether the device has risks is as follows: If the device score is greater than or equal to the device score threshold, generate a high-risk signal of device change; If the device score is less than the device score threshold, generate a low-risk signal of device change.

8. A data analysis and management system based on cloud-edge collaboration, characterized in that, This system is used to execute the method described in any one of claims 1-7 above. The system includes: Edge-side environmental assessment module: Deploy multiple edge nodes, obtain the environmental data during the device production process, process the environmental data of the device to obtain the environmental score, and judge whether there are risks in the production environment of the device; Among them, the environmental data includes the environmental temperature value; Edge-side device assessment module: Deploy multiple edge nodes, obtain the device operation data, process the device operation data to obtain the device score, and judge whether the device has risks; Among them, the operation data includes the device operation temperature; Cloud-side association assessment module: Send the environmental change risk signal and the device change risk signal analyzed by the edge node to the cloud, evaluate their linkage risks, and obtain a high-degree signal of the same frequency; Cloud-side risk warning module: When obtaining a high-degree signal of the same frequency, determine the production warning time of the device.

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