Data analysis management system and method based on cloud edge collaboration

Abstract

The invention relates to the technical field of data analysis and management, in particular to a data analysis and management system and method based on cloud-edge collaboration, and the method comprises the steps: deploying a plurality of edge nodes, obtaining environment data in an equipment production process, processing the environment data of the equipment, obtaining an environment score, and judging whether the production environment of the equipment has a risk or not; deploying a plurality of edge nodes, acquiring equipment operation data, processing the equipment operation data to obtain an equipment score, and judging whether the equipment has a risk or not; sending the environment change risk signal and the equipment change risk signal obtained by analysis of the edge node to a cloud, and evaluating the linkage risk to obtain a same-frequency high-degree signal; when the same-frequency high-degree signal is obtained, the production early warning time of the equipment is determined; according to the invention, timely early warning processing is carried out on production safety, and the risk of equipment production is reduced.

Description

Technical Field

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

[0002] Cloud-edge collaboration combines the elasticity of cloud computing with the real-time nature of edge computing to better meet the needs of different scenarios. Cloud-edge collaboration allows IoT devices to better connect to cloud applications and services, enabling remote monitoring and management of devices.

[0003] The data analysis and management methods involved in traditional equipment production often rely on manual inspections and simple automated monitoring systems, but this method has 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 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: A data analysis management method based on cloud-edge collaboration includes the following steps: 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 signals and equipment change risk signals analyzed by the edge nodes are sent to the cloud to evaluate their linkage risks and obtain high-level signals of the same frequency; When a high-level signal of the same frequency is obtained, the production warning time of the equipment is determined.

[0006] As a further technical solution of the present invention: the process of obtaining the environmental score is: 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.

[0007] As a further technical solution of the present invention: 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.

[0008] As a further technical solution of the present invention: the process of judging whether there is risk 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.

[0009] As a further technical solution of the present invention: the process of obtaining the device score is: comparing the operating temperature amplitude value of the analysis subunit with an operating temperature amplitude threshold value; If the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold, the analysis subunit is marked as a high-variation analysis subunit; The operating temperature amplitude values ​​of all high-variability analysis subunits are summed up to obtain the equipment score.

[0010] As a further technical solution of the present invention: the process of obtaining the operating temperature amplitude value of the analysis subunit is: Obtain the operating temperature curve, and extract the peak points and trough points in the operating 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 of the set temperature is compared with the width of the set temperature to obtain the operating temperature amplitude of the analysis subunit.

[0011] As a further technical solution of the present invention: the process of judging whether the equipment has risks is as follows: If the device score is greater than or equal to the device score threshold, a high-risk signal for device change is generated; If the device score is less than the device score threshold, a low risk signal of device change is generated.

[0012] As a further technical solution of the present invention: the process of acquiring the high-level signal with the same frequency is as follows: If a high-risk signal of an environmental change and a high-risk signal of an equipment change, or a low-risk signal of an environmental change and a low-risk signal of an equipment change 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 consistency value. If the frequency consistency value is less than the frequency consistency threshold, a frequency consistency high degree signal is generated.

[0013] As a further technical solution of the present invention: the process of determining the production warning time is: 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; 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 impact analysis sub-unit proportion threshold, the ambient temperature impact time differences of all impact analysis sub-units are averaged to obtain the warning time.

[0014] A data analysis and management system based on cloud-edge collaboration, the system comprising: 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; Wherein, the environmental data includes an environmental temperature value; 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; Among them, the operating data includes the operating temperature of the equipment; 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; 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.

[0015] Beneficial effects of the present invention: (1) The present invention obtains environmental data during the equipment production process, processes the environmental data, obtains an environmental score, and determines whether there is a risk in the production environment; obtains equipment operation data, processes the equipment operation data, obtains an equipment score, and determines whether there is a risk in the equipment; the present invention independently analyzes and evaluates the environment and equipment in the production process through edge processing, thereby achieving a more comprehensive safety assessment of the production process. Furthermore, by performing intelligent analysis from two aspects, temperature change time and temperature change value, a more accurate safety assessment of the production process is achieved; (2) The present invention evaluates the linkage risk based on the generated environmental change risk signal and equipment change risk signal to obtain the same-frequency high-degree signal. When the same-frequency high-degree signal is obtained, the production warning time of the equipment is determined. During the production process, the present invention evaluates the risk correlation between the environment and the equipment, further realizing the comprehensiveness of the safety assessment in the production process, and based on the results of intelligent analysis, the warning time can be determined in stages, thereby carrying out timely warning processing for production safety and reducing the risk of equipment production. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 is a flowchart of Embodiment 1 of the present invention; Figure 2 is a flowchart of Embodiment 2 of the present invention; Figure 3 It is a system block diagram of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 As shown, the present invention is a data analysis management method based on cloud-edge collaboration, comprising the following steps: S1: 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; In some implementation schemes, temperature sensors are deployed at multiple edge nodes of the target device, and during the monitoring period, the temperature sensors collect the ambient temperature values ​​produced by the device in real time according to a time series (e.g., every minute); With time as the X-axis and ambient temperature as the Y-axis, a two-dimensional coordinate system of time-ambient temperature is constructed. The ambient temperature value collected in real time is substituted into the two-dimensional coordinate system, and an ambient temperature curve is drawn. The ambient temperature curve is analyzed to determine the degree of ambient temperature change. Specifically, the process of determining the degree of ambient temperature change includes the following steps: 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 points of the analysis subunit, and the coordinates corresponding to the two adjacent trough points, and mark them as the set temperature peak point coordinates and the set temperature trough point coordinates, respectively, 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, and compare the set temperature vertical amplitude with the set temperature width to obtain the ambient temperature amplitude value of the analysis subunit; 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, it means that the ambient temperature corresponding to the analysis subunit has a large change, and the analysis subunit is marked as a high-change analysis subunit; If the ambient temperature amplitude value is less than the ambient temperature amplitude threshold, it means that the ambient temperature corresponding to the analysis subunit has a small change, and the analysis subunit is marked as a low-change analysis subunit; The ambient temperature amplitude values ​​of all high-variation analysis sub-units are summed up to obtain the ambient score; comparing the environmental score to an environmental score threshold; If the environmental score is greater than or equal to the environmental score threshold, it means that during the monitoring period, the environmental temperature changes greatly during the production process (comprehensively evaluated from the temperature change time and temperature change value), and exceeds the preset environmental temperature conditions required for production, which will cause great interference to production and affect the normal operation of production. There is a great production safety risk, and a high-risk signal for environmental changes is generated; If the environmental score is less than the environmental score threshold, it means that during the monitoring period, the degree of change in ambient temperature during the production process is small (comprehensively evaluated from the temperature change time and temperature change value), and does not exceed the preset ambient temperature conditions required for production. It will not cause major interference to production, will not affect the normal operation of production, and will not pose a major production safety risk, generating a low-risk signal for environmental change; In a first preferred embodiment, the calculation process of the temperature width is as follows: Obtain the horizontal coordinate of the set temperature valley point coordinate, calculate the difference between the horizontal coordinates of the two set temperature valley points adjacent to the wave peak point, and obtain the set temperature width; In a second preferred embodiment, the calculation process of the vertical amplitude of the temperature setting is: Obtain the ordinate in the coordinates of the set temperature peak point, perform difference calculation with the ordinates of the two set temperature valley points adjacent to the wave peak point, obtain two adjacent set temperature vertical amplitude values, and then perform average calculation to obtain the set temperature vertical amplitude value; S2: 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; In some implementation schemes, temperature sensors are deployed at multiple other edge nodes of the target device, and during the monitoring period, the operating temperature value of the device is collected in real time by the temperature sensors in a time series (e.g., every minute); With time as the X-axis and operating temperature as the Y-axis, a two-dimensional coordinate system of time-operating temperature is constructed. The operating temperature values ​​collected in real time are substituted into the two-dimensional coordinate system, and an operating temperature curve is drawn. The operating temperature curve is analyzed to determine the degree of change of the operating temperature. Specifically, the process of determining the degree of change of the operating temperature includes the following steps: Obtain the operating temperature curve, and extract the peak points and trough points in the operating 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 the two adjacent trough points, and mark them as the set temperature peak point coordinates and the set temperature trough point coordinates, respectively, 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, and compare the set temperature vertical amplitude with the set temperature width to obtain the operating temperature amplitude value of the analysis subunit; comparing the operating temperature amplitude value of the analysis subunit with an operating temperature amplitude threshold value; If the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold, it means that the operating temperature corresponding to the analysis subunit has a large change, and the analysis subunit is marked as a high-change analysis subunit; If the operating temperature amplitude value is less than the operating temperature amplitude threshold, it means that the operating temperature corresponding to the analysis subunit has a small change, and the analysis subunit is marked as a low-change analysis subunit; The operating temperature amplitude values ​​of all high-variability analysis subunits are summed up to obtain the equipment score; comparing the device score to a device score threshold; If the equipment score is greater than or equal to the equipment score threshold, it means that during the monitoring period, the operating temperature in the production process changes greatly (comprehensively evaluated from the temperature change time and temperature change value), and exceeds the preset operating temperature conditions required for production, which will cause great interference to production and affect the normal operation of production. There is a great production safety risk, and a high-risk signal for equipment change is generated; If the equipment score is less than the equipment score threshold, it means that during the monitoring period, the operating temperature change in the production process is small (comprehensively evaluated from the temperature change time and temperature change value), and does not exceed the preset operating temperature conditions required for production. It will not cause major interference to production, will not affect the normal operation of production, and will not pose a major production safety risk, generating a low risk signal for equipment change; In a first preferred embodiment, the calculation process of the temperature width is as follows: Obtain the horizontal coordinate of the set temperature valley point coordinate, calculate the difference between the horizontal coordinates of the two set temperature valley points adjacent to the wave peak point, and obtain the set temperature width; In a second preferred embodiment, the calculation process of the vertical amplitude of the temperature setting is: Obtain the ordinate in the coordinates of the set temperature peak point, perform difference calculation with the ordinates of the two set temperature valley points adjacent to the wave peak point, obtain two adjacent set temperature vertical amplitude values, and then perform average calculation to obtain the set temperature vertical amplitude value; The technical solution of the embodiment of the present invention is as follows: environmental data of the equipment during the production process is obtained, the environmental data is processed to obtain an environmental score, and whether there is a risk in the production environment is determined; equipment operation data is obtained, the equipment operation data is processed to obtain an equipment score, and whether there is a risk in the equipment is determined; the present invention independently analyzes and evaluates the environment and equipment in the production process through edge processing, thereby achieving a more comprehensive safety assessment of the production process, and further, by performing intelligent analysis from two aspects: temperature change time and temperature change value, a more accurate safety assessment of the production process is achieved.

[0020] Example 2, please refer to Figure 2 As shown, the present invention is a data analysis management method based on cloud-edge collaboration, comprising the following steps: Step 3: Send the environmental change risk signal and equipment change risk signal analyzed by the edge node to the cloud, evaluate their linkage risk, and obtain the same-frequency high-degree signal; In some implementation schemes, the environmental change risk signals and the equipment change risk signals analyzed by the edge node are sent to the cloud, and cross-analysis is performed on the environmental change high risk signals, the environmental change low risk signals, the equipment change high risk signals, and the equipment change low risk signals; If a high-risk signal of an environmental change and a high-risk signal of an equipment change, or a low-risk signal of an environmental change and a low-risk signal of an equipment change are obtained at the same time, a linkage analysis signal is generated; If a high-risk signal of environmental change and a low-risk signal of equipment change are obtained at the same time, or a low-risk signal of environmental change and a high-risk signal of equipment change are obtained, a non-linked analysis signal is generated; It should be explained that the non-linked analysis signal indicates that the temperature change caused by the environment has a risk level that does not match the temperature change produced by the equipment, which means that the current production environment has a small impact on the equipment production. When the linkage analysis signal is obtained, an analysis is performed based on the ambient temperature curve and the operating temperature curve, and a frequency consistency value is calculated, and the frequency consistency value is compared with a frequency consistency threshold; If the same frequency degree value is less than the same frequency degree threshold, a same frequency high degree signal is generated; If the frequency co-occurrence value is greater than or equal to the frequency co-occurrence threshold, a frequency co-occurrence low-level signal is generated; The calculation process of the same frequency degree value is as follows: According to the chronological order, the high change analysis subunits in the operating temperature curve are compared with the high change analysis subunits in the ambient temperature curve one by one (i.e., the high change analysis subunit in the ith operating temperature curve is compared with the high change analysis subunit in the ith ambient temperature curve), and the operating temperature amplitude value of the high change analysis subunit is calculated by ratio with the ambient temperature amplitude value to obtain the high change analysis subunit impact ratio; The variance of all high-variation analysis subunit influence ratios is calculated to obtain the high-variation analysis subunit influence fluctuation value, and the high-variation analysis subunit influence fluctuation value is recorded as the same frequency degree value; What needs to be explained is that when a high-degree signal with the same frequency is obtained, it means that there is a large impact between the ambient temperature change and the operating temperature change during the production process; When a low-level signal with the same frequency is obtained, it means that during the production process, the ambient temperature has little effect on the operating temperature. Therefore, when a low-level signal with the same frequency is obtained, it is necessary to check the risk status of the environment and equipment during the production process; Step 4: When a high-level signal with the same frequency is obtained, the production warning time of the equipment is determined; In some embodiments, 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 they are marked as the minimum valley point of the ambient temperature subunit and the minimum valley point of the set temperature subunit respectively; Calculate the difference between the minimum valley point of the ambient temperature subunit and the minimum valley point of the set temperature subunit to obtain the ambient set temperature influence time difference; Compare the ambient temperature impact time difference with the ambient temperature impact time difference 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; If the ambient temperature impact time difference is less than the ambient temperature impact time difference threshold, a small environmental impact signal is generated; Obtain the number of impact analysis subunits, perform ratio processing on the number of impact analysis subunits and the number of high change analysis subunits, and obtain the proportion of impact analysis subunits; If the proportion of the impact analysis subunits is greater than or equal to the threshold of the proportion of the impact analysis subunits, the average of the ambient temperature impact time difference of all the impact analysis subunits is calculated to obtain the average of the ambient temperature impact time difference, and the average of the ambient temperature impact time difference is marked as the warning time; so that in the production process, if the ambient temperature is abnormal (that is, the ambient temperature is monitored to generate a high change analysis subunit), the temperature of the equipment and the environment can be promptly repaired within the warning time; If the impact analysis sub-unit ratio is less than the impact analysis sub-unit ratio threshold, the risk status of the environment and equipment in the production process shall be checked; The technical solution of the embodiment of the present invention is as follows: based on the generated environment change risk signal and equipment change risk signal, the linkage risk is evaluated to obtain the same-frequency high-degree signal, and when the same-frequency high-degree signal is obtained, the production warning time of the equipment is determined; during the production process, the present invention evaluates the risk correlation degree between the environment and the equipment, further realizes the comprehensiveness of the safety assessment in the production process, and based on the results of the intelligent analysis of Example 1, the warning time can be determined in stages, thereby timely warning processing of production safety is carried out, and the risk of equipment production is reduced.

[0021] Example 3, please refer to Figure 3 As shown, the present invention is a data analysis and management system based on cloud-edge collaboration, including the following modules: 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; Wherein, the environmental data includes an environmental temperature value; 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; Among them, the operating data includes the operating temperature of the equipment; 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; 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.

[0022] 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 signals and equipment change risk signals analyzed by the edge nodes are sent to the cloud to evaluate their linkage risks and obtain high-level signals of the same frequency; When a high-level signal of the same frequency is obtained, the production warning time of the equipment is determined.

2. According to the data analysis and management method based on cloud-edge collaboration of claim 1, it is characterized in that: 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. According to the data analysis and management method based on cloud-edge collaboration according to claim 2, it is 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. According to the data analysis and management method based on cloud-edge collaboration of claim 3, it is 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. According to the data analysis and management method based on cloud-edge collaboration of claim 4, it is characterized in that: The process of obtaining device scores is as follows: comparing the operating temperature amplitude value of the analysis subunit with an operating temperature amplitude threshold value; If the operating temperature amplitude value is greater than or equal to the operating temperature amplitude threshold, the analysis subunit is marked as a high-variation analysis subunit; The operating temperature amplitude values ​​of all high-variability analysis subunits are summed up to obtain the equipment score.

6. The data analysis and management method based on cloud-edge collaboration according to claim 5 is characterized in that: The process of obtaining the operating temperature amplitude value of the analysis subunit is as follows: Obtain the operating temperature curve, and extract the peak points and trough points in the operating 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 of the set temperature is compared with the width of the set temperature to obtain the operating temperature amplitude of the analysis subunit.

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

8. The data analysis and management method based on cloud-edge collaboration according to claim 7 is characterized in that: The process of acquiring high-level signals of the same frequency is as follows: If a high-risk signal of an environmental change and a high-risk signal of an equipment change, or a low-risk signal of an environmental change and a low-risk signal of an equipment change 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 consistency value. If the frequency consistency value is less than the frequency consistency threshold, a frequency consistency high degree signal is generated.

9. The data analysis and management method based on cloud-edge collaboration according to claim 8 is characterized in that: The process of determining the production warning time is as follows: 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; 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 impact analysis sub-unit proportion threshold, the ambient temperature impact time differences of all impact analysis sub-units are averaged to obtain the warning time.

10. A data analysis and management system based on cloud-edge collaboration, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system comprises: 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; Wherein, the environmental data includes an environmental temperature value; 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; Among them, the operating data includes the operating temperature of the equipment; 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; 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.

Images