Enterprise information data management early warning method and system

Abstract

The invention relates to the field of data transmission, in particular to an enterprise information data management early warning method and system, and the method comprises the steps: determining the type of associated equipment according to whether to-be-uploaded data of the associated equipment is in a sensitive state or not; determining a transmission adjustment mode of the corresponding processing node according to the proportion of the first type of equipment of each processing node; the transmission adjustment mode comprises transmission task scheduling for the processing node or data optimization for to-be-uploaded data of each type of equipment; determining a task scheduling mode according to the distribution state of the first type of equipment of the processing nodes, wherein the task scheduling mode comprises determining a target scheduling node according to the aggregation area proportion of each processing node or the number of similar equipment; determining whether to perform preferable equipment screening according to whether the similar equipment set exists or not; and completing the transmission of the to-be-uploaded data according to the task scheduling result and the data optimization result. According to the invention, the security of the equipment data in the transmission process is improved.

Description

Technical Field

[0001] The present invention relates to the field of data transmission, and in particular to an enterprise information data management early warning method and system. Background Art

[0002] The photovoltaic data of power companies contains a large amount of sensitive information, such as equipment status, energy output, and equipment distribution. Information leakage during the transmission process will have a serious impact on the safety and stability of photovoltaic power stations. Data management and early warning can ensure data security and thus protect corporate interests. However, the existing data management and early warning methods often fail to take into account equipment data leakage and targeted protection of equipment layout data. Therefore, how to ensure the confidentiality of equipment-related data of power companies is an urgent problem to be solved by technical personnel in this field.

[0003] Chinese patent publication number CN118157958A discloses a distributed photovoltaic data transmission system and method, which includes: a terminal collection layer, a federated learning layer and a blockchain network layer, the terminal collection layer is used to collect distributed photovoltaic data; the blockchain network layer is used to receive a data transmission request sent by a data requester, and verify whether the data transmission request is valid. When the request is valid, according to the data type and communication requirements of the data transmission request, the registration information and historical transmission and transaction information of each terminal node of the blockchain network are retrieved, and according to the reputation and privacy level of each relevant node in the relevant node set, a federated learning pool in the federated learning layer is selected from the relevant nodes, and a data transmission request is sent to the federated learning pool; the federated learning layer responds to the data transmission request, controls each federated node in the federated learning pool to send the distributed photovoltaic data to the data requester according to the transmission rules, and the above scheme has the following problems: it is impossible to determine a targeted transmission adjustment method according to the proportion of devices with sensitive information in the data to be uploaded in the actual working scenario, which makes it easy to leak the layout of the photovoltaic equipment in the data of a single transmission, and then leads to low security of the device data during the transmission process. Summary of the invention

[0004] To this end, the present invention provides an enterprise information data management early warning method and system to overcome the problem in the prior art that it is impossible to determine a targeted transmission adjustment method according to the proportion of devices containing sensitive information in the data to be uploaded in the actual working scenario, which leads to the layout of the photovoltaic equipment being easily leaked in the data of a single transmission, and further leads to low security of the device data during the transmission process.

[0005] To achieve the above object, the present invention provides an enterprise information data management early warning method, comprising:

[0006] Obtaining the data to be uploaded of each associated device, and determining the category of the associated device according to whether the data to be uploaded of each associated device is in a sensitive state;

[0007] Determine a transmission adjustment method corresponding to each processing node according to the proportion of the first type of devices in each processing node, wherein the transmission adjustment method includes scheduling transmission tasks for the processing node or optimizing data to be uploaded for each first type of device;

[0008] If transmission task scheduling is performed, the task scheduling method is determined according to the distribution status of a type of equipment at the processing node, and the task scheduling method includes determining the target scheduling node according to the proportion of the clustered area of ​​each processing node or the number of similar equipment;

[0009] If data optimization is performed, whether to perform optimal device screening is determined based on whether there is a similar device set at each processing node;

[0010] Complete the transmission of the data to be uploaded based on the task scheduling results and data optimization results.

[0011] Furthermore, it is periodically detected whether the data to be uploaded of each associated device of each processing node is in a sensitive state, and the category of the associated device is determined according to whether the data to be uploaded of each associated device is in a sensitive state. If the data to be uploaded is in a sensitive state, the corresponding associated device is recorded as a Class I device.

[0012] Further, the sensitive state is determined according to whether the data to be uploaded by the associated device contains location information and performance information;

[0013] If the data to be uploaded of the associated device includes location information or performance information, it is determined that the data to be uploaded of the associated device is in a sensitive state.

[0014] Furthermore, for a single processing node, if the proportion of a type of equipment in the processing node is greater than a preset proportion, it is determined that transmission tasks are to be scheduled for the processing node, and the task scheduling method is determined according to the distribution status of the type of equipment in the processing node;

[0015] The task scheduling method includes performing task scheduling for data to be uploaded corresponding to a type of device in the cluster area and determining whether to perform task scheduling for data to be uploaded for the type of device according to the performance similarity between the type of device and the associated device in the corresponding processing node;

[0016] The distribution state is determined according to the number of devices of the first category within a preset range of each device of the first category, and the distribution state includes a clustered state and a discrete state.

[0017] Furthermore, for a single Class I device, if the Class I device is in a clustered state, task scheduling is performed for the data to be uploaded corresponding to the Class I device in the clustered area where the Class I device is located, and a number of target scheduling nodes are determined according to the proportion of the clustered area of ​​each processing node;

[0018] For any type of equipment that needs to be scheduled, determine the target scheduling node corresponding to the type of equipment according to the number of similar devices in the associated devices of each processing node;

[0019] The number of the target scheduling nodes is positively correlated with the number of a type of equipment in the aggregation area;

[0020] The gathering area is a collection of preset ranges of each type of equipment with overlapping preset ranges, and the preset range of any type of equipment in the gathering area intersects with the preset range of at least one type of equipment.

[0021] Further, for a single type-one device, if the type-one device is in a discrete state, it is determined whether to perform task scheduling for the data to be uploaded of the type-one device according to a performance similarity reference value between the type-one device and the type-one device in the corresponding processing node;

[0022] If the performance similarity reference value of the device of this type is greater than the preset performance similarity reference value, task scheduling is performed for the data to be uploaded by the device of this type, and the target scheduling node of the device of this type is determined according to the amount of data to be transmitted in the current cycle of each processing node.

[0023] Furthermore, for a single processing node, if the proportion of a type of equipment in the processing node is less than or equal to a preset proportion, data optimization is performed on the data to be uploaded for each type of equipment, and whether to perform preferred equipment screening is determined based on whether there is a similar device set;

[0024] The similar device set is determined according to the device spacing distance and performance similarity.

[0025] Further, if there is a similar device set in a class of devices of the processing node, a preset preferred number of preferred devices is determined according to the preferred transmission coefficient of each class of devices in the similar device set, and in the current data transmission cycle, for a class of devices in the similar device set, only the data to be uploaded of the preferred devices is transmitted;

[0026] The preferred transmission coefficient is determined according to a position difference reference value and an operation data change value.

[0027] Further, for the data to be uploaded corresponding to a type of device other than the similar device set of each processing node, a delayed transmission time of the corresponding data to be uploaded is set according to the change value of the operation data of each type of device;

[0028] The duration of the delayed transmission time is negatively correlated with the change value of the operation data of each type of equipment.

[0029] The present invention also provides an enterprise information data management early warning system, comprising:

[0030] A data monitoring module is used to periodically detect whether the data to be uploaded of each associated device of each processing node is in a sensitive state, and determine the category of the associated device;

[0031] A transmission analysis module, which is connected to the data monitoring module and is used to determine the transmission adjustment method of the corresponding processing node according to the proportion of a type of equipment in each processing node;

[0032] A task scheduling module, which is connected to the transmission analysis module and is used to determine a task scheduling method according to a distribution state of a class of devices of a processing node;

[0033] A data optimization module, which is connected to the transmission analysis module and is used to determine whether to perform a preferred device screening according to whether a similar device set exists;

[0034] The data transmission module is connected to the task scheduling module and the data optimization module respectively, and is used to complete the transmission of the data to be uploaded according to the task scheduling result and the data optimization result.

[0035] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the category of the associated device is determined according to whether the data to be uploaded of each associated device is in a sensitive state, and the transmission adjustment method of the corresponding processing node is determined according to the proportion of a type of equipment in each processing node, so that the selected transmission method is consistent with the actual working scenario, while ensuring the security of the data transmission process, the data transmission efficiency is also guaranteed. The present invention improves the security of device data during the transmission process.

[0036] Furthermore, the present invention determines the task scheduling method according to the distribution status of a type of equipment of the processing node. If a type of equipment is in a clustered state, task scheduling is performed for the data to be uploaded corresponding to the type of equipment in the clustered area where the type of equipment is located. This can ensure that the position correlation between the device data transmitted at a single time by the processing node is low, so as to avoid the leakage of the location distribution of the photovoltaic equipment due to the leakage of the device data. The present invention improves the security of the device data during the transmission process.

[0037] Furthermore, when a type of device is in a discrete state, the present invention determines whether to perform task scheduling for the data to be uploaded of the type of device based on the performance similarity between the type of device and the associated devices in the corresponding processing node, thereby ensuring that the device performance correlation between the device data transmitted at a single time by the target scheduling node after task scheduling is low, thereby avoiding the leakage of the performance of devices of the same model due to device data leakage. The present invention improves the security of device data during the transmission process.

[0038] Furthermore, whether to perform preferred device screening is determined based on whether there is a similar device set. If there is a similar device set, a preset preferred number of preferred devices is determined based on the preferred transmission coefficients of each type of device in the similar device set. This can reduce data transmission content and improve data transmission efficiency while avoiding leakage of device layout. For data to be uploaded corresponding to a type of device other than the similar device set, a delayed transmission time for the corresponding data to be uploaded is set based on the performance change value of each type of device. This can ensure real-time transmission of abnormal data while ensuring data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the enterprise information data management early warning method of the present invention;

[0040] Figure 2 A flow chart of the present invention for determining a transmission adjustment method of a corresponding processing node according to a proportion of a type of equipment in each processing node;

[0041] Figure 3 It is a flow chart of determining a task scheduling method according to a distribution state of a class of devices of a processing node according to the present invention;

[0042] Figure 4 This is a module connection diagram of the enterprise information data management early warning system of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0045] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0046] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] See also Figures 1 to 3 As shown, the present invention provides an enterprise information data management early warning method, comprising:

[0048] Obtaining the data to be uploaded of each associated device, and determining the category of the associated device according to whether the data to be uploaded of each associated device is in a sensitive state;

[0049] Determine a transmission adjustment method corresponding to each processing node according to the proportion of the first type of devices in each processing node, wherein the transmission adjustment method includes scheduling transmission tasks for the processing node or optimizing data to be uploaded for each first type of device;

[0050] If transmission task scheduling is performed, the task scheduling method is determined according to the distribution status of a type of equipment at the processing node, and the task scheduling method includes determining the target scheduling node according to the proportion of the clustered area of ​​each processing node or the number of similar equipment;

[0051] If data optimization is performed, whether to perform optimal device screening is determined based on whether there is a similar device set at each processing node;

[0052] Complete the transmission of the data to be uploaded based on the task scheduling results and data optimization results.

[0053] Among them, the application of the present invention is provided with several photovoltaic equipment areas, any photovoltaic equipment area is correspondingly provided with a processing node and several photovoltaic equipment, the photovoltaic equipment of the photovoltaic equipment area corresponding to each processing node is the associated equipment of the processing node, each processing node in the present invention periodically sends equipment data to the user, and the equipment data that each associated equipment needs to send to the user in a single cycle is the data to be uploaded of the corresponding associated equipment, and the data to be uploaded is various types of equipment information of the associated equipment of each associated equipment in a data transmission cycle, and the content of the equipment information is not limited to the photoelectric conversion efficiency of the equipment, the filling factor, the power generation of the equipment, the setting position and the setting angle;

[0054] The present invention applies a cyclic data transmission cycle, and the duration of the data transmission cycle can be determined by the user. The higher the real-time requirement of the user for data transmission, the shorter the duration of the data transmission cycle. A data transmission cycle duration is provided, and the data transmission cycle is 15 minutes. At the end of each data transmission cycle, the data monitoring module analyzes the data to be uploaded of each associated device of each processing node to determine the category of the associated device. The transmission analysis module adjusts the transmission process according to the proportion of a type of equipment, and transmits the data to be uploaded of each photovoltaic device in the current data transmission cycle. After completing task scheduling and data optimization, the data transmission module transmits the data to be uploaded of each photovoltaic device in the data transmission cycle.

[0055] Specifically, it is periodically detected whether the data to be uploaded of each associated device of each processing node is in a sensitive state, and the category of the associated device is determined according to whether the data to be uploaded of each associated device is in a sensitive state. If the data to be uploaded is in a sensitive state, the corresponding associated device is recorded as a Class I device.

[0056] Specifically, the sensitive state is determined according to whether the data to be uploaded by the associated device contains location information and performance information;

[0057] If the data to be uploaded of the associated device includes location information or performance information, it is determined that the data to be uploaded of the associated device is in a sensitive state.

[0058] Among them, the location information includes but is not limited to the setting position, setting angle and orientation of the photovoltaic equipment, and the performance information includes but is not limited to the conversion efficiency, fill factor and irradiance response of the photovoltaic equipment. Users can adaptively set the location information and performance information according to actual needs.

[0059] Specifically, for a single processing node, if the proportion of a type of equipment in the processing node is greater than a preset proportion, it is determined that the transmission task is scheduled for the processing node, and the task scheduling method is determined according to the distribution status of the type of equipment in the processing node;

[0060] The task scheduling method includes performing task scheduling for data to be uploaded corresponding to a type of device in the cluster area and determining whether to perform task scheduling for data to be uploaded for the type of device according to the performance similarity between the type of device and the associated device in the corresponding processing node;

[0061] The distribution state is determined according to the number of devices of the first category within a preset range of each device of the first category, and the distribution state includes a clustered state and a discrete state.

[0062] Among them, for a single processing node, the proportion of a type of equipment corresponding to it is the ratio of the number of a type of equipment corresponding to the processing node to the number of associated devices. The value of the preset proportion can be set by the user according to actual needs and historical records. The higher the user's requirements for the device data security of the data transmission process, the smaller the value of the preset proportion. A method for setting the value of the preset proportion is provided, and the historical records without task scheduling are recorded as reference records. In the reference records that meet the user's requirements for the device data security of the data transmission process, the proportion of a type of equipment of each processing node is recorded as the preset proportion. In this embodiment, the preferred value of the preset proportion is 30%;

[0063] The preset range is a circular area with the location of each type of equipment as the center and the preset area length as the radius. The value of the preset area length can be set by the user according to actual needs and historical records. The higher the user's requirements for the device data security during the data transmission process, the larger the preset area length. A method for setting the value of the preset area length is provided, and the average value of the interval distances of each type of equipment in the clustered area in the historical records that meet the user's requirements for the device data security during the data transmission process is recorded as the preset area length;

[0064] If the number of a class of devices within the preset range of a class of devices is greater than the preset number of a class of devices, the class of devices is in a clustered state; if the number of a class of devices within the preset range of a class of devices is less than or equal to the preset number of a class of devices, the class of devices is in a discrete state. The value of the preset number of a class of devices is set by the user according to actual needs and historical records. The higher the user's requirements for device data security during the data transmission process, the smaller the preset number of a class of devices is. A value of the preset number of a class of devices is provided, and the value of the preset number of a class of devices is 30% of the average value of the number of associated devices within each preset range.

[0065] Specifically, for a single Class I device, if the Class I device is in a clustered state, task scheduling is performed for the data to be uploaded corresponding to the Class I device in the clustered area where the Class I device is located, and a number of target scheduling nodes are determined according to the proportion of the clustered area of ​​each processing node;

[0066] For any type of equipment that needs to be scheduled, determine the target scheduling node corresponding to the type of equipment according to the number of similar devices in the associated devices of each processing node;

[0067] The number of the target scheduling nodes is positively correlated with the number of a type of equipment in the aggregation area;

[0068] The gathering area is a collection of preset ranges of each type of equipment with overlapping preset ranges, and the preset range of any type of equipment in the gathering area intersects with the preset range of at least one type of equipment.

[0069] Among them, the proportion of clustered areas = the area of ​​the clustered areas in the photovoltaic equipment area corresponding to each processing node / the area of ​​the photovoltaic equipment area corresponding to the corresponding processing node. Since all processing nodes with clustered areas need to be scheduled, selecting processing nodes with a larger proportion of clustered areas can avoid the situation where the processing nodes have too heavy data transmission tasks after task scheduling, resulting in low data transmission efficiency. Therefore, processing nodes with a larger proportion of clustered areas are given priority to become target scheduling nodes;

[0070] For any device of a class that needs to be scheduled, the similar device is specifically the device of the class. For a single device of the class, if the device of the class is in a discrete state, whether to schedule tasks for the data to be uploaded of the device of the class is determined based on the performance similarity reference value of the device of the class and the device of the class in the corresponding processing node;

[0071] If the performance similarity reference value of the device of this type is greater than the preset performance similarity reference value, task scheduling is performed for the data to be uploaded by the device of this type, and the target scheduling node of the device of this type is determined according to the amount of data to be transmitted in the current cycle of each processing node.

[0072] Among them, the performance similarity reference value is the average value of the performance similarities of a class of equipment and each class of equipment in its corresponding processing node. The user can set the value of the preset performance similarity reference value according to actual needs and historical records. The higher the user's requirements for device data security in the data transmission process, the smaller the value of the preset performance similarity reference value. In this embodiment, a method for determining the preset performance similarity reference value is provided. The average value of the performance similarities of each class of equipment when each processing node performs data transmission in the historical records that meet the user's requirements for device data security in the data transmission process is recorded as the preset performance similarity reference value. The amount of data to be transmitted is the sum of the amount of data to be uploaded that each processing node needs to transmit.

[0073] Specifically, for a single processing node, if the proportion of a type of equipment in the processing node is less than or equal to the preset proportion, data optimization is performed on the data to be uploaded for each type of equipment, and whether to perform preferred equipment screening is determined based on whether there is a similar device set;

[0074] The similar device set is determined according to the device spacing distance and performance similarity.

[0075] The performance similarity of any two devices of the same category in the similar device set is greater than the preset performance similarity and the device spacing distance between any two adjacent devices of the same category is less than or equal to the preset device spacing distance. The performance similarity is determined according to the conversion efficiency difference value and the fill factor difference value. The performance similarity X=e h +e t, h is the conversion efficiency difference value, t is the fill factor difference value, the conversion efficiency difference value is the absolute value of the difference in photoelectric conversion efficiency of the two photovoltaic devices, the fill factor difference value is the absolute value of the difference in fill factors of the two photovoltaic devices, and the fill factor is the ratio of the maximum power of the battery of the photovoltaic device to the product of the open circuit voltage and the short circuit current;

[0076] The user can set the values ​​of the preset performance similarity and the preset device spacing distance according to actual needs and historical records. The higher the user's requirements for the integrity of the optimized device data, the larger the value of the preset performance similarity and the smaller the preset device spacing distance. A method for setting the value of the preset performance similarity is provided, and the average value of the performance similarities in each similar device set in the historical records that meet the user's requirements for the integrity of the optimized device data is recorded as the preset performance similarity. A method for setting the value of the preset device spacing distance is provided, and the average value of the device spacing distances of each adjacent type of device in each similar device set in the historical records that meet the user's requirements for the integrity of the optimized device data is recorded as the preset device spacing distance.

[0077] Specifically, if there is a similar device set in a class of devices of the processing node, a preset preferred number of preferred devices is determined according to the preferred transmission coefficient of each class of devices in the similar device set, and in the current data transmission cycle, for a class of devices in the similar device set, only the data to be uploaded of the preferred devices is transmitted;

[0078] The preferred transmission coefficient is determined according to a position difference reference value and an operation data change value.

[0079] The preferred transmission coefficient is the natural logarithm of the ratio of the operation data change value to the position difference reference value, the operation data change value is the absolute value of the difference between the power generation of each type of equipment in the current cycle and the power generation of the equipment in the previous cycle, and the position difference reference value of each type of equipment is the average value of the interval distance between each type of equipment and other types of equipment in the set of similar equipment to which it belongs;

[0080] A type of device with a larger preferred transmission coefficient is selected as the preferred device. The user can set the value of the preset preferred number according to actual needs. The higher the user's requirements for device data security in the data transmission process, the smaller the value of the preset preferred number is. A value of the preset preferred number is provided, and the value of the preset preferred number is 10% of the number of devices in a type within a set of similar devices.

[0081] Specifically, for the data to be uploaded corresponding to a type of device other than the similar device set of each processing node, the delayed transmission time of the corresponding data to be uploaded is set according to the change value of the operation data of each type of device;

[0082] The duration of the delayed transmission time is negatively correlated with the change value of the operation data of each type of equipment.

[0083] Among them, if there is no similar device set in a type of equipment of the processing node, or there is still a type of equipment in the processing node except the similar device set, the data to be uploaded of the type of equipment that has not been optimized for data is delayed in transmission, and the data to be uploaded is adjusted to a subsequent data transmission cycle for data transmission, and the delayed transmission time is the time interval between the end time of the current data transmission cycle and the start time of the data transmission cycle for data transmission after adjustment.

[0084] See also Figure 4 As shown, it is a module connection diagram of the enterprise information data management early warning system of the present invention. The present invention also provides an enterprise information data management early warning system, including:

[0085] A data monitoring module is used to periodically detect whether the data to be uploaded of each associated device of each processing node is in a sensitive state, and determine the category of the associated device;

[0086] A transmission analysis module, which is connected to the data monitoring module and is used to determine the transmission adjustment method of the corresponding processing node according to the proportion of a type of equipment in each processing node;

[0087] A task scheduling module, which is connected to the transmission analysis module and is used to determine a task scheduling method according to a distribution state of a class of devices of a processing node;

[0088] A data optimization module, which is connected to the transmission analysis module and is used to determine whether to perform a preferred device screening according to whether a similar device set exists;

[0089] The data transmission module is connected to the task scheduling module and the data optimization module respectively, and is used to complete the transmission of the data to be uploaded according to the task scheduling result and the data optimization result.

[0090] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An enterprise information data management early warning method, characterized in that: include: Obtaining the data to be uploaded of each associated device, and determining the category of the associated device according to whether the data to be uploaded of each associated device is in a sensitive state; Determine a transmission adjustment method corresponding to each processing node according to the proportion of the first type of devices in each processing node, wherein the transmission adjustment method includes scheduling transmission tasks for the processing node or optimizing data to be uploaded for each first type of device; If transmission task scheduling is performed, the task scheduling method is determined according to the distribution status of a type of equipment at the processing node, and the task scheduling method includes determining the target scheduling node according to the proportion of the clustered area of ​​each processing node or the number of similar equipment; If data optimization is performed, whether to perform optimal device screening is determined based on whether there is a similar device set at each processing node; Complete the transmission of the data to be uploaded based on the task scheduling results and data optimization results.

2. The enterprise information data management early warning method according to claim 1 is characterized in that: Periodically detect whether the data to be uploaded of each associated device of each processing node is in a sensitive state, and determine the category of the associated device based on whether the data to be uploaded of each associated device is in a sensitive state. If the data to be uploaded is in a sensitive state, the corresponding associated device is recorded as a Class I device.

3. The enterprise information data management early warning method according to claim 2 is characterized in that: The sensitive state is determined according to whether the data to be uploaded by the associated device contains location information and performance information; If the data to be uploaded of the associated device includes location information or performance information, it is determined that the data to be uploaded of the associated device is in a sensitive state.

4. The enterprise information data management early warning method according to claim 3 is characterized in that: For a single processing node, if the proportion of a type of equipment in the processing node is greater than a preset proportion, it is determined that the transmission task is scheduled for the processing node, and the task scheduling method is determined according to the distribution status of the type of equipment in the processing node; The task scheduling method includes performing task scheduling for data to be uploaded corresponding to a type of device in the cluster area and determining whether to perform task scheduling for data to be uploaded for the type of device according to the performance similarity between the type of device and the associated device in the corresponding processing node; The distribution state is determined according to the number of devices of the first category within a preset range of each device of the first category, and the distribution state includes a clustered state and a discrete state.

5. The enterprise information data management early warning method according to claim 4 is characterized in that: For a single Class I device, if the Class I device is in a clustered state, task scheduling is performed for the data to be uploaded corresponding to the Class I device in the clustered area where the Class I device is located, and a number of target scheduling nodes are determined according to the proportion of the clustered area of ​​each processing node; For any type of equipment that needs to be scheduled, determine the target scheduling node corresponding to the type of equipment according to the number of similar devices in the associated devices of each processing node; The number of the target scheduling nodes is positively correlated with the number of a type of equipment in the aggregation area; The gathering area is a collection of preset ranges of each type of equipment with overlapping preset ranges, and the preset range of any type of equipment in the gathering area intersects with the preset range of at least one type of equipment.

6. The enterprise information data management early warning method according to claim 5, characterized in that: For a single Class I device, if the Class I device is in a discrete state, determine whether to perform task scheduling for the data to be uploaded for the Class I device based on the performance similarity reference value between the Class I device and the Class I device in the corresponding processing node; If the performance similarity reference value of the device of this type is greater than the preset performance similarity reference value, task scheduling is performed for the data to be uploaded by the device of this type, and the target scheduling node of the device of this type is determined according to the amount of data to be transmitted in the current cycle of each processing node.

7. The enterprise information data management early warning method according to claim 6, characterized in that: For a single processing node, if the proportion of a type of equipment in the processing node is less than or equal to the preset proportion, data optimization is performed on the data to be uploaded for each type of equipment, and whether to perform preferred equipment screening is determined based on whether there is a similar device set; The similar device set is determined according to the device spacing distance and performance similarity.

8. The enterprise information data management early warning method according to claim 7, characterized in that: If there is a similar device set in a class of devices of the processing node, a preset preferred number of preferred devices is determined according to the preferred transmission coefficient of each class of devices in the similar device set, and in the current data transmission cycle, for a class of devices in the similar device set, only the data to be uploaded of the preferred devices is transmitted; The preferred transmission coefficient is determined according to a position difference reference value and an operation data change value.

9. The enterprise information data management early warning method according to claim 8, characterized in that: For the data to be uploaded corresponding to a type of device other than the similar device set of each processing node, a delayed transmission time of the corresponding data to be uploaded is set according to the change value of the operation data of each type of device; The duration of the delayed transmission time is negatively correlated with the change value of the operation data of each type of equipment.

10. An early warning system using the enterprise information data management early warning method according to any one of claims 1 to 9, characterized in that: include: A data monitoring module is used to periodically detect whether the data to be uploaded of each associated device of each processing node is in a sensitive state, and determine the category of the associated device; A transmission analysis module, which is connected to the data monitoring module and is used to determine the transmission adjustment method of the corresponding processing node according to the proportion of a type of equipment in each processing node; A task scheduling module, which is connected to the transmission analysis module and is used to determine a task scheduling method according to a distribution state of a class of devices of a processing node; A data optimization module, which is connected to the transmission analysis module and is used to determine whether to perform a preferred device screening according to whether a similar device set exists; The data transmission module is connected to the task scheduling module and the data optimization module respectively, and is used to complete the transmission of the data to be uploaded according to the task scheduling result and the data optimization result.

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