A data flow method across data spaces

Abstract

The present application relates to the technical field of data transmission management, and more particularly to a data flow circulation method across data spaces, which determines a plurality of data flow circulation spaces by acquiring node parameters of adjacent device nodes on a data transmission path, determines data flow circulation characteristic time periods by determining a plurality of circulation state explicit nodes on traffic variation curves of different data flow circulation spaces, determines whether there is an invalid circulation phenomenon by performing continuity analysis on the data flow circulation characteristic time periods determined according to a first constraint condition, and determines whether to transmit data of the space network cache node to a target device node having an interface mapping relationship according to a second constraint condition by setting a space network cache node between adjacent device nodes. The present application performs associated recognition analysis on the data flow circulation states of adjacent nodes across data spaces, thereby avoiding waste of data flow circulation resources caused by redundant transmission, response delay and other problems.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data transmission management, and in particular to a data flow method across data spaces. BACKGROUND

[0002] With the rapid development of information technology, data flow across data spaces has been widely used in many fields, and data flow across data spaces supports risk assessment and ensures efficient operation of production processes. However, in the process of data transmission, the complexity of adjacent equipment nodes on the data transmission path leads to low efficiency of data flow. Each equipment node may have different transmission capabilities and data processing rules, and the flow fluctuation of data on different branch transmission paths is irregular, resulting in a large amount of invalid flow of data transmission. Some data frequently appear problems such as redundant transmission, waiting delay, etc. in the transmission process, not only waste network bandwidth resources, but also may cause critical data transmission not in time, affecting the normal operation of the whole system. The traditional data flow method is difficult to accurately judge the data flow state, and lacks a targeted processing mechanism, so there is an urgent need for a data flow method across data spaces to ensure efficient and stable data flow in different spaces.

[0003] For example, Chinese patent publication No. CN119473802A discloses a real-time flow monitoring method and platform in a trusted data space, relating to the technical field of data management and security. The method includes: constructing a trusted data space based on data flow demand, and combining a supervision instruction console to monitor data flow in real time, and obtaining real-time monitoring data. Then, risk identification is performed on these monitoring data, the flow risk coefficient is calculated, and compared with the preset risk threshold. When the risk coefficient reaches or exceeds the threshold, the system generates a flow risk warning signal.

[0004] The prior art also has the following problems:

[0005] The prior art does not consider the correlation and identification analysis of the data flow state of adjacent nodes across data spaces, and it is difficult to identify the invalid flow phenomenon of data flow in the space-time dimension of data flow, resulting in waste of data flow resources due to redundant transmission, response delay, etc. SUMMARY

[0006] Therefore, the present application provides a data flow method across data spaces to overcome the problem that the prior art cannot correlate and identify the data flow state of adjacent nodes across data spaces, and it is difficult to identify the invalid flow phenomenon of data flow in the space-time dimension of data flow.

[0007] To achieve the above purpose, the present application provides a data flow method across data spaces, comprising:

[0008] acquire adjacent device nodes on a data transmission path to determine a plurality of data flow spaces;

[0009] draw a flow change curve of data flow of each device node over time, and determine a plurality of flow state explicit nodes on the flow change curve respectively;

[0010] sort the flow state explicit nodes according to a time sequence relationship, determine a plurality of data flow characteristic time periods based on a preset first constraint condition, and analyze continuity of the data flow characteristic time periods to determine whether there is an invalid flow phenomenon between adjacent device nodes;

[0011] the first constraint condition includes a distribution state of the flow state explicit nodes;

[0012] in response to the existence of the invalid flow phenomenon, set a space network cache node between the adjacent device nodes, establish an interface mapping relationship between the space network cache node and the adjacent device nodes, and determine whether to transmit data of the space network cache node to a target device node according to a preset second constraint condition;

[0013] the second constraint condition is a comparison result of total data flow values transmitted between different nodes within a response waiting time.

[0014] Further, the process of determining a plurality of data flow spaces based on the node parameters includes:

[0015] respectively acquire branch transmission paths on a source flow node and a target flow node in adjacent device nodes;

[0016] determine each branch transmission path on the source flow node as a data flow space of the source flow node, and determine each branch transmission path on the target flow node as a data flow space of the target flow node.

[0017] Further, the process of determining a plurality of flow state explicit nodes on the flow change curve includes:

[0018] construct a first flow change curve with data flow of the data flow space on the source flow node as a vertical axis and a preset monitoring period as a horizontal axis, and construct a second flow change curve with data flow of the data flow space on the target flow node as a vertical axis and the preset monitoring period as a horizontal axis;

[0019] respectively determine absolute values of curve slopes of a plurality of data points on the first flow change curve and the second flow change curve;

[0020] determine a data point with an absolute value of a curve slope exceeding a preset slope reference value as a flow state explicit node.

[0021] Further, the process of determining the data flow feature period comprises:

[0022] sequentially taking the flow state explicit node on the first flow change curve as a period start point in time sequence;

[0023] sequentially determining the flow state explicit node on the second flow change curve with the shortest interval time length from the period start point as a period end point in time sequence;

[0024] determining the period composed of the period start point and the period end point as the data flow feature period;

[0025] wherein the period end point of a previous data flow feature period precedes the period start point of a subsequent data flow feature period in time sequence.

[0026] Further, the process of analyzing the continuity of the data flow feature period comprises:

[0027] obtaining the cumulative value of the duration of all data flow feature periods;

[0028] determining the ratio of the cumulative value of the duration to the duration of the monitoring period as a continuity feature representation value.

[0029] Further, the process of comparing the continuity feature representation value with a preset continuity feature representation reference value and determining whether there is an invalid flow phenomenon between adjacent device nodes according to the comparison result comprises:

[0030] if the continuity feature representation value does not exceed the preset continuity feature representation reference value, it is determined that there is an invalid flow phenomenon between adjacent device nodes.

[0031] Further, the process of establishing the interface mapping relationship between the space network cache node and the source device node comprises:

[0032] establishing a first transmission mapping relationship between the space network cache node and the source device node, so that the data transmission of the source device node is transmitted to the space network cache node for caching;

[0033] obtaining the total value of the data flow of the source device node to the space network cache node within the response waiting time.

[0034] Further, the process of establishing the interface mapping relationship between the space network cache node and the target device node comprises:

[0035] establishing a second transmission mapping relationship between the space network cache node and the target device node, so that the space network cache node receives the data request of the target device node;

[0036] Determine the total data flow value corresponding to the data request of the target device node within the response waiting time.

[0037] Further, compare the total data flow value corresponding to the data request with the total data flow value of the spatial network cache node.

[0038] If the total data flow value corresponding to the data request exceeds the total data flow value of the spatial network cache node, determine to transmit the data of the spatial network cache node to the target device node.

[0039] Further, the process of determining the response waiting time comprises:

[0040] Obtain the duration of each data flow characteristic period;

[0041] Calculate the average value of the duration of all data flow characteristic periods;

[0042] Determine the average value of the duration as the response waiting time.

[0043] Compared with the prior art, the beneficial effects of the present application are that the present application determines a plurality of data flow spaces by obtaining the node parameters of adjacent device nodes on the data transmission path, draws the flow change curve of the data flow of different data flow spaces with time, determines a plurality of flow state dominant nodes on the flow change curve, analyzes the continuity of the data flow characteristic period determined according to the first constraint condition to determine whether there is an invalid flow phenomenon, sets a spatial network cache node between adjacent device nodes, and determines whether to transmit the data of the spatial network cache node to the target device node having an interface mapping relationship according to the second constraint condition. The present application correlates and analyzes the data flow state of adjacent nodes across the data space, identifies the invalid flow phenomenon of data flow in the time-space dimension of data flow, and avoids the waste of data flow resources caused by redundant transmission and response delay.

[0044] Further, the present application uses the flow change curve as an important carrier reflecting the data flow state of each branch transmission path, the real-time data flow of the vertical axis and the monitoring period of the horizontal axis, which can intuitively present the dynamic change trend of data in the time dimension, the absolute value of the slope of the curve, which directly reflects the rate of data flow change. The larger the absolute value of the slope is, the more dramatic the increase or decrease of the data flow corresponding to the time is, which represents a significant turning point of the data flow state. The flow state dominant node determined in this way provides an accurate anchor point for analyzing the data flow characteristic period, and realizes the analysis of the data flow state of adjacent nodes across the data space in the time-space dimension.

[0045] Further, the application records the data flow dynamics of the source flow node through the first flow change curve, presents the data flow dynamics of the target flow node through the second flow change curve, and the flow state explicit node represents the key time point of the data flow appearing dramatic fluctuation. The flow state explicit node on the first flow change curve is selected as the time period starting point because these nodes mean that the data has changed significantly at the source end. The shortest time interval between the time period starting point and the flow state explicit node on the second flow change curve is selected as the time period ending point. The selection of the starting point and the ending point on different curves can reflect the correlation and response of data flow change at both ends during the data transmission from the source end to the target end. Further, the data flow state of adjacent nodes across the data space is associated and recognized.

[0046] Further, the application judges whether there is invalid flow phenomenon between adjacent equipment nodes by quantitatively analyzing the continuity of the data flow feature period. The total time of the data flow stage is counted by obtaining the cumulative value of the duration of all data flow feature periods. The data flow feature period is the process of significant change of data between the source node and the target node. The total duration of the state is calculated. The ratio of the cumulative value of the duration to the duration of the monitoring period is obtained to obtain the continuity feature value, which directly reflects the continuity degree of data flow. Further, the data flow state of adjacent nodes across the data space is associated and recognized.

[0047] Further, the application can accurately filter out the data that truly meets the needs of the target equipment node for transmission by comparing the data request flow and the cache flow and identifying redundant invalid data when the request flow does not exceed the cache flow. It can not only ensure that the target equipment node obtains the required information in time, but also optimize the data transmission path, reduce the network burden, improve the flow efficiency of data in the space network, realize the reasonable configuration and efficient use of data resources, and avoid the waste of data flow resources caused by redundant transmission, response delay and other problems.

[0048] Further, the data flow feature period of the application records the actual duration of the effective transmission of data between equipment nodes. The average value of the duration of all data flow feature periods is calculated, and the average value is set as the response waiting time. It can not only avoid the misjudgment of normal data transmission as timeout due to too short waiting time, but also prevent resource idling caused by too long time, so as to provide a quantitative standard for the reasonable waiting boundary of data request, match the response waiting time with the actual data flow capacity, and realize the associated recognition and analysis of the data flow state of adjacent nodes across the data space, and identify the invalid flow phenomenon of data flow in the time and space dimensions of data flow. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1A step diagram of the data flow method across data spaces of the embodiment of the present application;

[0050] Figure 2 A schematic diagram of the data flow space in the embodiment of the present application;

[0051] Figure 3 A step diagram of determining the flow state explicit node in the embodiment of the present application;

[0052] Figure 4 A step diagram of determining the data flow characteristic period in the embodiment of the present application;

[0053] Figure 5 A step diagram of determining the response waiting time in the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the objects and advantages of the present application clearer, the present application will be further described in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0055] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and not to limit the protection scope of the present application.

[0056] It should be noted that, in the description of the present application, the terms of direction or position relationship indicated by the terms of "up", "down", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which is only 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, and therefore cannot be understood as a limitation on the present application.

[0057] Please refer to Figure 1 The step diagram of the data flow method across data spaces of the embodiment of the present application is shown in the figure, the data flow method across data spaces of the present application comprises:

[0058] In step S100, adjacent device nodes on the data transmission path are acquired to determine a plurality of data flow spaces.

[0059] Specifically, the device node in the present application can be a flow node of a router device or a switch for data flow.

[0060] In step S200, the data flow variation curve of different device nodes with time is drawn respectively, and a plurality of flow state explicit nodes are determined on the flow variation curve respectively.

[0061] Step S300, the flow state explicit node is sequenced according to time sequence relationship, a plurality of data flow characteristic time periods are determined based on the first constraint condition, and the continuity of the data flow characteristic time period is analyzed to determine whether invalid flow phenomenon exists between adjacent device nodes.

[0062] The first constraint condition includes the distribution state of the flow state explicit node.

[0063] Step S400, in response to the existence of invalid flow phenomenon, a space network cache node is set between the adjacent device nodes, an interface mapping relationship between the space network cache node and the adjacent device nodes is established, and whether the data of the space network cache node is transmitted to a target device node is determined according to the second constraint condition.

[0064] The second constraint condition is the comparison result of the total value of data flow transmitted between different nodes within a response waiting time.

[0065] Specifically, the space network cache node can be a processor responsible for protocol analysis, data processing and data scheduling, and is equipped with an Ethernet interface for establishing a physical connection with a source device node and a target device node. This is a commonly used data interaction storage device in the art, and will not be described again.

[0066] Specifically, the process of determining a plurality of data flow spaces based on the node parameters includes:

[0067] Respectively acquire branch transmission paths on the source flow node and the target flow node in the adjacent device nodes.

[0068] Specifically, the source flow node in the application is a flow node that sends data in the adjacent device nodes, and the target flow node is a flow node that receives data sent by the source flow node.

[0069] Each branch transmission path on the source flow node is determined as a data flow space of the source flow node, and each branch transmission path on the target flow node is determined as a data flow space of the target flow node.

[0070] Please refer to Figure 2As shown in the figure, it is a schematic diagram of data flow space in the implementation of the application, the source flow node in the adjacent device node is a1, the target flow node is a2, the source flow node a1 includes branch transmission paths a1b1, a1b2, a1b3, a1b4, the target flow node a2 includes branch transmission paths a2c1, a2c2, a2c3, the branch transmission paths a1b1, a1b2, a1b3, a1b4 are determined as four data flow spaces of the source flow node a1 respectively, and the branch transmission paths a2c1, a2c2, a2c3 are determined as three data flow spaces of the target flow node a2 respectively.

[0071] It can be understood that there are multiple branch transmission paths on the source device node and the target device node of data flow, the data transmission path presents a complex mesh structure, and each branch transmission path has differences in bandwidth, transmission rate, data processing capacity and the like, resulting in different flow characteristics of data on different branches. By utilizing the differences of the branch paths, the data transmission process is finely divided, and by acquiring the branch transmission paths of each device node in the adjacent device node, the data transmission network is actually deconstructed, and the overall complex transmission path is decomposed into multiple relatively independent data flow units, and each branch transmission path is determined as a data flow space of the adjacent device node.

[0072] Specifically, refer to Figure 3 As shown in the figure, it is a step diagram for determining the flow state explicit node in the embodiment of the application, and the process of determining a plurality of flow state explicit nodes on the flow change curve includes:

[0073] In step S201, a first flow change curve is constructed with the data flow of the data flow space on the source flow node as the vertical axis and the monitoring period of the preset time length as the horizontal axis, and a second flow change curve is constructed with the data flow of the data flow space on the target flow node as the vertical axis and the monitoring period of the preset time length as the horizontal axis.

[0074] Specifically, the unit on the vertical axis of the first flow change curve and the second flow change curve is M / s, and the unit on the horizontal axis is h.

[0075] The value on the vertical axis of the first flow change curve is the real-time data flow of all data flow spaces on the source flow node, and the value on the vertical axis of the second flow change curve is the real-time data flow of all data flow spaces on the target flow node; the preset time length of the monitoring period can be set by the person skilled in the art, and the value range of the preset time length is [1, 3] with the interval unit of h, and preferably, the preset time length of the monitoring period in the implementation of the application can be 2h.

[0076] Step S202, determining the absolute value of the curve slope of a plurality of data points on the first flow change curve and the second flow change curve respectively;

[0077] Specifically, the absolute value of the curve slope of any data point on the flow change curve can be determined according to the difference method in mathematics, which is prior art and will not be repeated here.

[0078] Step S203, determining the data point with the absolute value of the curve slope exceeding the preset slope reference value as the flow state dominant node.

[0079] Specifically, the preset slope reference value in the present application can be in the range of [0.35, 0.45]. In order to avoid missing the time point of sudden change of data flow caused by too large value of the preset slope reference value, and misjudging the time point of sudden change of data flow caused by too small value of the slope reference value, preferably, a value of the slope reference value is provided here, which is 0.4.

[0080] It can be understood that the flow change curve is an important carrier reflecting the data flow state of each branch transmission path, and the real-time data flow of the vertical axis and the monitoring period of the horizontal axis can directly present the dynamic change trend of data in the time dimension. The absolute value of the curve slope directly reflects the rate of change of data flow, and the larger the absolute value of the slope is, the more intense the increase or decrease of data flow at the corresponding time is, which represents a significant turning point of the data flow state. The flow state dominant node determined in this way provides an accurate anchor point for analyzing the data flow characteristic period, and realizes the analysis of the data flow state of adjacent nodes in the space-time dimension across the data space.

[0081] Specifically, please refer to Figure 4 Fig. 2 shows a step diagram for determining the data flow characteristic period according to an embodiment of the present application. The process of determining the data flow characteristic period includes:

[0082] Step S301, sequentially taking the flow state dominant node on the first flow change curve as the period start point in time sequence;

[0083] Step S302, determining the flow state dominant node on the second flow change curve with the shortest interval time length from the period start point as the period end point in time sequence;

[0084] Step S303, determining the period composed of the period start point and the period end point as the data flow characteristic period.

[0085] Among them, the period end point of the previous data flow characteristic period is prior to the period start point of the next data flow characteristic period in the time sequence.

[0086] The skilled in the art can understand that when the source node appears data flow surge due to the start of a new task, the target node will soon appear corresponding data flow significant change after network transmission. The period covers the complete process from data change at the source end to the change perception at the target end.

[0087] It can be understood that the first flow change curve records the data flow dynamics of the source flow node, and the second flow change curve presents the data flow dynamics of the target flow node. The flow state explicit node represents the key time point of the dramatic fluctuation of data flow. The flow state explicit node on the first flow change curve is selected as the starting point of the period because these nodes mean that data has changed significantly at the source end. The starting point can determine the change starting point in data transmission. The flow state explicit node with the shortest interval length from the starting point of the period is selected as the ending point on the second flow change curve. The selection of the starting point and the ending point on different curves can reflect the correlation and response of data flow change from the source end to the target end in the process of data transmission. The selection of the explicit node with the shortest interval as the ending point not only ensures the coherence and closeness of the data flow state change in the period, but also focuses on the process from the significant change of data flow at the source end to the target end, avoiding the introduction of irrelevant data due to the too long period.

[0088] Specifically, the process of analyzing the continuity of the data flow feature period includes:

[0089] Obtaining the cumulative value of the duration of all data flow feature periods;

[0090] The ratio of the cumulative value of the duration to the duration of the monitoring period is determined as the continuity feature representation value.

[0091] Specifically, the value range of the continuity feature representation value is [0, 1].

[0092] It can be understood that if the response of the target flow node and the response of the source flow node in the adjacent flow nodes are generally associated, it means that the data interaction of the two lacks close time sequence connection, and the change of data output of the source node is difficult to trigger the timely and corresponding response of the target node, resulting in that the data flow feature period is scattered in the monitoring period and the total duration accounts for a low proportion. The weaker the correlation is, the more fragmented the time segment of effective interaction is, the smaller the cumulative value is, and the closer the representation value is to 0, thereby accurately reflecting the low efficiency of data flow due to the lack of linkage.

[0093] Specifically, the process of comparing the continuity feature representation value with the preset continuity feature representation reference value and determining whether there is an invalid flow phenomenon between the adjacent device nodes according to the comparison result includes:

[0094] If the continuity feature representation value exceeds the preset continuity feature representation reference value, it is determined that there is no invalid flow phenomenon between adjacent device nodes.

[0095] If the continuity feature representation value does not exceed the preset continuity feature representation reference value, it is determined that there is an invalid flow phenomenon between adjacent device nodes.

[0096] Specifically, the continuity feature representation reference value can be calculated according to a pre-test. A plurality of groups of adjacent flow nodes are tested for continuity feature representation test values in a data flow process, and the average value of the continuity feature representation test values is calculated. The average value of the continuity feature representation test values is determined as the continuity feature representation reference value. The following is 6 groups of test data:

[0097]

[0098] According to the data in the above table, the value of the continuity feature representation reference value can be calculated as 0.725. Preferably, the value of the continuity feature representation reference value can be set as 0.725.

[0099] It can be understood that the efficiency of data flow is closely related to continuity. By quantitatively analyzing the continuity of the data flow feature period, it is determined whether there is an invalid flow phenomenon between adjacent device nodes. By obtaining the cumulative value of the duration of the data flow feature period, the total amount of time of the data flow stage is counted. The data flow feature period is the process of significant change of data between the source node and the target node. The total duration of the state is calculated. The ratio of the cumulative value of the duration to the duration of the monitoring period is the continuity feature representation value, which directly reflects the continuity degree of the data flow. If the ratio is close to 1, it means that most of the time in the entire monitoring period is in an effective flow state, that is, the continuity of the data flow is good. If the ratio is small, it means that there is more time for non-associated flow change in the monitoring period, and the continuity of the data flow is poor.

[0100] Specifically, the process of establishing the interface mapping relationship between the space network cache node and the source device node includes:

[0101] Establishing a first transmission mapping relationship between the space network cache node and the source device node, so that the data transmission of the source device node is transmitted to the space network cache node for caching;

[0102] Obtaining the total data flow value of the data transmission of the source device node to the space network cache node within the response waiting time.

[0103] Specifically, the first transmission mapping relationship can be a TCP / IP-based transmission protocol, which is used to regulate the data transmission interaction between the space network cache node and the source device node, ensures that the original data generated by the source device node can be accurately and completely transmitted to the cache node for temporary storage according to a unified rule, and supports the cache node to monitor the total data flow value of the data transmission process. The space network cache node records the total data flow received in a unit of time in real time, which is not repeated here.

[0104] Specifically, the process of establishing the interface mapping relationship between the space network cache node and the target device node includes:

[0105] The second transmission mapping relationship between the space network cache node and the target device node is established, so that the space network cache node receives the data request of the target device node.

[0106] The total data flow value corresponding to the data request of the target device node within the response waiting time is determined.

[0107] Specifically, the second transmission mapping relationship is used to regulate the data request interaction between the space network cache node and the target device node, adopts a request-response mode based on the HTTP / 2 protocol, the target device node initiates a data request, the cache node verifies the validity of the signature and the compliance of the request format after receiving the request, and then queries the matching data according to the request parameters within the preset response waiting time, while the total data flow value corresponding to all valid requests within the period is calculated in real time. This is a common technical means for those skilled in the art, which is not repeated here.

[0108] Specifically, the total data flow value corresponding to the data request is compared with the total data flow value of the space network cache node.

[0109] If the total data flow value corresponding to the data request does not exceed the total data flow value of the space network cache node, it is determined that the data of the space network cache node will not be transmitted to the target device node.

[0110] If the total data flow value corresponding to the data request exceeds the total data flow value of the space network cache node, it is determined that the data of the space network cache node will be transmitted to the target device node.

[0111] Specifically, in the implementation of the present application, the number of requests of the target device node to the cache data of the source flow node within the response waiting time is counted. If the total data flow value corresponding to the data request within the response waiting time does not exceed the total data flow value of the space network cache node for 3 times, the data in the space network cache node is cleaned up.

[0112] It can be understood that when the total data flow value corresponding to the data request does not exceed the total data flow value of the spatial network cache node, it means that the amount of data in the cache node exceeds the current actual demand of the target device node. From the perspective of data value, the data exceeding the demand part cannot be effectively utilized by the target device node in the current transmission stage, and therefore can be identified as redundant and invalid data.

[0113] Those skilled in the art can understand that different device nodes can adopt different data formats and transmission rules, and the interface transmission protocol ensures that the data of the source device node can be accurately and correctly transmitted to the spatial network cache node for caching. The spatial network cache node and the target device node establish a second transmission mapping relationship in order to realize the response of the cache node to the data request of the target device node. The target device node initiates a request to the cache node according to the protocol when it needs data, and the response waiting time is determined based on factors such as data flow characteristic period, which reflects the demand amount of the target device node within a normal data acquisition period.

[0114] Specifically, referring to FIG. 8, which is a step diagram for determining the response waiting time, the process of determining the response waiting time includes the following steps: Figure 5

[0115] In step S401, the duration of each data flow characteristic period is obtained.

[0116] In step S402, the average value of the duration of all data flow characteristic periods is calculated.

[0117] In step S403, the average value of the duration is determined as the response waiting time.

[0118] It can be understood that the data flow characteristic period of the present application records the actual duration of effective transmission of data between device nodes. By calculating the average value of the duration of all data flow characteristic periods and setting the average value as the response waiting time, it can avoid misjudging normal data transmission as timeout due to too short waiting time, and prevent resource idling due to too long time, thereby providing a quantitative standard for the reasonable waiting boundary of data request, matching the response waiting time with the actual data flow capacity, and realizing the correlation recognition analysis of the data flow state of the adjacent nodes across the data space, and identifying the invalid flow phenomenon of data flow in the time-space dimension of data flow.

[0119] The embodiment also provides a computer readable storage medium having computer program code stored therein, which, when executed on a computer, causes the computer to perform the above-mentioned related method steps to realize the data flow method across the data space provided by the above-mentioned embodiment.

[0120] ​So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

[0121] The above only describes the preferred embodiments of the present application and is not intended to limit the present application; the present application can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data flow method across data spaces, characterized by, The method comprises the following steps: acquiring adjacent device nodes on a data transmission path to determine a plurality of data flow spaces; the process of determining the plurality of data flow spaces comprises acquiring branch transmission paths on a source flow node and a target flow node in the adjacent device nodes respectively, determining each branch transmission path on the source flow node as a data flow space of the source flow node, and determining each branch transmission path on the target flow node as a data flow space of the target flow node; drawing a flow change curve of data flow of different device nodes with respect to time respectively, and determining a plurality of flow state dominant nodes on the flow change curve respectively; the process of determining the plurality of flow state dominant nodes on the flow change curve comprises constructing a first flow change curve with data flow of the data flow space on the source flow node as a vertical axis and a preset monitoring period as a horizontal axis, constructing a second flow change curve with data flow of the data flow space on the target flow node as a vertical axis and the preset monitoring period as a horizontal axis, determining a curve slope absolute value of a plurality of data points on the first flow change curve and the second flow change curve respectively, and determining a data point with a curve slope absolute value exceeding a preset slope reference value as a flow state dominant node; sequentially sorting the flow state dominant nodes according to a time sequence, determining a plurality of data flow characteristic time periods based on a preset first constraint condition, and analyzing continuity of the data flow characteristic time periods to determine whether there is an invalid flow phenomenon between the adjacent device nodes; the first constraint condition comprises a distribution state of the flow state dominant nodes; in response to the existence of the invalid flow phenomenon, setting a space network cache node between the adjacent device nodes, establishing an interface mapping relationship between the space network cache node and the adjacent device nodes, and determining whether to transmit data of the space network cache node to a target device node according to a preset second constraint condition; the second constraint condition is a comparison result of total data flow value transmitted between different nodes within a response waiting time.

2. The method of claim 1, wherein, The process of determining the data flow characteristic time periods comprises: sequentially taking a flow state dominant node on the first flow change curve as a time period starting point in a time sequence; sequentially taking a flow state dominant node on the second flow change curve with a shortest time interval from the time period starting point as a time period ending point in a time sequence; determining a time period composed of the time period starting point and the time period ending point as the data flow characteristic time period; wherein a time period ending point of a previous data flow characteristic time period precedes a time period starting point of a subsequent data flow characteristic time period in a time sequence.

3. The method of claim 2, wherein, The process of analyzing continuity of the data flow characteristic time periods comprises: acquiring a duration cumulative value of all data flow characteristic time periods; determining a continuity characteristic representation value as a ratio of the duration cumulative value to a duration of a monitoring period.

4. The method of claim 3, wherein, The process of comparing the continuity characteristic representation value with a preset continuity characteristic representation reference value and determining whether there is an invalid flow phenomenon between the adjacent device nodes according to a comparison result comprises: If the continuity feature representation value does not exceed the preset continuity feature representation reference value, it is determined that there is an invalid flow phenomenon between the adjacent device nodes.

5. The data flow method across data spaces according to claim 4, characterized in that, The process of establishing the interface mapping relationship between the space network cache node and the source device node includes: Establishing a first transmission mapping relationship between the space network cache node and the source device node, so that the data transmission of the source device node is transmitted to the space network cache node for caching; Obtaining the total data flow value of the data transmission of the source device node to the space network cache node within the response waiting time.

6. The data flow method across data spaces according to claim 5, characterized in that, The process of establishing the interface mapping relationship between the space network cache node and the target device node includes: Establishing a second transmission mapping relationship between the space network cache node and the target device node, so that the space network cache node receives the data request of the target device node; Determining the total data flow value corresponding to the data request of the target device node within the response waiting time.

7. The method of claim 6, wherein, Comparing the total data flow value corresponding to the data request with the total data flow value of the space network cache node; If the total data flow value corresponding to the data request exceeds the total data flow value of the space network cache node, it is determined that the data transmission of the space network cache node is transmitted to the target device node.

8. The method of claim 7, wherein, The process of determining the response waiting time includes: Obtaining the duration of each data flow feature period; Calculating the average value of the duration of all data flow feature periods; Determining the average value of the duration as the response waiting time.

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