Cross-data-space data circulation method

Abstract

The invention relates to the technical field of data transmission management, in particular to a cross-data-space data circulation method, which comprises the following steps of: determining a plurality of data circulation spaces by acquiring node parameters of adjacent equipment nodes on a data transmission path; a plurality of flow state dominant nodes are determined on flow change curves of different data flow spaces to determine a data flow characteristic time period, and continuity analysis is performed on the data flow characteristic time period determined according to a first constraint condition to determine whether an invalid flow phenomenon exists or not. A spatial network cache node is arranged between adjacent equipment nodes, and whether data of the spatial network cache node is transmitted to a target equipment node with an interface mapping relation is determined according to a second constraint condition. According to the method, association identification analysis is carried out on the data circulation states of the adjacent nodes crossing the data space, and waste of data circulation resources caused by the problems of redundant transmission, response delay and the like is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission management, and in particular to a data circulation method across data spaces. Background Art

[0002] With the rapid development of information technology, cross-data space data flow has been widely used in many fields. Cross-data space data flow interaction supports risk assessment and ensures the efficient operation of production processes. However, during the data transmission process, the complexity of adjacent device nodes on the data transmission path leads to low data flow efficiency. Each device node may have different transmission capabilities and data processing rules. The data flow on different branch transmission paths fluctuates irregularly, resulting in a large amount of invalid data flow. Some data frequently encounters redundant transmission, waiting delays and other problems during the transmission process, which not only wastes network bandwidth resources, but may also lead to untimely transmission of critical data, affecting the normal operation of the entire system. Traditional data flow methods are difficult to accurately judge the data flow status and lack targeted processing mechanisms. Therefore, cross-data space data flow methods are urgently needed to ensure efficient and stable data flow in different spaces.

[0003] For example, Chinese patent publication number CN119473802A discloses a real-time circulation monitoring method and platform in a trusted data space, relating to the fields of data management and security technology. The method includes: constructing a trusted data space based on data circulation requirements, and using a supervisory command console to monitor data circulation in real time to obtain real-time monitoring data. Risks are then identified using this monitoring data, and a circulation risk coefficient is calculated and compared with a preset risk threshold. When the risk coefficient reaches or exceeds the threshold, the system generates a circulation risk warning signal.

[0004] The following problems also exist in the prior art: Existing technologies do not consider the correlation identification and analysis of the data flow status of adjacent nodes across data space, making it difficult to identify invalid data flow phenomena in the spatiotemporal dimensions of data flow, resulting in redundant transmission, response delays and other problems that waste data flow resources. Summary of the Invention

[0005] To this end, the present invention provides a data circulation method across data space to overcome the problem that the existing technology cannot perform correlation identification and analysis on the data circulation status of adjacent nodes across data space, and it is difficult to identify the invalid circulation phenomenon of data circulation in the spatiotemporal dimension of data circulation.

[0006] To achieve the above objectives, the present invention provides a data circulation method across data spaces, comprising: Acquire adjacent device nodes on a data transmission path to determine a number of data circulation spaces; Draw the flow change curves of data flow of different device nodes over time, and determine several flow status explicit nodes on the flow change curves; Sort the nodes with explicit circulation status according to the time sequence relationship, determine a number of data circulation characteristic time periods based on the preset first constraint condition, and analyze the continuity of the data circulation characteristic time periods to determine whether there is invalid circulation between adjacent device nodes; The first constraint condition includes the distribution state of the circulation state explicit nodes; In response to the existence of the 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 according to a preset second constraint condition, whether to transmit the data of the space network cache node to the target device node is determined; The second constraint condition is a comparison result of the total value of data traffic transmitted between different nodes within the response waiting time.

[0007] Furthermore, the process of determining a plurality of data flow spaces based on the node parameters includes: respectively obtaining branch transmission paths on source circulation nodes and target circulation nodes in adjacent device nodes; Each branch transmission path on the source circulation node is determined as the data circulation space of the source circulation node, and each branch transmission path on the target circulation node is determined as the data circulation space of the target circulation node.

[0008] Furthermore, the process of determining a number of flow state explicit nodes on the flow change curve includes: 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 a preset duration as the horizontal axis; 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 a preset duration as the horizontal axis; Determining absolute values ​​of curve slopes of a plurality of data points on the first flow change curve and the second flow change curve respectively; The data point whose absolute value of the slope of the curve exceeds a preset slope reference value is determined as a flow state dominant node.

[0009] Furthermore, the process of determining the characteristic period of data circulation includes: Taking the flow state dominant nodes on the first flow change curve as the starting points of the time period in sequence; Determine the flow state explicit node on the second flow change curve with the shortest interval between the time interval and the start point of the time interval as the end point of the time interval in time sequence; Determine a period consisting of the period start point and the period end point as the data flow characteristic period; Among them, the end point of the previous data circulation characteristic time period in each data circulation characteristic time period is earlier than the start point of the next data circulation characteristic time period in terms of time sequence relationship.

[0010] Furthermore, the process of analyzing the continuity of the characteristic period of data circulation includes: Obtain the cumulative duration of all data flow characteristic periods; The ratio of the accumulated duration value to the duration of the monitoring period is determined as the continuity characteristic value.

[0011] Furthermore, the continuity characteristic representation value is compared with a preset continuity characteristic representation reference value, and a process of determining whether invalid flow occurs between adjacent device nodes based on the comparison result includes: If the continuity characteristic representation value does not exceed the preset continuity characteristic representation reference value, it is determined that invalid flow exists between adjacent device nodes.

[0012] Furthermore, the process of establishing the interface mapping relationship between the spatial network cache node and the source device node includes: Establishing a first transmission mapping relationship between the spatial network cache node and the source device node, so that data of the source device node is transmitted to the spatial network cache node for caching; The total value of data flow from the source device node to the spatial network cache node within the response waiting time is obtained.

[0013] Furthermore, the process of establishing the interface mapping relationship between the spatial network cache node and the target device node includes: Establishing a second transmission mapping relationship between the spatial network cache node and the target device node, so that the spatial network cache node receives the data request of the target device node; Determine the total data flow value corresponding to the data request of the target device node within the response waiting time.

[0014] Further, comparing the total data traffic value corresponding to the data request with the total data traffic value of the spatial network cache node; If the total value of the data traffic corresponding to the data request exceeds the total value of the data traffic of the space network cache node, it is determined to transmit the data of the space network cache node to the target device node.

[0015] Furthermore, the process of determining the response waiting time includes: Obtain the duration of each data circulation characteristic period; Calculate the average duration of all data flow characteristic periods; An average value of the durations is determined as the response waiting time.

[0016] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention determines several data circulation 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 circulation spaces over time, and determines several flow state explicit nodes on the flow change curve, and performs continuity analysis on the data flow characteristic period determined according to the first constraint condition to determine whether there is invalid flow phenomenon, and sets a space network cache node between adjacent device nodes, and determines whether to transmit the data of the space network cache node to the target device node with the interface mapping relationship according to the second constraint condition. The present invention performs correlation identification analysis on the data flow status of adjacent nodes across the data space, identifies the invalid flow phenomenon of data flow in the spatiotemporal dimension of data flow, and avoids the waste of data flow resources caused by redundant transmission, response delay and other problems.

[0017] Furthermore, the present invention uses the traffic change curve as an important carrier to reflect the data flow status of each branch transmission path. The real-time data flow on the vertical axis and the monitoring period on the horizontal axis can intuitively present the dynamic change trend of the data in the time dimension. The absolute value of the slope of the curve directly reflects the rate of change of the data flow. The larger the absolute value of the slope, the more drastic the increase or decrease of the data flow at the corresponding time, indicating that there is a significant turning point in the data flow status. The explicit node of the flow status determined in this way provides a precise anchor point for analyzing the characteristic period of data flow, and realizes the analysis of the data flow status of adjacent nodes across the data space in the time and space dimensions.

[0018] Furthermore, the present invention records the data flow dynamics of the source circulation node through the first flow change curve, and presents the data flow dynamics of the target circulation node through the second flow change curve. The flow status explicit nodes represent the key time points when the data flow fluctuates violently. The flow status explicit nodes on the first flow change curve are used as the starting points of the time period because these nodes mean that the data has changed significantly at the source end. Taking this as the starting point, the starting point of the change in data transmission can be determined. On the second flow change curve, the flow status explicit node with the shortest interval with the time period starting point is selected as the end point of the time period. Selecting the starting point and end point on different curves can reflect the correlation and response of the data flow changes at both ends during the process of data starting from the source end to the target end; thereby, the correlation identification and analysis of the data flow status of adjacent nodes across the data space is realized.

[0019] Furthermore, the present invention determines whether there is invalid circulation between adjacent device nodes by quantitatively analyzing the continuity of the data circulation characteristic time period, and statistics the total time of the data circulation stage by obtaining the cumulative value of the duration of all data circulation characteristic time periods. The data circulation characteristic time period is the process in which the data changes significantly between the source node and the target node. The total duration of the state is calculated by the ratio of the cumulative value of the duration to the duration of the monitoring period to obtain the continuity characteristic representation value, which intuitively reflects the degree of continuity of the data circulation, and thus realizes the association identification analysis of the data circulation status of adjacent nodes across the data space.

[0020] Furthermore, the present invention compares data request traffic with cache traffic, and identifies redundant and invalid data when the request traffic does not exceed the cache traffic, so as to accurately screen out data that truly meets the needs of the target device node for transmission. This can not only ensure that the target device node obtains the required information in a timely manner, but also optimize the data transmission path, reduce the network burden, improve the data circulation efficiency in the spatial network, realize the reasonable allocation and efficient utilization of data resources, and avoid the waste of data circulation resources caused by problems such as redundant transmission and response delay.

[0021] Furthermore, the data flow characteristic period of the present invention records the actual duration of effective data transmission between device nodes. By calculating the average duration of all data flow characteristic periods, the average value is set as the response waiting time. This can not only avoid normal data transmission being misjudged as timeout due to too short waiting time, but also prevent resources from being idle due to too long a duration, thereby providing a quantitative standard for a reasonable waiting boundary of data requests, matching the response waiting time with the actual data flow capacity, and realizing the correlation identification and analysis of the data flow status of adjacent nodes across the data space, and identifying the invalid flow phenomenon of data flow in the time and space dimension of data flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A diagram showing the steps of a method for data circulation across data spaces according to an embodiment of the present invention; Figure 2 A schematic diagram of the data circulation space in the implementation of the present invention; Figure 3 A diagram showing the steps for determining a flow status explicit node according to an embodiment of the present invention; Figure 4 A diagram showing the steps for determining a characteristic period of data flow according to an embodiment of the present invention; Figure 5 A diagram showing the steps for determining the response waiting time according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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 merely used to explain the present invention and are not intended to limit the present invention.

[0024] 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 scope of protection of the present invention.

[0025] It should be noted that, in the description of the present invention, terms such as "upper", "lower", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This 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. Therefore, it cannot be understood as a limitation on the present invention.

[0026] See also Figure 1 FIG. 2 is a diagram showing steps of a method for data circulation across data spaces according to an embodiment of the present invention. The method for data circulation across data spaces according to the present invention includes: Step S100, obtaining adjacent device nodes on a data transmission path to determine a number of data flow spaces; Specifically, the device node in the present invention may be a circulation node of a router device or a switch used for data circulation.

[0027] Step S200: drawing flow change curves of data flows of different device nodes over time, and determining a number of flow state explicit nodes on the flow change curves; Step S300: sorting the nodes with explicit data flow status according to a time sequence relationship, determining a number of characteristic data flow periods based on a preset first constraint condition, and analyzing the continuity of the characteristic data flow periods to determine whether there is invalid data flow between adjacent device nodes; Wherein, the first constraint condition includes the distribution state of the circulation state explicit nodes; Step S400: In response to the existence of an 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 to transmit data of the space network cache node to a target device node is determined according to a preset second constraint condition; The second constraint condition is a comparison result of the total value of data traffic transmitted between different nodes within the response waiting time.

[0028] Specifically, the spatial network cache node can be a processor, responsible for protocol parsing, data processing and data scheduling, and equipped with an Ethernet interface for establishing a physical connection with the source device node and the target device node. This is a data interaction storage device commonly used by technicians in this field, and will not be repeated here.

[0029] Specifically, the process of determining a plurality of data flow spaces based on the node parameters includes: respectively obtaining branch transmission paths on source circulation nodes and target circulation nodes in adjacent device nodes; Specifically, the source circulation node in the present invention is a circulation node that sends data among adjacent device nodes, and the target circulation node is a circulation node that receives data sent by the source circulation node.

[0030] Each branch transmission path on the source circulation node is determined as the data circulation space of the source circulation node, and each branch transmission path on the target circulation node is determined as the data circulation space of the target circulation node.

[0031] See also Figure 2 As shown, it is a schematic diagram of the data circulation space in the implementation of the present invention. Among the adjacent device nodes, the source circulation node is a1, and the target circulation node is a2. The source circulation node a1 includes branch transmission paths a1b1, a1b2, a1b3, and a1b4, and the target circulation node a2 includes branch transmission paths a2c1, a2c2, and a2c3. The branch transmission paths a1b1, a1b2, a1b3, and a1b4 are respectively determined as the four data circulation spaces of the source circulation node a1, and the branch transmission paths a2c1, a2c2, and a2c3 are respectively determined as the three data circulation spaces of the target circulation node a2.

[0032] It is understandable that multiple branching transmission paths exist between the source and destination device nodes of data flow, presenting a complex mesh structure. Each branching transmission path differs in bandwidth, transmission rate, and data processing capability, resulting in varying data flow characteristics on different branches. By leveraging these differences in branching paths, the data transmission process is refined and divided. By obtaining the branching transmission paths of each device node in adjacent device nodes, the data transmission network is effectively deconstructed, breaking down the overall complex transmission path into multiple relatively independent data flow units, and defining each branching transmission path as the data flow space for adjacent device nodes.

[0033] Specifically, see Figure 3 As shown in FIG. , which is a diagram showing the steps of determining a flow state explicit node according to an embodiment of the present invention, the process of determining a plurality of flow state explicit nodes on a flow change curve includes: Step S201: constructing a first flow change curve with the data flow rate of the data flow space on the source flow node as the vertical axis and the preset monitoring period as the horizontal axis; and constructing a second flow change curve with the data flow rate of the data flow space on the target flow node as the vertical axis and the preset monitoring period as the horizontal axis; 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.

[0034] The value of the first flow change curve on the vertical axis is the real-time data flow of the entire data flow space on the source flow node, and the value of the second flow change curve on the vertical axis is the real-time data flow of the entire data flow space on the target flow node; the preset duration of the monitoring period can be set by a person skilled in the art, and the value range of the preset duration is [1, 3], and the interval unit is h. Preferably, in the implementation of the present invention, the preset duration of the monitoring period can be 2h.

[0035] Step S202, determining the absolute values ​​of the slopes of a plurality of data points on the first flow rate change curve and the second flow rate change curve respectively; 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. This is a prior art and will not be described in detail here.

[0036] Step S203 : determining a data point whose absolute value of the slope of the curve exceeds a preset slope reference value as a flow state explicit node.

[0037] Specifically, the value range of the slope reference value preset in the present invention can be [0.35, 0.45]. In order to avoid the time point when the data flow suddenly changes being missed due to the preset slope reference value being too large, and the time point when the data flow suddenly changes being misjudged due to the slope reference value being too small, preferably, a slope reference value is provided here, and its value is 0.4.

[0038] It can be understood that the traffic change curve is an important carrier that reflects the data flow status of each branch transmission path. The real-time data flow on the vertical axis and the monitoring period on the horizontal axis can intuitively present the dynamic change trend of the data in the time dimension. The absolute value of the slope of the curve directly reflects the rate of change of the data flow. The larger the absolute value of the slope, the more drastic the increase or decrease in the data flow at the corresponding time, indicating that there has been a significant turning point in the data flow status. The explicit nodes of the flow status determined in this way provide a precise anchor point for analyzing the characteristic time period of data flow, and realize the analysis of the data flow status of adjacent nodes across the data space in the time and space dimensions.

[0039] Specifically, see Figure 4As shown in FIG. 1 , which is a diagram showing the steps for determining a characteristic period of data circulation according to an embodiment of the present invention, the process of determining a characteristic period of data circulation includes: Step S301, sequentially taking the flow state dominant nodes on the first flow change curve as the starting points of the time period in chronological order; Step S302, determining the flow state explicit node on the second flow change curve with the shortest interval from the start point of the time period as the end point of the time period in time sequence; Step S303: Determine the period consisting of the period start point and the period end point as the data flow characteristic period.

[0040] Among them, the end point of the previous data circulation characteristic time period in each data circulation characteristic time period is earlier than the start point of the next data circulation characteristic time period in terms of time sequence relationship.

[0041] Those skilled in the art can understand that when the source node experiences a drastic change in data traffic due to the start of a new task, the target node will soon experience a corresponding significant change in data traffic after transmission over the network. This period covers the entire process from the generation of data changes at the source end to the perception of changes at the target end. It can be understood that the first flow change curve records the data flow dynamics of the source circulation node, and the second flow change curve presents the data flow dynamics of the target circulation node. The flow status explicit nodes represent the key time points when the data flow fluctuates violently. The flow status explicit nodes on the first flow change curve are used as the starting points of the time period because these nodes mean that the data has changed significantly at the source end. Taking this as the starting point can determine the starting point of the change in data transmission. On the second flow change curve, the flow status explicit node with the shortest interval with the time period starting point is selected as the end point of the time period. Selecting the starting point and end point on different curves can reflect the correlation and response of the data flow changes at both ends during the process of data starting from the source end and receiving at the target end; selecting the explicit node with the shortest interval as the end point not only ensures the continuity and closeness of the data flow state changes within the time period, but also focuses on the process of significant changes in data flow from the source end to the target end, avoiding the introduction of irrelevant data due to too long a time period.

[0042] Specifically, the process of analyzing the continuity of the characteristic period of data circulation includes: Obtain the cumulative duration of all data flow characteristic periods; The ratio of the accumulated duration value to the duration of the monitoring period is determined as the continuity characteristic value.

[0043] Specifically, the value range of the continuity feature representation value is [0, 1].

[0044] It is understandable that if the response of the target circulation node in adjacent circulation nodes is generally correlated with the response of the source circulation node, it means that the data interaction between the two lacks a close temporal connection, and the data output changes of the source node are difficult to trigger a timely and corresponding response from the target node, resulting in the data circulation feature time periods being scattered in the monitoring cycle and accounting for a low proportion of the total duration. The weaker the correlation, the more fragmented the time segments of effective interaction, the smaller the cumulative value, and the closer the representation value is to 0, thereby accurately reflecting the inefficient state of data circulation due to lack of linkage.

[0045] Specifically, the process of comparing the continuity characteristic value with a preset continuity characteristic reference value and determining whether invalid flow exists between adjacent device nodes based on the comparison result includes: If the continuity characteristic representation value exceeds a preset continuity characteristic representation reference value, it is determined that there is no invalid flow phenomenon between adjacent device nodes; If the continuity characteristic representation value does not exceed the preset continuity characteristic representation reference value, it is determined that invalid flow exists between adjacent device nodes.

[0046] Specifically, the continuity feature characterization reference value can be calculated based on pre-testing. The continuity feature characterization test values ​​of several groups of adjacent circulation nodes during the data circulation process are pre-tested, and the average value of the continuity feature characterization test values ​​is calculated. The average value of the continuity feature characterization test values ​​is determined as the continuity feature characterization reference value. The following are 6 groups of test data:

[0047] According to the data in the above table, the value of the continuity feature characterization reference value can be calculated to be 0.725. Preferably, the value of the continuity feature characterization reference value can be set to be 0.725.

[0048] It is understandable that the efficiency of data flow is closely related to its continuity. By quantitatively analyzing the continuity of the characteristic periods of data flow, it is possible to determine whether there is invalid flow between adjacent device nodes. By obtaining the cumulative duration of all characteristic periods of data flow, the total time of the data flow stage is statistically analyzed. The characteristic period of data flow is the process in which the data undergoes significant changes between the source node and the target node. The total duration of the state is calculated by calculating the ratio of the cumulative duration to the duration of the monitoring period to obtain the continuity characteristic value, which directly reflects the degree of continuity of data flow. If the ratio is close to 1, it means that the data is in an effective flow state most of the time during the entire monitoring period, that is, the continuity of data flow is good; if the ratio is small, it means that there is a lot of time during the monitoring period when the data flow changes unrelatedly, and the continuity of data flow is poor. Specifically, the process of establishing the interface mapping relationship between the spatial network cache node and the source device node includes: Establishing a first transmission mapping relationship between the spatial network cache node and the source device node, so that data of the source device node is transmitted to the spatial network cache node for caching; The total value of data flow from the source device node to the spatial network cache node within the response waiting time is obtained.

[0049] Specifically, the first transmission mapping relationship can be a TCP / IP-based transmission protocol, which is used to standardize the data transmission interaction between the spatial network cache node and the source device node, ensuring 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 unified rules, and at the same time supports the cache node to monitor the total data traffic value of the data transmission process. The spatial network cache node records the total data traffic received per unit time in real time, which is an existing technology and will not be repeated here.

[0050] Specifically, the process of establishing the interface mapping relationship between the spatial network cache node and the target device node includes: Establishing a second transmission mapping relationship between the spatial network cache node and the target device node, so that the spatial network cache node receives the data request of the target device node; Determine the total data flow value corresponding to the data request of the target device node within the response waiting time.

[0051] Specifically, the second transmission mapping relationship is used to standardize the data request interaction between the spatial network cache node and the target device node, adopting a request-response mode based on the HTTP / 2 protocol. The target device node initiates a data request. After receiving the request, the cache node first verifies the validity of the signature and the compliance of the request format, and then queries the matching data according to the request parameters within the preset response waiting time. At the same time, the total data traffic corresponding to all valid requests in the period is counted in real time. This is a technical means commonly used by technicians in this field and will not be repeated here.

[0052] Specifically, the total data traffic value corresponding to the data request is compared with the total data traffic value of the spatial network cache node; If the total data traffic value corresponding to the data request does not exceed the total data traffic value of the space network cache node, determining not to transmit the data of the space network cache node to the target device node; If the total value of the data traffic corresponding to the data request exceeds the total value of the data traffic of the space network cache node, it is determined to transmit the data of the space network cache node to the target device node.

[0053] Specifically, in the implementation of the present invention, the number of requests for cached data of the source circulation node by the target device node within the response waiting time is counted. If the total value of data traffic corresponding to the data requests accumulated within the response waiting time for three times does not exceed the total value of data traffic of the spatial network cache node, the data in the spatial network cache node is cleaned up.

[0054] It's understandable that when the total data traffic corresponding to a data request doesn't exceed the total data traffic of the spatial network's cache nodes, it means the amount of data in the cache nodes exceeds the actual current needs of the target device node. From the perspective of data value, the excess data cannot be effectively utilized by the target device node during the current transmission phase and is therefore considered redundant and invalid.

[0055] It will be understood by those skilled in the art that different device nodes may adopt different data formats and transmission rules. The interface transmission protocol ensures that the data of the source device node can be accurately transmitted to the space network cache node for caching. The space network cache node establishes a second transmission mapping relationship with the target device node in order to realize the cache node's response to the target device node's data request. When the target device node needs data, it initiates a request to the cache node according to the protocol. The response waiting time is determined based on factors such as the characteristic period of data circulation, reflecting the demand of the target device node during the normal data acquisition cycle. Specifically, see Figure 5 As shown in FIG, which is a step diagram for determining the response waiting time, the process of determining the response waiting time includes: Step S401, obtaining the duration of each data flow characteristic period; Step S402, calculating the average duration of all data flow characteristic time periods; Step S403: Determine the average value of the durations as the response waiting time.

[0056] It can be understood that the data flow characteristic period of the present invention records the actual duration of effective data transmission between device nodes. By calculating the average duration of all data flow characteristic periods and setting the average duration as the response waiting time, it can not only avoid normal data transmission being misjudged as timeout due to too short waiting time, but also prevent resources from being idle due to too long a duration, thereby providing a quantitative standard for a reasonable waiting boundary of data requests, matching the response waiting time with the actual data flow capacity, and realizing the correlation identification and analysis of the data flow status of adjacent nodes across the data space, and identifying the invalid flow phenomenon of data flow in the time and space dimension of data flow.

[0057] This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement the data circulation method across data spaces provided by the above-mentioned embodiment.

[0058] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0059] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A data circulation method across data spaces, characterized in that: include: Acquire adjacent device nodes on a data transmission path to determine a number of data circulation spaces; Draw the flow change curves of data flow of different device nodes over time, and determine several flow status explicit nodes on the flow change curves; Sort the nodes with explicit circulation status according to the time sequence relationship, determine a number of data circulation characteristic time periods based on the preset first constraint condition, and analyze the continuity of the data circulation characteristic time periods to determine whether there is invalid circulation between adjacent device nodes; The first constraint condition includes the distribution state of the circulation state explicit nodes; In response to the existence of the 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 according to a preset second constraint condition, whether to transmit the data of the space network cache node to the target device node is determined; The second constraint condition is a comparison result of the total value of data traffic transmitted between different nodes within the response waiting time.

2. The data circulation method across data spaces according to claim 1, characterized in that: The process of determining a plurality of data circulation spaces based on the node parameters includes: respectively obtaining branch transmission paths on source circulation nodes and target circulation nodes in adjacent device nodes; Each branch transmission path on the source circulation node is determined as the data circulation space of the source circulation node, and each branch transmission path on the target circulation node is determined as the data circulation space of the target circulation node.

3. The data circulation method across data spaces according to claim 2, characterized in that: The process of determining several flow state explicit nodes on the flow change curve includes: 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 a preset duration as the horizontal axis; 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 a preset duration as the horizontal axis; Determining absolute values ​​of curve slopes of a plurality of data points on the first flow change curve and the second flow change curve respectively; The data point whose absolute value of the slope of the curve exceeds a preset slope reference value is determined as a flow state dominant node.

4. The data circulation method across data spaces according to claim 3, characterized in that: The process of determining the characteristic period of data circulation includes: Taking the flow state dominant nodes on the first flow change curve as the starting points of the time period in sequence; Determine the flow state explicit node on the second flow change curve with the shortest interval between the time interval and the start point of the time interval as the end point of the time interval in time sequence; Determine a period consisting of the period start point and the period end point as the data flow characteristic period; Among them, the end point of the previous data circulation characteristic time period in each data circulation characteristic time period is earlier than the start point of the next data circulation characteristic time period in terms of time sequence relationship.

5. The data circulation method across data spaces according to claim 4, characterized in that: The process of analyzing the continuity of the characteristic period of data circulation includes: Obtain the cumulative duration of all data flow characteristic periods; The ratio of the accumulated duration value to the duration of the monitoring period is determined as the continuity characteristic value.

6. The data circulation method across data spaces according to claim 5, characterized in that: The process of comparing the continuity characteristic value with a preset continuity characteristic reference value and determining whether invalid flow exists between adjacent device nodes according to the comparison result includes: If the continuity characteristic representation value does not exceed the preset continuity characteristic representation reference value, it is determined that invalid flow exists between adjacent device nodes.

7. The data circulation method across data spaces according to claim 6, characterized in that: The process of establishing the interface mapping relationship between the spatial network cache node and the source device node includes: Establishing a first transmission mapping relationship between the spatial network cache node and the source device node, so that data of the source device node is transmitted to the spatial network cache node for caching; The total value of data flow from the source device node to the spatial network cache node within the response waiting time is obtained.

8. The data circulation method across data spaces according to claim 7, characterized in that: The process of establishing the interface mapping relationship between the spatial network cache node and the target device node includes: Establishing a second transmission mapping relationship between the spatial network cache node and the target device node, so that the spatial network cache node receives the data request of the target device node; Determine the total data flow value corresponding to the data request of the target device node within the response waiting time.

9. The data circulation method across data spaces according to claim 8, characterized in that: Comparing the total data traffic value corresponding to the data request with the total data traffic value of the spatial network cache node; If the total value of the data traffic corresponding to the data request exceeds the total value of the data traffic of the space network cache node, it is determined to transmit the data of the space network cache node to the target device node.

10. The data circulation method across data spaces according to claim 8, characterized in that: The process for determining response latency includes: Obtain the duration of each data circulation characteristic period; Calculate the average duration of all data flow characteristic periods; An average value of the durations is determined as the response waiting time.

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