A data transmission optimization method and system based on an energy big data center

Abstract

The present application relates to the technical field of data transmission, and more particularly to a data transmission optimization method and system based on an energy big data center, which identifies bandwidth bottlenecks in advance through a flow prediction mode, avoids the processing lag caused by the traditional passive alarm and manual intervention mode, responds to the bandwidth bottleneck in advance, determines the reference priority corresponding to each energy data type according to the data consumption information prediction result, determines the data priority in combination with the business demand, ensures the high-demand data to be transmitted in priority when the data is transmitted in a priority layering architecture, avoids the delay or interruption of key businesses due to insufficient bandwidth, improves the efficiency of business data transmission, in addition, the low-priority data shares the remaining bandwidth through time division multiplexing, improves the balance of data transmission of each data source, the data transmission optimization does not need to modify the device itself, and when a new data source or a new energy data type is added, it can quickly adapt, and the expansibility and flexibility are relatively strong.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to a data transmission optimization method and system based on an energy big data center. Background Technology

[0002] To strengthen the aggregation and management of energy big data and address issues such as difficulty in data aggregation, fragmented management of data resources, poor timeliness of data access, and cross-regional network isolation, existing methods utilize energy big data centers to aggregate and integrate various types of energy indicator data. This allows for the aggregation and access of energy data indicators such as electricity, coal, oil, and natural gas from multiple data sources, thereby providing a data foundation for energy scenario construction, data services, and other application scenarios.

[0003] In existing technologies, energy big data centers typically deploy network security isolation devices. These devices only receive data with specific protocols before forwarding it to the data platform of the energy big data center, thereby improving the security and reliability of data transmission and storage.

[0004] However, when transmitting data through network security isolation devices, data traffic fluctuations may cause transmission delays or interruptions, which in turn may lead to service delays or interruptions. This results in poor efficiency in transmitting data required by the service. Existing methods require replacing network security isolation devices to provide higher data transmission bandwidth, and the ease and convenience of data transmission optimization are both poor.

[0005] Therefore, improving the efficiency of business data transmission has become an urgent problem to be solved. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention provides a data transmission optimization method based on an energy big data center, which includes:

[0007] S101, based on the reference traffic of the network isolation device obtained at I preset time points, predict the traffic corresponding to the target time point, where I is a positive integer.

[0008] S102, if the predicted flow rate is greater than the preset flow rate threshold, then the predicted flow rate is obtained by predicting the data consumption information of N data consumption terminals obtained at I preset time points, and the predicted flow rate information corresponding to the target time point is obtained. The predicted flow rate information includes the predicted flow rate of J energy data types, where N and J are positive integers.

[0009] S103, Based on the predicted consumption information, determine the reference priority corresponding to each type of energy data.

[0010] S104. Obtain the data generation distribution information corresponding to M data sources respectively. Based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type, determine the target priority threshold, where M is a positive integer.

[0011] S105, for any data source, after the target time point, if the reference priority of the energy data type to which the collected data sent by the data source to the network isolation device belongs is greater than the target priority threshold, then the collected data is directly sent to the network isolation device; otherwise, the collected data is sent to the network isolation device in a time-division multiplexing manner.

[0012] The present invention also provides a data transmission optimization system based on an energy big data center, the data transmission optimization system based on an energy big data center comprising:

[0013] The traffic prediction module is used to predict the traffic corresponding to the target time point based on the reference traffic of the network isolation device obtained at I preset time points, where I is a positive integer.

[0014] The consumption prediction module is used to predict the consumption information corresponding to the target time point by using the data consumption information of N data consumption terminals obtained at I preset time points if the predicted flow is greater than the preset flow threshold. The predicted consumption information includes the predicted consumption data volume corresponding to J energy data types, where N and J are positive integers.

[0015] The priority determination module is used to determine the reference priority corresponding to each type of energy data based on the predicted consumption information.

[0016] The threshold determination module is used to obtain the data generation distribution information corresponding to M data sources respectively, and determine the target priority threshold based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type, where M is a positive integer;

[0017] The data transmission module is configured to, for any data source, after the target time point, if the reference priority of the energy data type to which the collected data sent by the data source to the network isolation device belongs is greater than the target priority threshold, then directly send the collected data to the network isolation device; otherwise, send the collected data to the network isolation device using time-division multiplexing.

[0018] This invention has at least the following beneficial effects: it identifies bandwidth bottlenecks in advance through traffic prediction, avoiding the processing delays caused by manual intervention after traditional passive alarms, and responds to bandwidth bottlenecks in advance. Based on the prediction results of data consumption information, it determines the reference priority corresponding to each energy data type, and determines the data priority in combination with business needs. When transmitting data according to the priority-based hierarchical architecture, it ensures that high-demand data is transmitted first, avoiding delays or interruptions to critical business due to insufficient bandwidth, and improving the efficiency of business data transmission. In addition, low-priority data shares the remaining bandwidth through time-division multiplexing, improving the balance of data transmission from various data sources, and also avoiding data loss caused by data accumulation in local data sources. Furthermore, data transmission optimization does not require modification of the device itself, and it can quickly adapt when adding new data sources or introducing new energy data types, exhibiting strong scalability and flexibility. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a data transmission optimization method based on an energy big data center provided in Embodiment 1 of the present invention;

[0021] Figure 2 This is a schematic diagram of a data transmission optimization system based on an energy big data center, provided in Embodiment 2 of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is understood that, where appropriate, the terms used to distinguish similar objects can be interchanged so that the invention can also be implemented in other embodiments besides the illustrated or described embodiments. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0024] Example 1

[0025] This embodiment provides a data transmission optimization method based on an energy big data center, such as... Figure 1 The diagram shows a flowchart of a data transmission optimization method based on an energy big data center, provided by an embodiment of the present invention. This data transmission optimization method based on an energy big data center includes:

[0026] S101, based on the reference traffic of the network isolation device obtained at I preset time points, predict the traffic corresponding to the target time point, where I is a positive integer;

[0027] S102, if the predicted flow rate is greater than the preset flow rate threshold, then the predicted flow rate is obtained by predicting the data consumption information of N data consumption terminals obtained at I preset time points, and the predicted consumption information corresponding to the target time point is obtained. The predicted consumption information includes the predicted consumption data volume corresponding to J energy data types, where N and J are positive integers.

[0028] S103, Based on the predicted consumption information, determine the reference priority corresponding to each type of energy data;

[0029] S104, obtain the data generation distribution information corresponding to M data sources respectively, and determine the target priority threshold based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type respectively, where M is a positive integer;

[0030] S105, for any data source, after the target time point, if the reference priority of the energy data type to which the collected data sent by the data source to the network isolation device belongs is greater than the target priority threshold, then the collected data is directly sent to the network isolation device; otherwise, the collected data is sent to the network isolation device in a time-division multiplexing manner.

[0031] The data source can refer to edge devices, including sensors and edge terminals, while the data consumer can correspond to different business types and be used to consume data.

[0032] Network isolation devices can be used to improve the security of data transmission, while data middleware can be used for data distribution, storage, and real-time computing.

[0033] The time interval between adjacent preset time points can be a fixed value. The preset traffic threshold can be calculated based on the maximum bandwidth of the network isolation device, the preset bandwidth ratio, and the time interval between adjacent preset time points. The preset traffic threshold is obtained by multiplying the maximum bandwidth, the preset bandwidth ratio, and the time interval between adjacent preset time points. In this embodiment, the preset bandwidth ratio can be set to 0.9.

[0034] Energy data types can include electricity, coal, natural gas, etc. Furthermore, under the electricity type, it can be divided into power generation, load, electricity price, etc., and under the coal type, it can include calorific value, output, inventory, etc. Implementers can determine the energy data type according to actual needs.

[0035] Data consumption information may include the actual consumption data volume corresponding to J energy data types. The actual consumption data volume may refer to the consumption data volume from the previous preset time point to the corresponding preset time point.

[0036] Reference priority can be used to characterize the transmission priority of data of different energy types.

[0037] In one specific implementation, the energy big data center includes a data middle platform, which includes a public database and a Datahub message queue. The network isolation device is used to receive collected data conforming to a preset protocol and then send the received collected data to the public database. The public database is used to push the received collected data to the Datahub message queue. The Datahub message queue is used to distribute the received collected data to N data consumers for data consumption.

[0038] The default protocol can refer to the JDBC (Java Database Connectivity) protocol. The Datahub message queue supports high throughput, low latency, and elastic scaling of data flow requirements. It can be responsible for receiving, buffering, and distributing massive amounts of data in real time, solving the speed matching problem between the data source and the computing / storage system.

[0039] Specifically, the data platform can also include a stream computing engine to support the cleaning, transformation, aggregation, and analysis of real-time data in the Datahub message queue, and output results that can be directly used for business decisions.

[0040] The data platform may also include a storage database, which is used for long-term storage of data in the Datahub message queue.

[0041] In one specific implementation, the step of predicting the traffic corresponding to the target time point based on the reference traffic of the network isolation device obtained at I preset time points includes:

[0042] The reference traffic of the network isolation device obtained at I preset time points is input into the trained traffic prediction model for prediction, so as to obtain the predicted traffic corresponding to the target time point.

[0043] Among them, the traffic prediction model can adopt the time series prediction model, which can be selected from the recurrent neural network model, long short-term memory network model, temporal convolution model, etc. The architecture and training of the time series prediction model are existing technologies, and will not be elaborated here.

[0044] Specifically, the target time point can be the (I+K)th preset time point. The reference traffic obtained from the first preset time point to the first preset time point is input into the trained traffic prediction model for prediction to obtain the predicted traffic at the (I+1)th preset time point. Then, the reference traffic obtained from the second preset time point to the first preset time point and the predicted traffic at the (I+1)th preset time point are input into the trained traffic prediction model for prediction to obtain the predicted traffic at the (I+2)th preset time point, and so on, until the predicted traffic corresponding to the (I+K)th preset time point is obtained. K is a positive integer. In this embodiment, K can be set to 5.

[0045] In one specific implementation, the step of predicting the data consumption information of N data consumers obtained at I preset time points to obtain the predicted consumption information corresponding to the target time point includes:

[0046] For any preset time point, the data consumption information corresponding to each data consumption terminal obtained at that preset time point is added together, and the sum is used as the comprehensive consumption information corresponding to that preset time point.

[0047] The comprehensive consumption information corresponding to I preset time points is input into the trained consumption information prediction model for prediction, so as to obtain the predicted consumption information corresponding to the target time point.

[0048] The data consumption information may include the actual amount of data consumed by the corresponding data consumer under the J energy data types, and the comprehensive consumption information may include the comprehensive amount of data consumed by all data consumers under the J energy data types.

[0049] Consumer information prediction models can adopt time-series prediction models. The architecture and training of time-series prediction models are existing technologies. The inference process of time-series prediction models can be found in the inference process of traffic prediction models, and will not be repeated here.

[0050] In one specific implementation, based on the predicted consumption information, a reference priority is determined for each type of energy data, including:

[0051] Based on the predicted consumption data volume corresponding to each energy data type, the energy data types are sorted to obtain the first type sequence;

[0052] For any energy data type, determine the initial priority of the energy data type based on its position in the first type sequence;

[0053] Based on the predicted consumption data volume corresponding to the energy data type and the comprehensive consumption data volume corresponding to the energy data type in the comprehensive consumption information at the I-th preset time point, determine the change value of the consumption data volume corresponding to the energy data type.

[0054] The consumption data change value is mapped to the priority offset value corresponding to the energy data type using a preset mapping function;

[0055] The reference priority corresponding to the energy data type is determined based on the initial priority and priority offset value corresponding to the energy data type.

[0056] In the first type sequence, the energy data type with the higher the predicted consumption data volume is ranked later, which is easy to know. The initial priority value range is an integer in [1, J].

[0057] Specifically, the change in the amount of consumption data corresponding to the energy data type is obtained by subtracting the amount of comprehensive consumption data corresponding to the energy data type in the comprehensive consumption information at the I-th preset time point.

[0058] The default mapping function can be y = 0.5tan -1(p×x), where x is the change in the amount of consumption data, y is the priority offset value, and p is the adjustment coefficient. This adjustment coefficient can be used to support different data volume levels in different scenarios. In this embodiment, p can be set to 1 / 1000. The priority offset value ranges from (-1, 1). The priority offset value can characterize the change in the degree of data demand. When the priority offset value corresponding to a certain energy data type is close to -1, it can be indicated that the degree of data demand for that energy data type has been greatly reduced, and its reference priority should also be reduced accordingly.

[0059] The initial priority and priority offset value corresponding to the energy data type are added together, and the result is used as the temporary priority corresponding to the energy data type. Based on the temporary priorities corresponding to each energy data type, the energy data types are reordered to obtain the second type sequence. For any energy data type, the reference priority corresponding to the energy data type is determined according to the position of the energy data type in the second type sequence, so as to ensure that the reference priorities are all integers, which facilitates subsequent processing.

[0060] In one specific implementation, the step of generating distribution information and reference priorities corresponding to each energy data type based on the data from each data source, and determining the target priority threshold, includes:

[0061] The distribution information of the data from each of the acquired data sources is generated and summed together, and the sum is used as the overall distribution information.

[0062] The target priority threshold is determined based on the overall distribution information and the reference priorities corresponding to each energy data type.

[0063] The data generation distribution information can include the data increments corresponding to each energy data type from the previous preset time point to the current preset time point. Correspondingly, the overall distribution information can include the data increments corresponding to each energy data type from all data sources from the previous preset time point to the current preset time point.

[0064] In one specific implementation, determining the target priority threshold based on the overall distribution information and the reference priorities corresponding to each energy data type includes:

[0065] The overall distribution information is adjusted according to the reference priority corresponding to each energy data type to obtain the target distribution information;

[0066] Obtain a preset traffic ratio, and determine the target priority threshold based on the target distribution information, the predicted traffic corresponding to the target time point, and the preset traffic ratio.

[0067] The target distribution information identifies energy data types with corresponding reference priorities. In other words, the target distribution information includes the data increments corresponding to each reference priority.

[0068] Specifically, the predicted traffic at the target time point is multiplied by the preset traffic ratio to obtain the multiplication result. The reference priorities are traversed from largest to smallest. If a certain reference priority meets the condition, the sum of the data increments corresponding to each reference priority greater than that reference priority is calculated. If the sum of the calculated data increments is greater than the multiplication result, then that reference priority is used as the target priority threshold.

[0069] In one specific implementation, the preset traffic threshold is initially set as a base traffic value;

[0070] Following step S104, the following steps are also included:

[0071] The preset traffic threshold is updated based on the predicted traffic corresponding to the target time point.

[0072] In order to avoid frequent updates to the reference priority and target priority thresholds, which would lead to a waste of computing resources, this embodiment updates the preset traffic threshold after determining the target priority threshold. Specifically, the update method can be to compare the predicted traffic with the preset bandwidth ratio, and update the preset traffic threshold based on the ratio calculation result. Thus, when the predicted traffic does not change much, the reference priority and target priority thresholds will not be updated, and when the predicted traffic changes significantly, the reference priority and target priority thresholds will be updated.

[0073] In one specific implementation, after step S102, the following step is further included:

[0074] If the predicted traffic is less than or equal to the base traffic value, then the target priority threshold is set to the first preset value, the preset traffic threshold is reset to the base traffic value, and the process jumps to step S105.

[0075] If the predicted traffic is less than or equal to the base traffic value, it means that the corresponding target time point is no longer a case of insufficient data transmission bandwidth. The target priority threshold is set to the first preset value, which can be 0. Then, the process jumps to step S105. At this time, the reference priority of the collected data of any energy data type is greater than the target priority threshold, that is, all collected data are directly sent to the network isolation device.

[0076] In one specific implementation, the network isolation device is used to determine the target source corresponding to the target time slice from M data sources according to a preset arbitration method, and send a receiving signal to the target source corresponding to the target time slice at the start time of the target time slice, wherein the start time of the target time slice is greater than the target time slice.

[0077] The step of sending the collected data to the network isolation device using time-division multiplexing includes:

[0078] When the data source receives the received signal, it sends the collected data to the network isolation device.

[0079] The starting time of the target time slice can be the (I+K+Q)th preset time point, and the ending time of the target time slice can be the (I+K+R)th preset time point, where Q is an integer greater than or equal to zero, and R is an integer greater than Q.

[0080] Preset arbitration methods can include round-robin arbitration, priority arbitration based on the data source priority determined by the amount of non-high-demand data, etc.

[0081] Specifically, starting from the target time point, several target time slices are divided. The network isolation device selects the target source corresponding to each target time slice through a round-robin arbitration method. In the corresponding time slice, it only receives the collection data sent by the target source, realizing time-division multiplexing of bandwidth. Thus, under the premise of unchanged overall transmission efficiency, the balance of transmission efficiency of each data source is improved, and the situation of data loss caused by data accumulation in the local data source due to high demand data occupying bandwidth is avoided.

[0082] In this first embodiment, bandwidth bottlenecks are identified in advance through traffic prediction, avoiding the processing delays caused by manual intervention after traditional passive alarms. This allows for early response to bandwidth bottlenecks. Based on the predicted data consumption information, reference priorities are determined for each type of energy data. Combined with business needs, data priorities are determined. When transmitting data according to the priority-based hierarchical architecture, high-demand data is prioritized for transmission, preventing delays or interruptions to critical services due to insufficient bandwidth and improving the efficiency of business data transmission. In addition, low-priority data shares the remaining bandwidth through time-division multiplexing, improving the balance of data transmission across data sources and preventing data loss due to data accumulation at local data sources. Furthermore, data transmission optimization does not require modification to the device itself and can be quickly adapted when adding new data sources or introducing new energy data types, demonstrating strong scalability and flexibility.

[0083] Example 2

[0084] This second embodiment provides a data transmission optimization system based on an energy big data center, such as... Figure 2The diagram shown is a schematic of a data transmission optimization system based on an energy big data center according to Embodiment 2 of the present invention. This data transmission optimization system based on an energy big data center includes:

[0085] The traffic prediction module 201 is used to predict the traffic corresponding to the target time point based on the reference traffic of the network isolation device obtained at I preset time points, where I is a positive integer.

[0086] The consumption prediction module 202 is used to predict the consumption information corresponding to the target time point by using the data consumption information of N data consumption terminals obtained at I preset time points if the predicted flow is greater than the preset flow threshold. The predicted consumption information includes the predicted consumption data volume corresponding to J energy data types, where N and J are positive integers.

[0087] The priority determination module 203 is used to determine the reference priority corresponding to each type of energy data based on the predicted consumption information.

[0088] The threshold determination module 204 is used to obtain the data generation distribution information corresponding to M data sources respectively, and determine the target priority threshold based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type, where M is a positive integer;

[0089] The data transmission module 205 is configured to, for any data source, after the target time point, if the reference priority of the energy data type to which the collected data sent by the data source to the network isolation device belongs is greater than the target priority threshold, then directly send the collected data to the network isolation device; otherwise, send the collected data to the network isolation device using time-division multiplexing.

[0090] It should be noted that the specific limitations of the data transmission optimization system based on the energy big data center can be found in the limitations of the data transmission optimization method based on the energy big data center mentioned above, and will not be repeated here. The information interaction and execution process between the above modules are based on the same concept as the method embodiments of this invention, and their specific functions and technical effects can be found in the method embodiments section, and will not be repeated here.

[0091] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A data transmission optimization method based on an energy big data center, characterized in that, The data transmission optimization method includes: S101, based on the reference traffic of the network isolation device obtained at I preset time points, predict the traffic corresponding to the target time point, where I is a positive integer; S102, if the predicted flow rate is greater than the preset flow rate threshold, then the predicted flow rate is obtained by predicting the data consumption information of N data consumption terminals obtained at I preset time points, and the predicted consumption information corresponding to the target time point is obtained. The predicted consumption information includes the predicted consumption data volume corresponding to J energy data types, where N and J are positive integers. S103, Based on the predicted consumption information, determine the reference priority corresponding to each type of energy data; S104, Obtain data generation distribution information corresponding to M data sources respectively, and determine the target priority threshold based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type respectively, where M is a positive integer. The data generation distribution information includes the data increment corresponding to each energy data type of the corresponding data source between the previous preset time point and the current preset time point. The overall distribution information includes the data increment corresponding to each energy data type of all data sources between the previous preset time point and the current preset time point. Determining the target priority threshold based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type includes: The distribution information of the data from each of the acquired data sources is generated and summed together, and the sum is used as the overall distribution information. The overall distribution information is adjusted according to the reference priority corresponding to each energy data type to obtain the target distribution information, wherein the target distribution information includes the data increment corresponding to each reference priority. Obtain a preset traffic ratio, multiply the predicted traffic corresponding to the target time point by the preset traffic ratio to obtain the multiplication result, and iterate through each reference priority from largest to smallest. If any reference priority meets the condition, the current reference priority is used as the target priority threshold. The condition is: the sum of the data increments corresponding to each reference priority of the current reference priority is greater than the multiplication result. S105, for any data source, after the target time point, if the reference priority of the energy data type to which the collected data sent by the data source to the network isolation device belongs is greater than the target priority threshold, then the collected data is directly sent to the network isolation device; otherwise, the collected data is sent to the network isolation device in a time-division multiplexing manner.

2. The data transmission optimization method based on an energy big data center according to claim 1, characterized in that, The energy big data center includes a data platform, which includes a public database and a Datahub message queue. The network isolation device is used to receive collected data conforming to a preset protocol and then send the received collected data to the public database. The public database is used to push the received collected data to the Datahub message queue. The Datahub message queue is used to distribute the received collected data to N data consumers for data consumption.

3. The data transmission optimization method based on an energy big data center according to claim 1, characterized in that, The step of predicting the target time point based on the reference traffic of the network isolation device obtained at I preset time points includes: The reference traffic of the network isolation device obtained at I preset time points is input into the trained traffic prediction model for prediction, so as to obtain the predicted traffic corresponding to the target time point.

4. The data transmission optimization method based on an energy big data center according to claim 1, characterized in that, The step of predicting the data consumption information of N data consumers obtained at I preset time points to obtain the predicted consumption information corresponding to the target time point includes: For any preset time point, the data consumption information corresponding to each data consumption terminal obtained at the preset time point is added together, and the sum is used as the comprehensive consumption information corresponding to the preset time point. The comprehensive consumption information includes the comprehensive consumption data volume corresponding to all data consumption terminals under J energy data types. The comprehensive consumption information corresponding to I preset time points is input into the trained consumption information prediction model for prediction, so as to obtain the predicted consumption information corresponding to the target time point.

5. The data transmission optimization method based on an energy big data center according to claim 4, characterized in that, Based on the predicted consumption information, the reference priority corresponding to each type of energy data is determined, including: Based on the predicted consumption data volume corresponding to each energy data type, the energy data types are sorted to obtain the first type sequence; For any energy data type, determine the initial priority of the energy data type based on its position in the first type sequence; Based on the predicted consumption data volume corresponding to the energy data type and the comprehensive consumption data volume corresponding to the energy data type in the comprehensive consumption information at the I-th preset time point, determine the change value of the consumption data volume corresponding to the energy data type. The consumption data change value is mapped to the priority offset value corresponding to the energy data type using a preset mapping function; The reference priority corresponding to the energy data type is determined based on the initial priority and priority offset value corresponding to the energy data type.

6. The data transmission optimization method based on an energy big data center according to claim 1, characterized in that, The preset flow threshold is initially set to the base flow value; Following step S104, the following steps are also included: The preset traffic threshold is updated based on the predicted traffic corresponding to the target time point.

7. The data transmission optimization method based on an energy big data center according to claim 6, characterized in that, Following step S102, the following steps are also included: If the predicted traffic is less than or equal to the base traffic value, then the target priority threshold is set to the first preset value, the preset traffic threshold is reset to the base traffic value, and the process jumps to step S105.

8. The data transmission optimization method based on an energy big data center according to claim 1, characterized in that, The network isolation device is used to determine the target source corresponding to the target time slice from M data sources according to a preset arbitration method, and send a receiving signal to the target source corresponding to the target time slice at the start time of the target time slice, wherein the start time of the target time slice is greater than the target time slice. The step of sending the collected data to the network isolation device using time-division multiplexing includes: When the data source receives the received signal, it sends the collected data to the network isolation device.

9. A data transmission optimization system based on an energy big data center, characterized in that, The data transmission optimization system based on the energy big data center includes: The traffic prediction module is used to predict the traffic corresponding to the target time point based on the reference traffic of the network isolation device obtained at I preset time points, where I is a positive integer. The consumption prediction module is used to predict the consumption information corresponding to the target time point by using the data consumption information of N data consumption terminals obtained at I preset time points if the predicted flow is greater than the preset flow threshold. The predicted consumption information includes the predicted consumption data volume corresponding to J energy data types, where N and J are positive integers. The priority determination module is used to determine the reference priority corresponding to each type of energy data based on the predicted consumption information. The threshold determination module is used to acquire data generation distribution information corresponding to M data sources, and determine a target priority threshold based on the data generation distribution information corresponding to each data source and the reference priority corresponding to each energy data type, where M is a positive integer. The data generation distribution information includes the data increment corresponding to each energy data type of the corresponding data source between the previous preset time point and the current preset time point, and the overall distribution information includes the data increment corresponding to each energy data type of all data sources between the previous preset time point and the current preset time point. The threshold determination module is also used for: The distribution information of the data from each of the acquired data sources is generated and summed together, and the sum is used as the overall distribution information. The overall distribution information is adjusted according to the reference priority corresponding to each energy data type to obtain the target distribution information, wherein the target distribution information includes the data increment corresponding to each reference priority. Obtain a preset traffic ratio, multiply the predicted traffic corresponding to the target time point by the preset traffic ratio to obtain the multiplication result, and iterate through each reference priority from largest to smallest. If any reference priority meets the condition, the current reference priority is used as the target priority threshold. The condition is: the sum of the data increments corresponding to each reference priority of the current reference priority is greater than the multiplication result. The data transmission module is configured to, for any data source, after the target time point, if the reference priority of the energy data type to which the collected data sent by the data source to the network isolation device belongs is greater than the target priority threshold, then directly send the collected data to the network isolation device; otherwise, send the collected data to the network isolation device using time-division multiplexing.

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