A data processing method and system for thermal system monitoring

Abstract

The present invention relates to the field of data processing technology, and discloses a data processing method and system for thermal system monitoring. The method generates an initialization ranking value for each data packet to be processed based on the acquisition time and a preset score decay function to obtain an initial data packet chain to be processed; obtains the historical processing behavior of each data packet to be processed, and calculates the data processing importance; determines the historical processing time of each data packet to be processed, and calculates the data processing difficulty; calculates the data processing factor according to the data processing importance and the data processing difficulty, and sets the correction coefficient of the data packet to be processed; corrects the initialization ranking value based on the correction coefficient to obtain a target ranking value, updates the initial data packet chain to obtain a target data packet chain to be processed, and can dynamically adjust the data processing priority according to the actual situation of the data packet to be processed, so as to achieve more efficient and intelligent data processing, and improve the data processing flexibility and adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing method and system for thermal system monitoring. Background Art

[0002] The thermal system is an intelligent system that integrates data collection, processing, analysis and remote monitoring. It mainly monitors and manages the operation status of the heat exchange station in real time. The thermal system can collect various parameters in the heat exchange station in real time, such as temperature, pressure, flow, etc., to ensure the timeliness and accuracy of the data. By analyzing the collected data, the operating efficiency of the system can be evaluated and decision support can be provided for optimizing operation.

[0003] With the rapid development of information technology, the amount of data in thermal systems has grown exponentially. With limited computing resources, how to efficiently process and transmit this data has become a major challenge. Traditional data processing methods mainly include weight-based queue scheduling algorithms and real-time scheduling algorithms. Among them, the weighted queue scheduling algorithm: assigns different weights to each data stream to determine its processing priority, and data streams with high weights will be served more frequently. Real-time scheduling algorithm: arranges the processing order according to the deadline of the data to ensure that the most urgent tasks are completed first. Although the above methods have solved the problem of data processing to a certain extent, they often lack flexibility and adaptability, and cannot cope well with the rapid changes in data volume and data type, as well as the need for dynamic adjustment of priorities. Summary of the invention

[0004] In view of this, the present invention proposes a data processing method and system for thermal system monitoring, which can dynamically adjust the priority of data processing in combination with the actual situation of the data to achieve more efficient and intelligent data processing.

[0005] The present invention proposes a data processing method for thermal system monitoring, comprising:

[0006] Determine all data packets to be processed, generate an initial ranking value for each data packet to be processed based on the acquisition time and a preset score decay function, and obtain an initial chain of data packets to be processed;

[0007] Obtaining historical processing behavior of each data packet to be processed, analyzing the historical processing behavior, and calculating the data processing importance of each data packet to be processed based on the analysis result;

[0008] Determine the historical processing time of each data packet to be processed, and calculate the data processing difficulty of each data packet to be processed based on the historical processing time;

[0009] Calculating a data processing factor for each data packet to be processed according to the data processing importance and the data processing difficulty, and setting a correction coefficient for the data packet to be processed based on the data processing factor;

[0010] The initialization sorting value is corrected based on the correction coefficient to obtain a target sorting value, and the initial to-be-processed data packet chain is updated according to the numerical value relationship of the target sorting value to obtain a target to-be-processed data packet chain.

[0011] Further, when all the data packets to be processed are determined, an initial ranking value is generated for each data packet to be processed based on the collection time and a preset score decay function, and an initial chain of data packets to be processed is obtained, including:

[0012] Determine the acquisition timestamp of each data packet to be processed;

[0013] Determine the position order of the data packets to be processed in the time series according to the acquisition timestamp of each data packet to be processed;

[0014] An initialization ranking value of each data packet to be processed is determined based on a preset score decay function, wherein the score decay function assigns the initialization ranking value according to the shift of the time series position, and the initialization ranking value decreases step by step.

[0015] Furthermore, when obtaining the historical processing behavior of each data packet to be processed, analyzing the historical processing behavior, and calculating the data processing importance of each data packet to be processed based on the analysis result, it includes:

[0016] Obtaining a reasonable range of processing behaviors corresponding to each historical processing behavior, wherein the reasonable range of processing behaviors includes a first preset processing behavior value and a second preset processing behavior value, and the first preset processing behavior value is less than the second preset processing behavior value;

[0017] Generate a low external processing behavior set according to all historical processing behaviors that are less than the first preset processing behavior value;

[0018] Generate a high-external processing behavior set according to all historical processing behaviors that are greater than the second preset processing behavior value;

[0019] Generate an internal processing behavior set according to all historical processing behaviors that are greater than or equal to the first preset processing behavior value and less than or equal to the second preset processing behavior value;

[0020] The first set number of the low external processing behavior set, the second set number of the high external processing behavior set and the third set number of the internal processing behavior set are counted, and the data processing importance of each data packet to be processed is calculated.

[0021] Further, when counting the first set number of the low external processing behavior set, the second set number of the high external processing behavior set, and the third set number of the internal processing behavior set, and calculating the data processing importance of each data packet to be processed, it includes:

[0022] A first calculation coefficient is configured for the internal processing behavior set, and a second calculation coefficient is configured for the high external processing behavior set;

[0023] The data processing importance of each data packet to be processed is calculated according to the following formula:

[0024] ;

[0025] Among them, P is the importance of data processing, e1 is the first calculation coefficient, n is the number of third sets, s i is the i-th historical processing behavior in the internal processing behavior set, k1 is the first preset processing behavior value corresponding to the i-th historical processing behavior, f i is the weight corresponding to the i-th historical processing behavior, e2 is the second calculation coefficient, m is the number of the second set, g j is the jth historical processing behavior in the high-external processing behavior set, k2 is the second preset processing behavior value corresponding to the jth historical processing behavior, b j is the weight corresponding to the jth historical processing behavior, h is the adjustment factor of data processing importance, e1+e2=3, and e1<e2.

[0026] Furthermore, the adjustment factor h of the data processing importance is determined according to the following steps:

[0027] Processing the high-level external processing behavior set based on a first preset processing method to obtain a high-level external processing behavior sequence;

[0028] Calculate the first average value and the first variance of the high external processing behavior sequence, extract all historical data behaviors greater than the first average value and greater than the first variance, and count the number of corresponding first behaviors;

[0029] Processing the internal processing behavior set based on a second preset processing method to obtain an internal processing behavior sequence;

[0030] Calculating a second mean value and a second variance of the internal processing behavior sequence, extracting all historical data behaviors that are greater than the second mean value and greater than the second variance, and counting the number of corresponding second behaviors;

[0031] Determining a behavior quantity and a value according to the first behavior quantity and the second behavior quantity;

[0032] Presetting a first preset adjustment factor, a second preset adjustment factor, and a third preset adjustment factor;

[0033] When the number of the first sets is less than the number and value of the behaviors, the first preset adjustment factor is used as the adjustment factor h of the data processing importance;

[0034] When the number of the first sets is equal to the number and value of the behaviors, the second preset adjustment factor is used as the adjustment factor h of the data processing importance;

[0035] When the number of the first sets is greater than the number and value of the behaviors, the third preset adjustment factor is used as the adjustment factor h of the data processing importance.

[0036] Further, when determining the historical processing time of each data packet to be processed, and calculating the data processing difficulty of each data packet to be processed according to the historical processing time, it includes:

[0037] Determine the number of historical processing times of each data packet to be processed, and when the number of historical processing times is less than or equal to a preset number, set the data processing difficulty of the corresponding data packet to be processed to zero;

[0038] When the number of historical processing durations is greater than the preset number, a preset standard processing duration is obtained, and an excellent processing mark is generated for the historical processing duration that is less than the standard processing duration;

[0039] Generating a qualified processing mark for a historical processing time equal to the standard processing time;

[0040] Generate a timeout processing mark for a historical processing time that is greater than the standard processing time;

[0041] The data processing difficulty of each data packet to be processed is calculated according to the excellent processing mark, the qualified processing mark and the timeout processing mark.

[0042] Further, when calculating the data processing difficulty of each to-be-processed data packet according to the excellent processing mark, the qualified processing mark and the timeout processing mark, it includes:

[0043] The data processing difficulty of each data packet to be processed is calculated according to the following formula:

[0044] ;

[0045] Among them, W is the difficulty of data processing, D1 is the number of tags corresponding to the excellent processing tag, D2 is the number of tags corresponding to the qualified processing tag, and D3 is the number of tags corresponding to the timeout processing tag.

[0046] Further, when calculating the data processing factor of each data packet to be processed according to the data processing importance and the data processing difficulty, it includes:

[0047] configuring a third calculation coefficient for the data processing importance, and configuring a fourth calculation coefficient for the data processing difficulty;

[0048] The data processing factor for each packet to be processed is calculated according to the following formula:

[0049] ;

[0050] Wherein, T is a data processing factor, r1 is a third calculation coefficient, r2 is a fourth calculation coefficient, r1+r2=1, and r1>r2.

[0051] Furthermore, when setting the correction coefficient of the data packet to be processed based on the data processing factor, it includes:

[0052] Presetting a first preset data processing factor and a second preset data processing factor;

[0053] Presetting a first preset correction coefficient, a second preset correction coefficient, and a third preset correction coefficient;

[0054] When a first preset condition is identified, the first preset correction coefficient is selected as the correction coefficient of the data packet to be processed;

[0055] When the second preset condition is identified, the second preset correction coefficient is selected as the correction coefficient of the data packet to be processed;

[0056] When the third preset condition is identified, the third preset correction coefficient is selected as the correction coefficient of the data packet to be processed;

[0057] Among them, the first preset condition includes that the data processing factor is less than the first preset data processing factor, the second preset condition includes that the data processing factor is greater than or equal to the first preset data processing factor and less than the second preset data processing factor, and the third preset condition includes that the data processing factor is greater than or equal to the second preset data processing factor.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] Based on the collection time and the preset score decay function, an initial sorting value is generated for each data packet to be processed to obtain an initial chain of data packets to be processed; the historical processing behavior of each data packet to be processed is obtained to calculate the data processing importance; the historical processing time of each data packet to be processed is determined to calculate the data processing difficulty; the data processing factor is calculated according to the data processing importance and the data processing difficulty, and the correction coefficient of the data packet to be processed is set; the initial sorting value is corrected based on the correction coefficient to obtain a target sorting value, the initial chain of data packets to be processed is updated to obtain a target chain of data packets to be processed, and the priority of data processing can be dynamically adjusted according to the actual situation of the data packets to be processed to achieve more efficient and intelligent data processing and improve data processing flexibility and adaptability.

[0060] On the other hand, the present application also provides a data processing system for thermal system monitoring, comprising:

[0061] The first module is used to determine all data packets to be processed, generate an initial ranking value for each data packet to be processed based on the collection time and a preset score decay function, and obtain an initial chain of data packets to be processed;

[0062] The second module is used to obtain the historical processing behavior of each data packet to be processed, analyze the historical processing behavior, and calculate the data processing importance of each data packet to be processed based on the analysis result;

[0063] The third module is used to determine the historical processing time of each data packet to be processed, and calculate the data processing difficulty of each data packet to be processed according to the historical processing time;

[0064] A fourth module is used to calculate a data processing factor of each data packet to be processed according to the data processing importance and the data processing difficulty, and set a correction coefficient of the data packet to be processed based on the data processing factor;

[0065] The fifth module is used to correct the initialization sort value based on the correction coefficient to obtain a target sort value, and update the initial to-be-processed data packet chain according to the numerical value relationship of the target sort value to obtain a target to-be-processed data packet chain.

[0066] It is understandable that the data processing system and method for thermal system monitoring provided above have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0068] Figure 1 A schematic flow chart of a data processing method for thermal system monitoring provided by an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of the structure of a data processing system for thermal system monitoring provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0071] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a data processing method for thermal system monitoring, including:

[0072] S110: determining all data packets to be processed, generating an initial ranking value for each data packet to be processed based on the collection time and a preset score decay function, and obtaining an initial chain of data packets to be processed;

[0073] In this embodiment, each data packet to be processed is packaged with data to be transmitted, including but not limited to environmental data (such as ambient temperature, ambient humidity, etc.), operating data (such as operating temperature, operating pressure, operating flow, etc.), energy consumption data, equipment status data, fault alarm data, etc.

[0074] In some embodiments of the present application, when all the data packets to be processed are determined, an initialization ranking value is generated for each data packet to be processed based on the acquisition time and a preset score decay function, and an initial chain of data packets to be processed is obtained, including:

[0075] Determine the acquisition timestamp of each data packet to be processed;

[0076] Determine the position order of the data packets to be processed in the time series according to the acquisition timestamp of each data packet to be processed;

[0077] An initialization ranking value of each data packet to be processed is determined based on a preset score decay function, wherein the score decay function assigns the initialization ranking value according to the shift of the time series position, and the initialization ranking value decreases step by step.

[0078] In this embodiment, the score decay function is a function commonly used in fields such as information retrieval, machine learning, and data analysis, and is used to adjust the score according to the distance or difference between a certain numerical feature and a target value, and specifically includes: linear decay function, exponential decay function, Gaussian decay function, and time decay function. You can choose according to actual needs.

[0079] In this embodiment, the to-be-processed data packets collected earlier in time are assigned a higher initialization ranking value, while the to-be-processed data packets collected later in time are assigned a lower initialization ranking value.

[0080] The beneficial effect of the above technical solution is that the present invention can assign an initial sorting value to the data packets to be processed according to the acquisition time, realize the initial sorting, and construct an initial chain of data packets to be processed, laying the foundation for subsequent processing.

[0081] S120: Obtaining historical processing behaviors of each data packet to be processed, analyzing the historical processing behaviors, and calculating the data processing importance of each data packet to be processed based on the analysis results;

[0082] In some embodiments of the present application, when obtaining the historical processing behavior of each data packet to be processed, analyzing the historical processing behavior, and calculating the data processing importance of each data packet to be processed based on the analysis result, it includes:

[0083] Obtaining a reasonable range of processing behaviors corresponding to each historical processing behavior, wherein the reasonable range of processing behaviors includes a first preset processing behavior value and a second preset processing behavior value, and the first preset processing behavior value is less than the second preset processing behavior value;

[0084] Generate a low external processing behavior set according to all historical processing behaviors that are less than the first preset processing behavior value;

[0085] Generate a high-external processing behavior set according to all historical processing behaviors that are greater than the second preset processing behavior value;

[0086] Generate an internal processing behavior set according to all historical processing behaviors that are greater than or equal to the first preset processing behavior value and less than or equal to the second preset processing behavior value;

[0087] The first set number of the low external processing behavior set, the second set number of the high external processing behavior set and the third set number of the internal processing behavior set are counted, and the data processing importance of each data packet to be processed is calculated.

[0088] In this embodiment, the historical processing behavior includes the number of processing times, the number of downloads, the number of clicks, the number of reprints, the number of calls, etc.

[0089] In this embodiment, the reasonable range of processing behavior corresponds to the historical processing behavior one by one. For example, if the historical processing behavior is the number of processing times, the corresponding reasonable range of processing behavior is [5, 10], 5 is the first preset processing behavior value, 10 is the second preset processing behavior value, and the rest are no longer shown one by one.

[0090] The beneficial effect of the above technical solution is: the present invention counts the first set number of low external processing behavior sets, the second set number of high external processing behavior sets and the third set number of internal processing behavior sets, and calculates the data processing importance of each data packet to be processed. By calculating the data processing importance of the data packet to be processed, it can lay the foundation for updating and optimizing the initial chain of data packets to be processed and provide reliable data support.

[0091] In some embodiments of the present application, when counting the first set number of the low external processing behavior set, the second set number of the high external processing behavior set, and the third set number of the internal processing behavior set, and calculating the data processing importance of each data packet to be processed, it includes:

[0092] A first calculation coefficient is configured for the internal processing behavior set, and a second calculation coefficient is configured for the high external processing behavior set;

[0093] The data processing importance of each data packet to be processed is calculated according to the following formula:

[0094] ;

[0095] Among them, P is the importance of data processing, e1 is the first calculation coefficient, n is the number of third sets, s i is the i-th historical processing behavior in the internal processing behavior set, k1 is the first preset processing behavior value corresponding to the i-th historical processing behavior, f i is the weight corresponding to the i-th historical processing behavior, e2 is the second calculation coefficient, m is the number of the second set, g j is the jth historical processing behavior in the high-external processing behavior set, k2 is the second preset processing behavior value corresponding to the jth historical processing behavior, b j is the weight corresponding to the jth historical processing behavior, h is the adjustment factor of data processing importance, e1+e2=3, and e1<e2.

[0096] In this embodiment, the first calculation coefficient is preferably 1.12, and the second calculation coefficient is preferably 1.88.

[0097] In some embodiments of the present application, the adjustment factor h of the data processing importance is determined according to the following steps:

[0098] Processing the high-level external processing behavior set based on a first preset processing method to obtain a high-level external processing behavior sequence;

[0099] Calculate the first average value and the first variance of the high external processing behavior sequence, extract all historical data behaviors greater than the first average value and greater than the first variance, and count the number of corresponding first behaviors;

[0100] Processing the internal processing behavior set based on a second preset processing method to obtain an internal processing behavior sequence;

[0101] Calculating a second mean value and a second variance of the internal processing behavior sequence, extracting all historical data behaviors that are greater than the second mean value and greater than the second variance, and counting the number of corresponding second behaviors;

[0102] Determining a behavior quantity and a value according to the first behavior quantity and the second behavior quantity;

[0103] Presetting a first preset adjustment factor, a second preset adjustment factor, and a third preset adjustment factor;

[0104] When the number of the first sets is less than the number and value of the behaviors, the first preset adjustment factor is used as the adjustment factor h of the data processing importance;

[0105] When the number of the first sets is equal to the number and value of the behaviors, the second preset adjustment factor is used as the adjustment factor h of the data processing importance;

[0106] When the number of the first sets is greater than the number and value of the behaviors, the third preset adjustment factor is used as the adjustment factor h of the data processing importance.

[0107] In this embodiment, the first preset processing method is normalization processing, the second preset processing method is standardization processing, the high external processing behavior sequence is the normalization sequence, and the internal processing behavior is the standardization sequence.

[0108] In this embodiment, all historical data behaviors greater than the first average value are extracted, all historical data behaviors greater than the first variance are extracted, the existence of intersection data behaviors is determined, and the number of first behaviors is counted.

[0109] In this embodiment, the first preset adjustment factor is preferably 0.95, the second preset adjustment factor is preferably 1.05, and the third preset adjustment factor is preferably 1.15.

[0110] The beneficial effect of the above technical solution is: the present invention selects the corresponding preset adjustment factor according to the relationship between the calculated number and value of behaviors and the number of the first set, and then adjusts the data processing importance, thereby realizing dynamic adjustment of the data processing importance, ensuring the calculation accuracy of the data processing importance, avoiding data errors, and ensuring the comprehensiveness of the calculation of the data processing importance through multi-faceted calculations.

[0111] S130: Determine the historical processing time of each data packet to be processed, and calculate the data processing difficulty of each data packet to be processed according to the historical processing time;

[0112] In some embodiments of the present application, when determining the historical processing time of each data packet to be processed, and calculating the data processing difficulty of each data packet to be processed according to the historical processing time, it includes:

[0113] Determine the number of historical processing times of each data packet to be processed, and when the number of historical processing times is less than or equal to a preset number, set the data processing difficulty of the corresponding data packet to be processed to zero;

[0114] When the number of historical processing durations is greater than the preset number, a preset standard processing duration is obtained, and an excellent processing mark is generated for the historical processing duration that is less than the standard processing duration;

[0115] Generating a qualified processing mark for a historical processing time equal to the standard processing time;

[0116] Generate a timeout processing mark for a historical processing time that is greater than the standard processing time;

[0117] The data processing difficulty of each data packet to be processed is calculated according to the excellent processing mark, the qualified processing mark and the timeout processing mark.

[0118] In this embodiment, each historical processing number corresponds to a historical processing duration.

[0119] In this embodiment, determining the amount of historical processing time is to determine whether there is any historical processing behavior.

[0120] In this embodiment, the standard processing time can be comprehensively determined based on the size of the data packet to be processed and the throughput, concurrency number, CPU performance, etc. of the data processor. No specific limitation is made here, and the standard processing time is set based on actual conditions.

[0121] The beneficial effect of the above technical solution is: the present invention calculates the data processing difficulty of each data packet to be processed according to the excellent processing mark, the qualified processing mark and the timeout processing mark, thereby ensuring the calculation accuracy of the data processing difficulty, and at the same time providing another data support for data priority sorting, thereby ensuring the sorting accuracy of data priority.

[0122] In some embodiments of the present application, when calculating the data processing difficulty of each to-be-processed data packet according to the excellent processing mark, the qualified processing mark and the timeout processing mark, it includes:

[0123] The data processing difficulty of each data packet to be processed is calculated according to the following formula:

[0124] ;

[0125] Among them, W is the difficulty of data processing, D1 is the number of tags corresponding to the excellent processing tag, D2 is the number of tags corresponding to the qualified processing tag, and D3 is the number of tags corresponding to the timeout processing tag.

[0126] S140: calculating a data processing factor for each data packet to be processed according to the data processing importance and the data processing difficulty, and setting a correction coefficient for the data packet to be processed based on the data processing factor;

[0127] In some embodiments of the present application, when calculating the data processing factor of each data packet to be processed according to the data processing importance and the data processing difficulty, it includes:

[0128] configuring a third calculation coefficient for the data processing importance, and configuring a fourth calculation coefficient for the data processing difficulty;

[0129] The data processing factor for each packet to be processed is calculated according to the following formula:

[0130] ;

[0131] Wherein, T is a data processing factor, r1 is a third calculation coefficient, r2 is a fourth calculation coefficient, r1+r2=1, and r1>r2.

[0132] In this embodiment, the third calculation coefficient is 0.6, and the fourth calculation coefficient is 0.4.

[0133] The beneficial effect of the above technical solution is that the present invention calculates the data processing factor of each data packet to be processed according to the data processing importance and data processing difficulty, and then can accurately set the correction coefficient of the data packet to be processed to achieve correction adjustment of the initialization sorting value.

[0134] In some embodiments of the present application, when setting the correction coefficient of the data packet to be processed based on the data processing factor, it includes:

[0135] Presetting a first preset data processing factor and a second preset data processing factor;

[0136] Presetting a first preset correction coefficient, a second preset correction coefficient, and a third preset correction coefficient;

[0137] When a first preset condition is identified, the first preset correction coefficient is selected as the correction coefficient of the data packet to be processed;

[0138] When the second preset condition is identified, the second preset correction coefficient is selected as the correction coefficient of the data packet to be processed;

[0139] When the third preset condition is identified, the third preset correction coefficient is selected as the correction coefficient of the data packet to be processed;

[0140] Among them, the first preset condition includes that the data processing factor is less than the first preset data processing factor, the second preset condition includes that the data processing factor is greater than or equal to the first preset data processing factor and less than the second preset data processing factor, and the third preset condition includes that the data processing factor is greater than or equal to the second preset data processing factor.

[0141] In this embodiment, the first preset data processing factor is preferably 1.8, and the second preset data processing factor is preferably 2.8.

[0142] In this embodiment, the first preset correction coefficient is preferably 0.85, the second preset correction coefficient is preferably 1, and the third preset correction coefficient is preferably 1.25.

[0143] The beneficial effect of the above technical solution is that the present invention can select a corresponding correction coefficient according to the relationship between the data processing factor, the first preset data processing factor and the second preset data processing factor, thereby improving work efficiency and avoiding complex calculations.

[0144] S150: Correcting the initialization sorting value based on the correction coefficient to obtain a target sorting value, and updating the initial to-be-processed data packet chain according to the numerical value relationship of the target sorting value to obtain a target to-be-processed data packet chain.

[0145] In this embodiment, the product value of the correction coefficient and the initialization ranking value is calculated, and then the product value is used as the target ranking value.

[0146] In this embodiment, priority data processing is performed based on the target to-be-processed data packet chain.

[0147] In this embodiment, if there are equal target ranking values, the to-be-processed data packet with a larger initialization ranking value will be processed preferentially.

[0148] The beneficial effect of the above technical solution is that the priority of data processing can be dynamically adjusted according to the actual situation of the data packets to be processed, so as to achieve more efficient and intelligent data processing and improve the flexibility and adaptability of data processing.

[0149] like Figure 2 As shown, in another preferred embodiment based on the above embodiment, this embodiment provides a data processing system for thermal system monitoring, including:

[0150] The first module is used to determine all data packets to be processed, generate an initial ranking value for each data packet to be processed based on the collection time and a preset score decay function, and obtain an initial chain of data packets to be processed;

[0151] The second module is used to obtain the historical processing behavior of each data packet to be processed, analyze the historical processing behavior, and calculate the data processing importance of each data packet to be processed based on the analysis result;

[0152] The third module is used to determine the historical processing time of each data packet to be processed, and calculate the data processing difficulty of each data packet to be processed according to the historical processing time;

[0153] A fourth module is used to calculate a data processing factor of each data packet to be processed according to the data processing importance and the data processing difficulty, and set a correction coefficient of the data packet to be processed based on the data processing factor;

[0154] The fifth module is used to correct the initialization sort value based on the correction coefficient to obtain a target sort value, and update the initial to-be-processed data packet chain according to the numerical value relationship of the target sort value to obtain a target to-be-processed data packet chain.

[0155] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0156] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A data processing method for thermal system monitoring, characterized in that: include: Determine all data packets to be processed, generate an initial ranking value for each data packet to be processed based on the acquisition time and a preset score decay function, and obtain an initial chain of data packets to be processed; Obtaining historical processing behavior of each data packet to be processed, analyzing the historical processing behavior, and calculating the data processing importance of each data packet to be processed based on the analysis result; Determine the historical processing time of each data packet to be processed, and calculate the data processing difficulty of each data packet to be processed based on the historical processing time; Calculating a data processing factor for each data packet to be processed according to the data processing importance and the data processing difficulty, and setting a correction coefficient for the data packet to be processed based on the data processing factor; The initialization sorting value is corrected based on the correction coefficient to obtain a target sorting value, and the initial to-be-processed data packet chain is updated according to the numerical value relationship of the target sorting value to obtain a target to-be-processed data packet chain.

2. The data processing method for thermal system monitoring according to claim 1, characterized in that: When all the data packets to be processed are determined, an initial sort value is generated for each data packet to be processed based on the collection time and a preset score decay function, and an initial chain of data packets to be processed is obtained, including: Determine the acquisition timestamp of each data packet to be processed; Determine the position order of the data packets to be processed in the time series according to the acquisition timestamp of each data packet to be processed; An initialization ranking value of each data packet to be processed is determined based on a preset score decay function, wherein the score decay function assigns the initialization ranking value according to the shift of the time series position, and the initialization ranking value decreases step by step.

3. The data processing method for thermal system monitoring according to claim 1, characterized in that: When obtaining the historical processing behavior of each data packet to be processed, analyzing the historical processing behavior, and calculating the data processing importance of each data packet to be processed based on the analysis result, it includes: Obtaining a reasonable range of processing behaviors corresponding to each historical processing behavior, wherein the reasonable range of processing behaviors includes a first preset processing behavior value and a second preset processing behavior value, and the first preset processing behavior value is less than the second preset processing behavior value, and the historical processing behaviors include processing times, download times, click times, reprint times, and call times; Generate a low external processing behavior set according to all historical processing behaviors that are less than the first preset processing behavior value; Generate a high-external processing behavior set according to all historical processing behaviors that are greater than the second preset processing behavior value; Generate an internal processing behavior set according to all historical processing behaviors that are greater than or equal to the first preset processing behavior value and less than or equal to the second preset processing behavior value; The first set number of the low external processing behavior set, the second set number of the high external processing behavior set and the third set number of the internal processing behavior set are counted, and the data processing importance of each data packet to be processed is calculated.

4. The data processing method for thermal system monitoring according to claim 3, characterized in that: When counting the first set number of the low external processing behavior set, the second set number of the high external processing behavior set, and the third set number of the internal processing behavior set, and calculating the data processing importance of each data packet to be processed, including: A first calculation coefficient is configured for the internal processing behavior set, and a second calculation coefficient is configured for the high external processing behavior set; The data processing importance of each data packet to be processed is calculated according to the following formula: Among them, P is the importance of data processing, e1 is the first calculation coefficient, n is the number of third sets, s i is the i-th historical processing behavior in the internal processing behavior set, k1 is the first preset processing behavior value corresponding to the i-th historical processing behavior, f i is the weight corresponding to the i-th historical processing behavior, e2 is the second calculation coefficient, m is the number of the second set, g j is the jth historical processing behavior in the high-external processing behavior set, k2 is the second preset processing behavior value corresponding to the jth historical processing behavior, b j is the weight corresponding to the jth historical processing behavior, h is the adjustment factor of data processing importance, e1+e2=3, and e1<e2.

5. The data processing method for thermal system monitoring according to claim 4, characterized in that: Determine the adjustment factor h of data processing importance according to the following steps: Processing the high-level external processing behavior set based on a first preset processing method to obtain a high-level external processing behavior sequence; Calculate the first average value and the first variance of the high external processing behavior sequence, extract all historical data behaviors greater than the first average value and greater than the first variance, and count the number of corresponding first behaviors; Processing the internal processing behavior set based on a second preset processing method to obtain an internal processing behavior sequence; Calculating a second mean value and a second variance of the internal processing behavior sequence, extracting all historical data behaviors that are greater than the second mean value and greater than the second variance, and counting the number of corresponding second behaviors; Determining a behavior quantity and a value according to the first behavior quantity and the second behavior quantity; Presetting a first preset adjustment factor, a second preset adjustment factor, and a third preset adjustment factor; When the number of the first sets is less than the number and value of the behaviors, the first preset adjustment factor is used as the adjustment factor h of the data processing importance; When the number of the first sets is equal to the number and value of the behaviors, the second preset adjustment factor is used as the adjustment factor h of the data processing importance; When the number of the first sets is greater than the number and value of the behaviors, the third preset adjustment factor is used as the adjustment factor h of the data processing importance.

6. The data processing method for thermal system monitoring according to claim 4, characterized in that: When determining the historical processing time of each data packet to be processed, and calculating the data processing difficulty of each data packet to be processed according to the historical processing time, it includes: Determine the number of historical processing times of each data packet to be processed, and when the number of historical processing times is less than or equal to a preset number, set the data processing difficulty of the corresponding data packet to be processed to zero; When the number of historical processing durations is greater than the preset number, a preset standard processing duration is obtained, and an excellent processing mark is generated for the historical processing duration that is less than the standard processing duration; Generating a qualified processing mark for a historical processing time equal to the standard processing time; Generate a timeout processing mark for a historical processing time that is greater than the standard processing time; The data processing difficulty of each data packet to be processed is calculated according to the excellent processing mark, the qualified processing mark and the timeout processing mark.

7. The data processing method for thermal system monitoring according to claim 6, characterized in that: When calculating the data processing difficulty of each to-be-processed data packet according to the excellent processing mark, the qualified processing mark and the timeout processing mark, it includes: The data processing difficulty of each data packet to be processed is calculated according to the following formula: Among them, W is the difficulty of data processing, D1 is the number of tags corresponding to the excellent processing tag, D2 is the number of tags corresponding to the qualified processing tag, and D3 is the number of tags corresponding to the timeout processing tag.

8. The data processing method for thermal system monitoring according to claim 7, characterized in that: When calculating the data processing factor of each data packet to be processed according to the data processing importance and the data processing difficulty, it includes: configuring a third calculation coefficient for the data processing importance, and configuring a fourth calculation coefficient for the data processing difficulty; The data processing factor for each packet to be processed is calculated according to the following formula: T=r1×(1+P) W +r2×(1+W) P ; Wherein, T is a data processing factor, r1 is a third calculation coefficient, r2 is a fourth calculation coefficient, r1+r2=1, and r1>r2.

9. The data processing method for thermal system monitoring according to claim 1, characterized in that: When the correction coefficient of the data packet to be processed is set based on the data processing factor, it includes: Presetting a first preset data processing factor and a second preset data processing factor; Presetting a first preset correction coefficient, a second preset correction coefficient, and a third preset correction coefficient; When a first preset condition is identified, the first preset correction coefficient is selected as the correction coefficient of the data packet to be processed; When the second preset condition is identified, the second preset correction coefficient is selected as the correction coefficient of the data packet to be processed; When the third preset condition is identified, the third preset correction coefficient is selected as the correction coefficient of the data packet to be processed; Among them, the first preset condition includes that the data processing factor is less than the first preset data processing factor, the second preset condition includes that the data processing factor is greater than or equal to the first preset data processing factor and less than the second preset data processing factor, and the third preset condition includes that the data processing factor is greater than or equal to the second preset data processing factor.

10. A data processing system for thermal system monitoring, characterized in that: include: The first module is used to determine all data packets to be processed, generate an initial ranking value for each data packet to be processed based on the collection time and a preset score decay function, and obtain an initial chain of data packets to be processed; The second module is used to obtain the historical processing behavior of each data packet to be processed, analyze the historical processing behavior, and calculate the data processing importance of each data packet to be processed based on the analysis result; The third module is used to determine the historical processing time of each data packet to be processed, and calculate the data processing difficulty of each data packet to be processed according to the historical processing time; A fourth module is used to calculate a data processing factor of each data packet to be processed according to the data processing importance and the data processing difficulty, and set a correction coefficient of the data packet to be processed based on the data processing factor; The fifth module is used to correct the initialization sort value based on the correction coefficient to obtain a target sort value, and update the initial to-be-processed data packet chain according to the numerical value relationship of the target sort value to obtain a target to-be-processed data packet chain.

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