Compression method and compression system for industrial process time series data

Abstract

The embodiment of the invention provides an industrial process time sequence data compression method and a compression system thereof, and belongs to the field of time sequence data compression. The compression method comprises the following steps: establishing an industrial process time sequence data model dictionary corresponding to an industrial model based on the industrial model and time sequence data characteristics generated by process variables corresponding to the model; performing variable grouping on each industrial process time series data model process variable to obtain a variable group corresponding to the data model; setting a data model parameter for each process variable in the variable group, and performing variable relation mapping and business logic decomposition on the process variable after the data model parameter is set to obtain a variable mapping relation and a business logic decomposition result; according to the business logic decomposition result, corresponding compression parameters are set for the same process variable under the multiple working conditions. According to the invention, efficient data compression can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of time-series data compression, and specifically relates to a compression method for industrial process time-series data, a compression system for industrial process time-series data, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the increasing demand for time-series big data, various time-series databases for processing time-series data have also developed accordingly. To increase the storage capacity, the time-series databases integrate data compression algorithms. However, the data compression algorithms applied in the time-series databases are general compression algorithms designed according to the characteristics of time-series data, and are compression methods adopted based on the common data characteristics of various time-series data. For example, Simple8b is mainly for integer data compression, XOR is for floating-point number compression, Delta and Delta-of-Delta are mainly for data compression based on the data change law, and RLE is mainly for data compression when the data remains unchanged for a long time. However, in actual applications, they are all applied in specific industrial scenarios, and each scenario has its own data characteristics and data characteristic concerns. Therefore, targeted data modeling and encoding can be carried out in these aspects to further improve the compression efficiency and the extraction efficiency of key data. For example, in most industrial automation production lines, especially in process industrial production lines, during production, to ensure product quality, the control system will have control models and control algorithms. These models are based on process knowledge and cannot be accurately described by simple time-series data feature statistics or real-time model prediction methods. For example, a temperature drop model consists of multiple process variables, coefficients, and exponential functions, etc., which is relatively complex. Therefore, the present invention proposes a joint-channel dynamic hybrid of multiple compression algorithms based on an industrial process data model dictionary, which can achieve efficient data compression. Summary of the Invention

[0003] The purpose of the embodiments of the present invention is to provide a compression method for industrial process time-series data, a compression system for industrial process time-series data, an electronic device, a computer-readable storage medium, and a computer program product, and this compression method can partially or completely solve the technical problem of low compression efficiency in the prior art.

[0004] To achieve the above object, an embodiment of the present invention provides a method for compressing industrial process time-series data. The compression method includes: establishing an industrial process time-series data model dictionary corresponding to the industrial model based on the time-series data characteristics generated by the industrial model and the process variables corresponding to the industrial model; grouping the process variables of each industrial process time-series data model in the industrial process time-series data model dictionary to obtain a variable group corresponding to each industrial process time-series data model; setting data model parameters for each process variable in the variable group, and performing variable relationship mapping and business logic decomposition on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables, where the business logic decomposition result includes multiple working conditions and the logical relationships between the process variables under multiple working conditions; and, respectively setting corresponding compression parameters for the same process variable under multiple working conditions according to the multiple working conditions and the logical relationships between the process variables under multiple working conditions, where the compression parameters include multiple compression algorithms.

[0005] Optionally, the compression parameters further include a compression trigger condition and a compression accuracy, where the compression trigger condition includes multiple trigger channels and multiple trigger logics.

[0006] Optionally, the compression method further includes: respectively performing hybrid switching compression of the compression algorithm and the compression accuracy on the time-series data of each process variable under multiple working conditions according to the compression requirements corresponding to the multiple working conditions.

[0007] Optionally, the data model parameters include a maximum value, a minimum value, a default value above the upper limit, and a default value below the lower limit. The maximum value is the largest numerical value in the normal working value range of each process variable, and the minimum value is the smallest numerical value in the normal working value range of each process variable. Setting the data model parameters for each process variable in the variable group includes: when the default value above the upper limit and the default value below the lower limit are related to fault analysis, the default value above the upper limit is the maximum value plus a specific number of process units or a first specific value convenient for time-series compression, and the default value below the lower limit is the minimum value minus a specific number of process units or a second specific value convenient for time-series compression; or, when the default value above the upper limit and the default value below the lower limit are not related to fault analysis, the maximum value is determined as the default value above the upper limit, and the minimum value is determined as the default value below the lower limit.

[0008] Optionally, the process variables of each industrial process time-series data model in the industrial process time-series data model dictionary are grouped to obtain a variable group corresponding to each industrial process time-series data model, including: grouping according to multiple business units belonging to the same industrial process time-series data model in the industrial process time-series data model dictionary to obtain a variable group corresponding to each industrial process time-series data model.

[0009] On the other hand, the present invention also provides a compression system for industrial process time-series data. The compression system includes: a construction module for establishing an industrial process time-series data model dictionary corresponding to the industrial model based on the industrial model and the time-series data characteristics generated by the process variables corresponding to the industrial model; a first acquisition module for grouping the process variables of each industrial process time-series data model in the industrial process time-series data model dictionary to obtain a variable group corresponding to each industrial process time-series data model; a second acquisition module for setting data model parameters for each process variable in the variable group, and performing variable relationship mapping and business logic decomposition on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables, where the business logic decomposition result includes multiple working conditions and the logical relationship between the process variables under multiple working conditions; and a setting module for setting corresponding compression parameters for the same process variable under multiple working conditions according to the logical relationship between the multiple working conditions and the process variables under multiple working conditions, where the compression parameters include multiple compression algorithms.

[0010] Optionally, the compression parameters further include a compression trigger condition and a compression accuracy, where the compression trigger condition includes multiple trigger channels and multiple trigger logics.

[0011] Optionally, the compression system further includes: a compression module for performing hybrid switching compression of the compression algorithm and the compression accuracy on the time-series data of each process variable under multiple working conditions according to the compression requirements corresponding to the multiple working conditions.

[0012] Optionally, the data model parameters include a maximum value, a minimum value, a default value for exceeding the upper limit, and a default value for exceeding the lower limit. Wherein, the maximum value is the largest numerical value of each process variable within the normal operating value range, and the minimum value is the smallest numerical value of each process variable within the normal operating value range. Setting data model parameters for each process variable in the variable group includes: when the default value for exceeding the upper limit and the default value for exceeding the lower limit are relevant to fault analysis, the default value for exceeding the upper limit is the maximum value plus a specific number of process units or a first specific value facilitating time series compression, and the default value for exceeding the lower limit is the minimum value minus a specific number of process units or a second specific value facilitating time series compression; or, when the default value for exceeding the upper limit and the default value for exceeding the lower limit are not relevant to fault analysis, the maximum value is determined as the default value for exceeding the upper limit, and the minimum value is determined as the default value for exceeding the lower limit.

[0013] Optionally, the first acquisition module is configured to group process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model, including: grouping according to multiple service units belonging to the same industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model.

[0014] On the other hand, an embodiment of the present invention further provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the compression method for industrial process time series data as described above.

[0015] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the compression method for industrial process time series data as described above.

[0016] On the other hand, an embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the compression method for industrial process time series data as described above.

[0017] Through the above technical solution, the compression method includes: establishing an industrial process time-series data model dictionary corresponding to the industrial model based on the process model and the time-series data characteristics generated by the process variables corresponding to the process model; grouping the process variables of each industrial process time-series data model in the industrial process time-series data model to obtain a variable group corresponding to each industrial process time-series data model; setting data model parameters for each process variable in the variable group, and performing variable relationship mapping and business logic decomposition on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables, where the business logic decomposition result includes multiple working conditions and the logical relationships between the process variables under multiple working conditions; and, according to the multiple working conditions and the logical relationships between the process variables under multiple working conditions, respectively setting corresponding compression parameters for the same process variable under multiple working conditions for the process variables in the actual problem under each working condition, where the compression parameters include multiple compression algorithms. This method first gives a time-series data model and defines the compression characteristics by setting model parameters and compression parameters, thereby achieving efficient data compression.

[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0020] Figure 1 is a flowchart of the compression method for industrial process time-series data provided by the embodiments of the present invention;

[0021] Figure 2 is a structural diagram of the compression system for industrial process time-series data provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will describe in detail the specific implementation of the embodiments of the present invention in conjunction with the drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiments of the present invention and does not limit the embodiments of the present invention.

[0023] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. In the embodiments of this application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0024] As Figure 1 Figure 1 This is a flowchart of a method for compressing industrial process control time series data provided by the first embodiment of the present invention. A method for compressing industrial process time series data, the compression method includes the following steps S10 to S13.

[0025] In step S10, based on the time series data characteristics generated by the industrial model and the process variables corresponding to the industrial model, an industrial process time series data model dictionary corresponding to the industrial model is established.

[0026] Exemplarily, the industrial model is an existing typical process model, control model, equipment model. For example, the process model includes a temperature model, a continuous rolling tension model, a rolling schedule model, etc.; the control model includes a temperature control model, a tension control model, a PID adjustment model, etc.; the equipment model includes a reducer model, a rack model, etc.; the time series data characteristics are the characteristics of the actual industrial process time series data generated by calculating and executing according to the industrial model, such as two-point switching characteristics, linear change characteristics, linear correlation characteristics, following change characteristics, data range characteristics, etc.

[0027] For example, an industrial production line includes a heating furnace temperature model, a heating furnace temperature control model, and a PID adjustment model. In the temperature model, the curve shapes and ranges of the temperature models for different steel grades are inconsistent. Suppose there are two steel grades A and B. At this time, the temperature model and model curve characteristics (i.e., time series data characteristics) of the heating furnace need to be divided into two industrial process time series data models (abbreviated as data models); similarly, for the time series data characteristics of the PID adjustment model for the position of the transverse transfer trolley and the PID adjustment model for the position of the rotary arm are inconsistent, they also need to be divided into multiple data models in the data model dictionary. If there are multiple trolleys or rotary arms, data models can be established for each trolley and rotary arm to form an industrial process time series data model (data model) dictionary.

[0028] In step S11, the process variables of each industrial process time series data model in the industrial process time series data model are grouped to obtain a variable group corresponding to each industrial process time series data model.

[0029] Further, grouping the process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model includes: grouping according to multiple business units belonging to the same industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model.

[0030] Exemplarily, still taking a certain industrial production line in the above steps as an example, the cross - transfer trolley position PID adjustment model and the rotary arm position PID adjustment model belonging to the same industrial process time - series data model need to be divided into two time - series data models because their time - series data characteristics are inconsistent. Further, for the same time - series data model, such as the cross - transfer trolley position PID adjustment model, it is grouped separately according to business units (for example, there are multiple trolleys, and each trolley is regarded as a business unit). For example, there are 5 trolleys with different functions, so the process variables corresponding to the industrial process time - series data models of the 5 trolleys are divided into 5 groups, corresponding to the 5 groups of trolleys respectively.

[0031] Taking the cross - transfer trolley position PID adjustment model as an example, the original formula of the model is:

[0032]

[0033] Where: u t represents the output, K p represents the proportional coefficient, T i represents the integral time constant, T d represents the derivative time constant, e t represents the set value P ref and the deviation from the actual value P act is d, t represents the sampling period, de t : the rate of change of the error.

[0034] In the industrial control system, it needs to be changed to the discrete form for use, and its formula is:

[0035]

[0036] Where, In the cross - transfer trolley position PID adjustment model, K d = 0, and when e max ≤ e k , the proportional is enabled. When e min < e k < e max , the integral is enabled. When e k ≤ e min , the output is 0, that is:

[0037] u k = K p e k e max ≤ e k (3)

[0038]

[0039] uk = 0, e k ≤ e min (5)

[0040] e k = P ref -P act , (6)

[0041] Meanwhile, there is a relationship equation between the set rotational speed rpm of the trolley and the output u k as follows:

[0042] rpm = u k K rpm (7)

[0043] The e in the above formula k represents the deviation between the set value and the actual value, e max represents the maximum value of the deviation, e min represents the minimum value of the deviation. The above PID model involves the trolley position reference value P ref , the actual value P act , the error e, the proportional amplification coefficient K p , the integral constant T i , the sampling period △t, the PID output value u, the set rotational speed rpm and other process variables. Meanwhile, there are also the operating current I, the operation instruction C run , the stop instruction C stop and the business logic variables related to the industrial process time series data model. Therefore, the process variables and related variables of these models are unified into a group to form the variable group corresponding to each industrial process time series data model.

[0044] According to the same principle, variable grouping can be performed on the process variables based on each data model in the model dictionary. As needed, the same variable can be assigned to different data models.

[0045] For example, for the transverse movement trolley position PID adjustment model and the rotary arm position PID adjustment model, although the industrial process time series data characteristics of the two models are not the same, if the two are in the same process section, there are the same business logic related variables such as the regional operation instruction C run , the stop instruction C stop . Then, for the convenience of compressing logical judgment, the two industrial process time series data models both contain the same process variables. Therefore, the same variable is assigned to different industrial process time series data models.

[0046] In step S12, data model parameters are set for each process variable in the variable group, and variable relationship mapping and business logic decomposition are performed on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables.

[0047] Among them, the variable relationship mapping is the corresponding relationship between the process variables corresponding to the industrial model and the process variables in the industrial process time series data model, and the logical decomposition result includes the logical relationships between multiple working conditions and the process variables under multiple working conditions.

[0048] Furthermore, the data model parameters include a maximum value, a minimum value, a default value for exceeding the upper limit, and a default value for exceeding the lower limit. Among them, the maximum value is the largest numerical value of each process variable within the normal working value range, and the minimum value is the smallest numerical value of each process variable within the normal working value range. Setting data model parameters for each process variable in the variable group includes: when the default value for exceeding the upper limit and the default value for exceeding the lower limit are related to fault analysis, the default value for exceeding the upper limit is the maximum value plus a specific number of process units or a first specific value convenient for time series compression, and the default value for exceeding the lower limit is the minimum value minus a specific number of process units or a second specific value convenient for time series compression; or, when the default value for exceeding the upper limit and the default value for exceeding the lower limit are not related to fault analysis, the maximum value is determined as the default value for exceeding the upper limit, and the minimum value is determined as the default value for exceeding the lower limit.

[0049] Exemplarily, setting data model parameters for each process variable in each variable group includes data type, maximum value, default value for exceeding the upper limit, default value for exceeding the lower limit, etc. Table 1 is a setting table for variable group data model parameters, and the set values are all set according to the actual needs of the industrial model and fault analysis. For example, P ref is the position reference value, 0 - 10000 mm is its normal working range (its corresponding minimum value is 0, and the maximum value is 10000), and exceeding the limit means an unexpected fault occurs and needs to be checked. And for K p, this value belongs to the set value and will not be adjusted frequently. Once the specific value is confirmed, there will be no error. At the same time, restricting its maximum and minimum values can meet the requirements without considering the over-limit situation. Therefore, the default value for exceeding the upper limit can directly use the maximum value, and the default value for exceeding the lower limit can directly use the minimum value. The general principle is that if the over-limit value (the default value for exceeding the upper limit and the default value for exceeding the lower limit) is meaningful for analyzing faults, then consider the over-limit value. If it is meaningless for analyzing faults, then the over-limit value does not need to be set separately. The default value for exceeding the upper limit can use the maximum value, and the default value for exceeding the lower limit can use the minimum value. At the same time, for the convenience of compression, the selection principle of the default value for exceeding (the upper or lower) limit is to add (or subtract) a specific number of process units (for example, the specific value is 1 process unit, and the unit is mm) on the basis of the maximum value (or minimum value) or the first specific value (or the second specific value) that is convenient for time series compression. Multiple first specific values (or second specific values) can be selected. For example, when representing data with 4 bits, the first specific value (or the second specific value) can select binary 1111, 0000, etc. as the specific over-limit value.

[0050]

[0051] Table 1

[0052] And variable relationship mapping and business logic decomposition are also performed on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables. For example, formulas (3) to (6) are the actual PID model equations used, and the variable mapping relationship is:

[0053] u k ->u

[0054] K p ->K p

[0055] e k ->e

[0056]

[0057] Δt -> △t

[0058] Among them, u k , K p , e k , K i , Δt on the left side of the variable mapping relationship are the process variables in the PID control model, and the corresponding variables u, K p , e, and Δt on the right side are the process variables defined by the industrial process time series data model. The channel is used to distinguish the variables in different variable groups. The business logic decomposition result is: when C stop = 0, C runWhen C = 1, it belongs to the load-bearing condition and the PID adjustment model works; when C stop = 0 and C run = 0, it belongs to the no-load condition and the PID adjustment model stops adjusting; when C stop = 1, it belongs to the parking condition. At the same time, in the three conditions, the logical relationship between the variables e and u follows formulas (3) to (5). In this embodiment, the logical decomposition result includes three conditions and the logical relationship between the process variables under the three conditions (for example, the logical relationship between the process variables e and u).

[0059] In step S13, according to the logical relationship between the multiple conditions and the process variables under the multiple conditions, corresponding compression parameters are set for the same process variable under the multiple conditions respectively.

[0060] Among them, the compression parameters include multiple compression algorithms.

[0061] Furthermore, the compression parameters further include a compression trigger condition and a compression accuracy, where the compression trigger condition includes multiple trigger channels and multiple trigger logics.

[0062] Exemplarily, the trigger logic includes a lossless compression trigger logic, a lossy compression trigger logic, a statistical feature coding trigger logic, etc. for each variable in the variable group under different conditions. The compression algorithm refers to a compression algorithm pool composed of a general compression algorithm and a data model-based compression algorithm. In this embodiment, the compression parameters are set as shown in Table 2, and two compression trigger channels are used as examples for explanation.

[0063] 1) The compression trigger channels are [1.0]C run and [1.1]C stop , which are used to distinguish different conditions to trigger different compression algorithms.

[0064] 2) The trigger logic is that when [1.1]C stop = 0 and the rising edge of [1.0]C run triggers the lossless compression of P ref , when [1.1]C stop = 0 and the falling edge of [1.0]C run triggers the lossy compression of P ref , and when [1.1]C stop falls, it triggers the statistical coding of P ref . The statistical coding is, for example, the statistical maximum value, minimum value, mean square error, etc. For other channels, refer to Table 2. Specifically, according to the importance, necessity of the service logic and the analysis requirements of the process variables under multiple conditions, the process variables are compressed respectively under the three conditions. For example, for the trigger channels [1.0]C run and [1.1]C stop, considering the process variable P in the trolley transverse movement model ref at C stop = 0, C run = 1 (i.e., the loaded condition) is very important and is also an important parameter in the process model. Therefore, trigger lossless compression for it, and specifically use the RLE lossless compression method. Similarly, for the process variable P ref at C stop = 0, C run = 0 (when in the no-load condition) is of general importance. Therefore, trigger lossy compression for the time-series data of this process variable under this condition, specifically use RLE lossy compression and retain 2 decimal places as needed. For the process variable P ref at C stop = 1 (i.e., the parking condition) is unimportant. Therefore, trigger statistical coding for the time-series data of the process variable under this condition. Similarly, for the compression methods of other process variables under different conditions, refer to Table 2, which will not be elaborated here.

[0065] 3) Common compression algorithms include RLE, Delta, XOR, simple8b and other commonly used compression algorithms in the time-series library. Compression algorithms based on the data model include channels [1:2]e that use formula (6) for prediction, then perform a difference operation between the actual value and the formula prediction value, and finally use XOR compression, and channels [1:6]u and [1:8]rpm all belong to this situation.

[0066]

[0067]

[0068] Table 2

[0069] Furthermore, the compression method further includes: according to the compression requirements corresponding to the multiple working conditions, respectively perform hybrid switching compression of the compression algorithm and compression accuracy on the time-series data of each process variable under the multiple working conditions.

[0070] Exemplarily, during the data compression process of the same channel, lossy compression, lossless compression, and statistical coding are dynamically and hybridly switched, and the compression accuracy is dynamically and hybridly switched. The compression requirements are the importance or degree of importance (such as very important, of general importance, and unimportant, etc.) and the necessity or degree of necessity (such as very necessary, of general necessity, and unnecessary, etc.) of the time-series data of each industrial process time-series data model. For the time-series data of each process variable under multiple working conditions, compression can be specifically referred to the following content.

[0071] According to Table 2, different compression methods are performed under different trigger logics. For example, for the process variable P ref at C stop = 0, C runWhen = 1, lossless compression starts. Since it is the set target position value, it should be a constant during PID regulation. Assuming it is 10,000, it is encoded by RLE as value = 10,000, quantity = n, where n is the number of sampling periods. At this time, if P ref has a jump or an outlier appears, it is necessary to record the jump or outlier and the quantity again, resulting in a longer compression encoding length. When searching for faults later, the fault point can be quickly found; P ref In C stop = 0, C run = 0 starts lossy compression. At this time, the PID model does not adjust, and P ref does not work. As long as it is ensured that there is no jump or outlier, lossy compression can be considered. In C stop = 1, the system has stopped working, and statistical encoding starts. When performing statistical encoding, if there is an outlier, it will change the statistical characteristics, which is convenient for fault detection.

[0072] This paper proposes a joint-channel dynamic hybrid (lossy and lossless dynamic switching, different-precision dynamic switching) compression algorithm based on an industrial data model dictionary. This method first gives a data model and defines the compression characteristics by setting model parameters and compression parameters to achieve efficient data compression.

[0073] To achieve the above object, an embodiment of the present invention provides a compression method for industrial process time-series data. The compression method includes: establishing an industrial process time-series data model dictionary corresponding to the industrial model based on the time-series data characteristics generated by the industrial model and the process variables corresponding to the industrial model; grouping the process variables of each industrial process time-series data model in the industrial process time-series data model dictionary to obtain a variable group corresponding to each industrial process time-series control model; setting data model parameters for each process variable in the variable group, and performing variable relationship mapping and business logic decomposition on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables, where the logical decomposition result includes multiple working conditions and the logical relationship between the process variables under multiple working conditions; and, according to the multiple working conditions and the logical relationship between the process variables under multiple working conditions, setting corresponding compression parameters for the same process variable under multiple working conditions, where the compression parameters include multiple compression algorithms.

[0074] Such as Figure 2It is the structure diagram of the compression system for industrial process time series data provided by the second embodiment of the present invention. The compression system 20 includes: a construction module 201, configured to establish an industrial process time series data model dictionary corresponding to the industrial model based on the time series data characteristics generated by the industrial model and the process variables corresponding to the industrial model; a first acquisition module 202, configured to group the process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data; a second acquisition module 203, configured to set data model parameters for each process variable in the variable group, and perform variable relationship mapping and business logic decomposition on the process variables after setting the data model parameters to obtain the variable mapping relationship and business logic decomposition result of the process variables, wherein the logic decomposition result includes multiple working conditions and the logical relationships between the process variables under multiple working conditions; and a setting module 204, configured to set corresponding compression parameters for the same process variable under multiple working conditions according to the multiple working conditions and the logical relationships between the process variables under multiple working conditions, wherein the compression parameters include multiple compression algorithms.

[0075] The compression system for industrial process time series data provided by the second embodiment of the present invention implements the compression method of the industrial process time series data as described above, and the technical effects achieved are the same as those of the first embodiment, which will not be elaborated herein.

[0076] Further, the compression parameters further include a compression trigger condition and a compression accuracy, wherein the compression trigger condition includes multiple trigger channels and multiple trigger logics.

[0077] Further, the compression system further includes: a compression module, configured to perform hybrid switching compression of the compression algorithm and the compression accuracy on the time series data of each process variable under multiple working conditions according to the compression requirements corresponding to the multiple working conditions.

[0078] Further, the data model parameters include a maximum value, a minimum value, a default value for exceeding the upper limit, and a default value for exceeding the lower limit. Among them, the maximum value is the largest numerical value of each process variable within the normal operating value range, and the minimum value is the smallest numerical value of each process variable within the normal operating value range. Setting data model parameters for each process variable in the variable group includes: when the default value for exceeding the upper limit and the default value for exceeding the lower limit are relevant to fault analysis, the default value for exceeding the upper limit is the maximum value plus a specific number of process units or a first specific value facilitating time series compression, and the default value for exceeding the lower limit is the minimum value minus a specific number of process units or a second specific value facilitating time series compression; or, when the default value for exceeding the upper limit and the default value for exceeding the lower limit are not relevant to fault analysis, the maximum value is determined as the default value for exceeding the upper limit, and the minimum value is determined as the default value for exceeding the lower limit.

[0079] Further, when there are multiple types of the control model, the first acquisition module is configured to group the process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model, including: grouping according to multiple service units belonging to the same industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model.

[0080] The third embodiment of the present invention further provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the compression method of industrial process time series data as described above.

[0081] The electronic device provided by the third embodiment of the present invention implements the compression method of industrial process time series data as described above and achieves the same technical effects as the first embodiment, which will not be elaborated here.

[0082] The fourth embodiment of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the compression method of industrial process time series data as described above.

[0083] The computer-readable storage medium provided by the fourth embodiment of the present invention executes instructions that, when executed by a processor, are used to implement the compression method of industrial process time series data as described above and achieve the same technical effects as the first embodiment, which will not be elaborated here.

[0084] The fifth embodiment of the present invention further provides a computer program product, including a computer program which, when executed by a processor, implements the compression method of industrial process time series data as described above.

[0085] The computer program product provided by the fifth embodiment of the present invention implements the compression method of industrial process time series data as described above, and the achieved technical effects are the same as those of the first embodiment, which will not be elaborated here.

[0086] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0090] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0092] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0094] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for compressing industrial process time series data, characterized in that: The compression method comprises: establishing an industrial process time series data model dictionary corresponding to the industrial model based on the industrial model and time series data features generated by process variables corresponding to the industrial model; Grouping the process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model; Setting data model parameters for each process variable in the variable group, and performing variable relationship mapping and business logic decomposition on the process variables after the data model parameters are set, to obtain variable mapping relationships and business logic decomposition results of the process variables, wherein the business logic decomposition results include multiple operating conditions and logical relationships between process variables under the multiple operating conditions; and, According to the various working conditions and the logical relationship between the process variables under the various working conditions, corresponding compression parameters are set for the same process variable under the various working conditions, wherein the compression parameters include a plurality of compression algorithms.

2. The compression method according to claim 1, characterized in that: The compression parameters also include compression trigger conditions and compression accuracy, wherein the compression trigger conditions include multiple trigger channels and multiple trigger logics.

3. The compression method according to claim 2, characterized in that: The compression method further includes: performing mixed switching compression of compression algorithm and compression accuracy on the time series data of each process variable under the various working conditions according to the compression requirements corresponding to the various working conditions.

4. The compression method according to claim 1, characterized in that: The data model parameters include a maximum value, a minimum value, an upper limit default value, and a lower limit default value, wherein the maximum value is the maximum value of each process variable in the normal working value range, and the minimum value is the minimum value of each process variable in the normal working value range. Wherein, setting the data model parameters for each process variable in the variable group includes: when the over-upper limit default value and the over-lower limit default value are related to fault analysis, the over-upper limit default value is the maximum value superimposed with a specific number of process units or a first specific value for time series compression, and the over-lower limit default value is the minimum value minus a specific number of process units or a second specific value for time series compression; or, When the over-upper limit default value and the over-lower limit default value are not relevant to fault analysis, the maximum value is determined as the over-upper limit default value, and the minimum value is determined as the over-lower limit default value.

5. The compression method according to claim 1, characterized in that: Grouping the process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model includes: Multiple business units belonging to the same industrial process time series data model in the industrial process time series data model dictionary are grouped to obtain a variable group corresponding to each industrial process time series data model.

6. A compression system for industrial process control time series data, characterized in that: The compression system includes: a construction module for establishing an industrial process time series data model dictionary corresponding to the industrial model based on the industrial model and time series data features generated by process variables corresponding to the industrial model; A first acquisition module, configured to group process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model; a second acquisition module, configured to set data model parameters for each process variable in the variable group, and perform variable relationship mapping and business logic decomposition on the process variables after the data model parameters are set, to obtain variable mapping relationships and business logic decomposition results of the process variables, wherein the business logic decomposition results include multiple operating conditions and logical relationships between process variables under the multiple operating conditions; and The setting module is used to set corresponding compression parameters for the same process variable under various working conditions according to the various working conditions and the logical relationship between the process variables under the various working conditions, wherein the compression parameters include multiple compression algorithms.

7. The compression system according to claim 6, characterized in that The compression parameters also include compression trigger conditions and compression accuracy, wherein the compression trigger conditions include multiple trigger channels and multiple trigger logics.

8. The compression system according to claim 7, characterized in that The compression system further includes: a compression module, which is used to perform mixed switching compression of compression algorithm and compression accuracy on the time series data of each process variable under various working conditions according to the compression requirements corresponding to the various working conditions.

9. The compression system according to claim 6, characterized in that The data model parameters include a maximum value, a minimum value, an upper limit default value, and a lower limit default value, wherein the maximum value is the maximum value of each process variable in the normal working value range, and the minimum value is the minimum value of each process variable in the normal working value range. Wherein, setting the data model parameters for each process variable in the variable group includes: when the over-upper limit default value and the over-lower limit default value are related to fault analysis, the over-upper limit default value is the maximum value superimposed with a specific number of process units or a first specific value for time series compression, and the over-lower limit default value is the minimum value minus a specific number of process units or a second specific value for time series compression; or, When the over-upper limit default value and the over-lower limit default value are not relevant to fault analysis, the maximum value is determined as the over-upper limit default value, and the minimum value is determined as the over-lower limit default value.

10. The compression system according to claim 6, characterized in that The first acquisition module is used to group the process variables of each industrial process time series data model in the industrial process time series data model dictionary to obtain a variable group corresponding to each industrial process time series data model, including: Multiple business units belonging to the same industrial process time series data model in the industrial process time series data model dictionary are grouped to obtain a variable group corresponding to each industrial process time series data model.

11. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for compressing industrial process time series data according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for compressing industrial process time series data according to any one of claims 1 to 5.

13. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the method for compressing industrial process time series data as described in any one of claims 1 to 5.

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