Process monitoring system supporting multi-level index decomposition

Abstract

The invention discloses a process monitoring system supporting multi-level index decomposition, and relates to the technical field of enterprise management, and the process monitoring system comprises the following specific steps: collecting enterprise data information in real time; decomposing the enterprise index data according to the enterprise architecture and the enterprise personnel data to form multiple levels; adding identifiers to the decomposed enterprise personnel data and enterprise index data; enterprise data information collected in real time is processed and analyzed, and all the modules are cooperated at the same time; and after the enterprise data information is processed and analyzed, corresponding early warning information is sent out according to the evaluation result of the enterprise personnel and the enterprise indexes of different levels and the corresponding index completion progress evaluation model. Through the arrangement of the system, microscopic monitoring and macroscopic monitoring can be carried out, dynamic monitoring can be carried out according to the change of data, and meanwhile, different levels in an enterprise architecture can be monitored in a penetrating manner, so that early warning is conveniently sent out, and management and control are facilitated.

Description

Technical Field

[0001] This invention relates to the field of enterprise management technology, and in particular to a process monitoring system that supports multi-level indicator decomposition. Background Technology

[0002] To better develop enterprises, management is necessary. Enterprise management is an objective requirement and an inevitable product of the development of large-scale socialized production, arising from the collective labor of people engaged in exchange. When social production develops to a certain stage, all large-scale collective labor requires, to varying degrees, direction to coordinate individual activities. Through supervision and regulation of the entire labor process, individual labor is made to conform to the requirements of overall production, ensuring the normal progress of the entire labor process according to its predetermined purpose. Especially in modern society, with its highly developed science and technology, rapidly changing products, and volatile markets, enterprise management becomes increasingly important.

[0003] Currently, in the process of enterprise management, corresponding enterprise indicators are set according to the enterprise structure and the work situation of the enterprise personnel, and then the enterprise indicators are allocated to specific departments and individuals.

[0004] However, in the process of achieving enterprise targets, since the targets are different at different levels, the completion rate of the target data at different levels varies, and a lot of data is generated. This places a heavy burden on enterprise managers to analyze this data and issue corresponding warnings, and also makes it difficult to monitor the progress of specific processes. Therefore, we propose a process monitoring system that supports multi-level target decomposition. Summary of the Invention

[0005] The purpose of this invention is to provide a process monitoring system that supports multi-level indicator decomposition. By setting up this system, micro-monitoring and macro-monitoring can be carried out, and dynamic monitoring can be performed based on data changes. At the same time, it can penetrate and monitor different levels in the enterprise architecture, thereby facilitating the issuance of early warnings for management and control.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a process monitoring method that supports multi-level indicator decomposition, comprising the following specific steps:

[0008] Step 1: Collect enterprise data in real time. Enterprise data includes enterprise personnel data, enterprise indicator data, and enterprise indicator completion progress data.

[0009] Step 2: Decompose the enterprise indicator data into multiple levels based on the enterprise structure and personnel data;

[0010] Step 3: Add labels to the decomposed enterprise personnel data and enterprise indicator data. The labels include enterprise personnel data labels and enterprise indicator data labels.

[0011] Step 4: Process and analyze the real-time collected enterprise data, while coordinating with various modules;

[0012] Step 5: After processing and analyzing the enterprise data, issue corresponding early warning information based on the assessment results of the progress evaluation model of different levels of enterprise personnel and enterprise indicators.

[0013] Step 6: Store the enterprise data information collected by the information collection module and the evaluation results of the indicator completion progress assessment model to the cloud, and establish a historical database.

[0014] Preferably, the specific content of the indicator completion progress assessment model is as follows:

[0015] The enterprise's performance indicators are quantified, with the target task size set as S and the completion time set as T. The target task size S and completion time T are substituted into the daily average target task size calculation formula to obtain the daily average target task size, which is set as D. Then, the actual daily average target task size is collected in real time and set as d. Next, the daily average target task size D and the actual daily average target task size d are substituted into the indicator completion change calculation model and the indicator completion deviation calculation model to obtain the indicator completion change factor, set as X, and the indicator completion deviation factor, set as Y. Based on the indicator completion progress... The system is configured with three states: silent, alert, and warning. For the calculation model of indicator change completion, the sub-threshold between the silent and alert states is set as X2, and the threshold between the alert and warning states is set as X1, where X1 < X2. Similarly, the sub-threshold between the silent and alert states is set as Y2, and the threshold between the alert and warning states is set as Y1, where Y1 < Y2. When X > X2 and Y > Y2, the system is in the silent state. When X1 ≥ X ≥ X2 or Y1 ≥ Y ≥ Y2, the system is in the alert state. When X2 > X or Y2 > Y, the system is in the warning state.

[0016] Preferably, the specific content of the calculation model for the change of the indicator is as follows:

[0017] Each time, the average daily completion amount d of the target task in the three most recent periods is captured, and d and D are substituted into the target completion change calculation formula. The target completion change calculation formula is as follows:

[0018] X=(d n -D) / D+(d n-1 -D) / D+(d n-2 -D) / D;

[0019] In the indicator change calculation model, after a prompt or warning state is reached once, it is necessary to calculate the result of X again after N intervals before determining whether it is in a silent state, prompt state, or warning state based on the calculation result of X again, where N is a natural number greater than 3.

[0020] Preferably, the specific content of the indicator completion deviation calculation model is as follows:

[0021] Capture the actual daily average completed target task amount d, and substitute d into the target completion deviation calculation formula. Target completion deviation calculation: Y=α*d, where the value of α ranges from 1.2 to 1.5.

[0022] Preferably, the formula for calculating the average daily target task is: D = S / T, where S is the enterprise target task and T is the time required to complete the enterprise target task.

[0023] Secondly, the present invention provides a process monitoring system that supports multi-level indicator decomposition, which implements the process monitoring method for supporting multi-level indicator decomposition as described above. The system includes the following:

[0024] The information collection module is used to collect enterprise data information. The information collection module includes an enterprise personnel data collection unit, an enterprise indicator data collection unit, and an enterprise indicator completion progress data collection unit.

[0025] The decomposition module is used to decompose enterprise indicator data based on enterprise structure and personnel data.

[0026] The identification module is used to attach identifications to the decomposed enterprise personnel data and enterprise indicator data, including enterprise personnel data identification units and enterprise indicator data identification units.

[0027] The analysis module is used to process and analyze the enterprise data collected by the information collection module, while also coordinating with other modules.

[0028] The early warning module is used to issue corresponding early warning information.

[0029] The storage module is used to store the enterprise data information collected by the information collection module and the evaluation results of the indicator completion progress assessment model to the cloud, and to establish a historical database.

[0030] Preferably, the enterprise personnel data collection unit is used to collect enterprise personnel data in real time, including basic information, functional information and ability information of enterprise personnel;

[0031] The enterprise indicator data acquisition unit is used to collect enterprise indicator data in real time.

[0032] The enterprise indicator completion progress data collection unit is used to collect enterprise personnel's progress data on enterprise indicator completion in real time.

[0033] Preferably, the enterprise structure is divided into management level, functional level, team level and individual level. The decomposition module divides the enterprise indicators into first-level indicators, second-level indicators, third-level indicators and fourth-level indicators for the management level, intelligence level, team level and individual level respectively.

[0034] Preferably, the identification module sequentially adds a management-level master identifier, a functional-level master identifier, a team-level master identifier, and an individual-level master identifier to the enterprise architecture. Then, for the management-level master identifier, the intelligence-level master identifier, the team-level master identifier, and the individual-level master identifier, it adds a first-level indicator sub-identifier, a second-level indicator sub-identifier, a third-level indicator sub-identifier, and a fourth-level indicator sub-identifier to the enterprise indicators.

[0035] Preferably, when the warning module is in a silent state, it does not provide any prompts or warnings; when the warning module is in a prompt state, it provides a pop-up reminder; and when the warning module is in a warning state, it provides a pop-up reminder, an SMS reminder, and a telephone reminder.

[0036] The technical effects and advantages of this invention are as follows:

[0037] (1) By decomposing enterprise indicators according to enterprise structure and personnel, enterprise indicators are specified to individuals. At the same time, enterprise data information can be collected in real time and dynamically monitored according to enterprise data information. The overall process can also be continuously monitored. According to different levels of enterprise personnel and enterprise indicators, corresponding indicator completion progress evaluation models are set for hierarchical monitoring, thereby forming a top-down penetration monitoring based on enterprise structure. It is also convenient for data traceability in the later stage and can be specified to different levels.

[0038] (2) By setting up a calculation model for changes in indicator completion and a calculation model for deviations in indicator completion, it is possible to set up a calculation model for changes in indicator completion and a calculation model for deviations in indicator completion based on the actual daily average amount of indicator completion tasks. This enables single-day monitoring and multi-day monitoring of the actual daily average amount of indicator completion tasks, thereby forming micro-monitoring and macro-monitoring. This allows for more accurate monitoring of the completion status of the actual daily average amount of indicator completion tasks. Furthermore, iterative dynamic monitoring can be performed based on the dynamic changes in the actual daily average amount of indicator completion tasks, which facilitates timely early warning and avoids the trouble of managers analyzing large amounts of data, thus facilitating timely control. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the process monitoring system that supports multi-level indicator decomposition according to the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] This invention provides a process monitoring method that supports multi-level indicator decomposition, comprising the following specific steps:

[0042] Step 1: Collect enterprise data in real time. Enterprise data includes enterprise personnel data, enterprise indicator data, and enterprise indicator completion progress data.

[0043] Step 2: Decompose the enterprise indicator data into multiple levels based on the enterprise structure and personnel data;

[0044] Step 3: Add labels to the decomposed enterprise personnel data and enterprise indicator data. The labels include enterprise personnel data labels and enterprise indicator data labels.

[0045] Step 4: Process and analyze the real-time collected enterprise data, while coordinating with various modules;

[0046] Step 5: After processing and analyzing the enterprise data, issue corresponding early warning information based on the assessment results of the progress evaluation model of different levels of enterprise personnel and enterprise indicators.

[0047] Step Six: Store the enterprise data information collected by the information collection module and the evaluation results of the indicator completion progress assessment model to the cloud, and establish a historical database. Based on the enterprise's internal structure and indicators, collect enterprise personnel data, enterprise indicator data, and enterprise indicator completion progress data in real time. Then, decompose the enterprise indicator data into multiple levels according to the enterprise architecture and personnel data. Add labels to the decomposed enterprise personnel data and enterprise indicator data. Subsequently, process and analyze the enterprise data information, and issue corresponding early warning information based on the evaluation results of different levels of enterprise personnel and indicators and the corresponding indicator completion progress assessment models. This design can collect enterprise data information in real time and perform dynamic monitoring based on the enterprise data information. It also sets corresponding indicator completion progress assessment models for hierarchical monitoring based on different levels of enterprise personnel and indicators, thus forming a top-down pervasive monitoring system based on the enterprise architecture. This facilitates data traceability later and allows for specific monitoring down to different levels, enabling detailed monitoring of the process.

[0048] Furthermore, the specific content of the indicator completion progress assessment model is as follows:

[0049] Enterprise indicators are quantified, with the target task volume set as S and the completion time set as T. The target task volume S and completion time T are substituted into the daily average target task volume calculation formula to obtain the daily average target task volume, which is set as D. Then, the actual daily average target task volume is collected in real time and set as d. The daily average target task volume D and the actual daily average target task volume d are then substituted into the indicator completion change calculation model and the indicator completion deviation calculation model to obtain the indicator completion change factor, set as X, and the indicator completion deviation factor, set as Y. Based on the indicator completion progress, there are silent state, prompt state, and warning state. Specifically, for the indicator completion change calculation model, the sub-threshold between the silent state and the prompt state is set as X2, and the threshold between the prompt state and the warning state is set as X1, and X... 1 < X2. For the indicator completion change calculation model, the sub-threshold between the silent state and the prompt state is set to Y2, and the threshold between the prompt state and the warning state is set to Y1, where Y1 < Y2. When X > X2 and Y > Y2, the system is in the silent state; when X1 ≥ X ≥ X2 or Y1 ≥ Y ≥ Y2, the system is in the prompt state; and when X2 > X or Y2 > Y, the system is in the warning state. This design can set up indicator completion change calculation models and indicator completion deviation calculation models based on the actual daily average completed indicator task volume, thereby enabling single-day and multi-day monitoring of the actual daily average completed indicator task volume. This forms micro-monitoring and macro-monitoring, allowing for more accurate monitoring of the actual daily average completed indicator task volume. Furthermore, iterative dynamic monitoring can be performed based on the dynamic changes in the actual daily average completed indicator task volume.

[0050] Furthermore, the specific details of the calculation model for the change in indicators are as follows:

[0051] Each time, the average daily completion amount d of the target task in the three most recent periods is captured, and d and D are substituted into the target completion change calculation formula. The target completion change calculation formula is as follows:

[0052] X=(d n -D) / D+(d n-1 -D) / D+(d n-2 -D) / D;

[0053] In the indicator change calculation model, after a prompt or warning state is reached once, it is necessary to wait for the calculation results of X N times before determining whether to enter a silent state, prompt state, or warning state again based on the calculation results of X, where N is a natural number greater than 3. This design can determine whether to issue a prompt or warning again based on the calculation results of X after the first prompt or warning based on the indicator change calculation model, after an interval of N times. This can avoid frequent triggering of prompts or warnings due to a single small d value, thus avoiding waste of computing resources, and also avoid issuing a prompt or warning again because a small d value has already issued a prompt or warning once.

[0054] Furthermore, the specific content of the indicator completion deviation calculation model is as follows:

[0055] The system captures the actual daily average completed target task volume d and substitutes d into the target completion deviation calculation formula: Y = α * d, where α ranges from 1.2 to 1.5. This target completion deviation calculation model allows for real-time monitoring of the actual daily average completed target task volume, enabling timely detection of data with significant deviations. This facilitates prompts or warnings, allowing for timely adjustments by the enterprise management team.

[0056] Furthermore, the formula for calculating the average daily target workload is: D = S / T, where S is the enterprise's target workload and T is the time required to complete the enterprise's target workload.

[0057] This invention provides, for example Figure 1 The illustrated process monitoring system supports multi-level indicator decomposition and implements the aforementioned process monitoring method supporting multi-level indicator decomposition. The system includes the following components:

[0058] The information collection module is used to collect enterprise data information. The information collection module includes an enterprise personnel data collection unit, an enterprise indicator data collection unit, and an enterprise indicator completion progress data collection unit.

[0059] The decomposition module is used to decompose enterprise indicator data based on enterprise structure and personnel data.

[0060] The identification module is used to attach identifications to the decomposed enterprise personnel data and enterprise indicator data, including enterprise personnel data identification units and enterprise indicator data identification units.

[0061] The analysis module is used to process and analyze the enterprise data collected by the information collection module, while also coordinating with other modules.

[0062] The early warning module is used to issue corresponding early warning information.

[0063] The storage module is used to store the enterprise data information collected by the information collection module and the evaluation results of the indicator completion progress assessment model to the cloud, and to establish a historical database.

[0064] Furthermore, the enterprise personnel data collection unit is used to collect enterprise personnel data in real time, including basic information, functional information and ability information of enterprise personnel;

[0065] The enterprise indicator data collection unit is used to collect enterprise indicator data in real time;

[0066] The enterprise target completion progress data collection unit is used to collect enterprise personnel's progress data on enterprise target completion in real time.

[0067] Furthermore, the enterprise structure is divided into management level, functional level, team level and individual level. The decomposition module divides enterprise indicators into first-level indicators, second-level indicators, third-level indicators and fourth-level indicators for management level, intelligence level, team level and individual level respectively.

[0068] Furthermore, the identification module sequentially adds management-level master identifiers, functional-level master identifiers, team-level master identifiers, and individual-level master identifiers to the enterprise architecture. Then, for the management-level master identifier, intelligence-level master identifier, team-level master identifier, and individual-level master identifier, it adds first-level indicator sub-identifiers, second-level indicator sub-identifiers, third-level indicator sub-identifiers, and fourth-level indicator sub-identifiers to the enterprise indicators.

[0069] By breaking down enterprise personnel into management, functional, team, and individual levels according to the enterprise structure, and then assigning corresponding identifiers to each level, we create master identifiers for management, functional, team, and individual levels. Enterprise indicators are then decomposed into primary indicators corresponding to management levels, secondary indicators corresponding to functional levels, tertiary indicators corresponding to team levels, and quaternary indicators corresponding to individual levels. Furthermore, each level is assigned a sub-identifier (sub-identifier for each level). This facilitates mutual identification between primary indicator sub-identifiers and their corresponding management level master identifiers, between secondary indicator sub-identifiers and their corresponding functional level master identifiers, and between tertiary indicator sub-identifiers and their corresponding individual level master identifiers. The system identifies the enterprise indicators by cross-referencing them with the corresponding team-level parent identifier, and the four-level indicator sub-identifiers identify them with the corresponding individual-level parent identifier. This enables data integration between enterprise indicators and corresponding personnel, thus concretizing the enterprise indicators and making the overall process clearer. It also facilitates accurate identification of the corresponding personnel during later data traceability. Furthermore, the indicator completion change calculation model and indicator completion deviation calculation model can be specifically set for the enterprise structure and enterprise indicators. For example, different X1, X2, Y1, and Y2 can be set for management level, functional level, team level, and individual level, respectively, to conduct separate monitoring, forming a pervasive monitoring system. This allows for accurate understanding of the progress of specific enterprise indicators and processes, facilitating timely control.

[0070] Furthermore, when the warning module is in silent mode, it will not provide any prompts or warnings; when the warning module is in prompt mode, it will provide pop-up reminders; and when the warning module is in alert mode, it will provide pop-up reminders, SMS reminders, and telephone reminders.

[0071] Example 1: Set the enterprise's target task S to 100 and the completion time T to 10. Substitute the target task S of 100 and the completion time T of 10 into the daily average target task calculation formula, D = 100 / 10, thus D is 10. Set the actual daily average target task completion d for the most recent three consecutive days. n For 8, d n-1 For 6 and d n-2 Set it to 11, then d n For 8, d n-1 For 6 and d n-2 Substituting 11 and D into the indicator to complete the change calculation formula, X = (8-10) / 10 + (6-10) / 10 + (11-10) / 10, we get X = -0.5. At the same time, we set d... n Substituting 8 into the formula for calculating the deviation of the indicator, Y = 1.3 * 8, we get Y = 10.4. Then, we set X2 to 0, X1 to -0.3, Y2 to 10, and Y1 to 8. Then, in d... nSince X < X1, a warning is issued.

[0072] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A process monitoring method supporting multi-level indicator decomposition, characterized in that, The specific steps include the following: Step 1: Collect enterprise data in real time. Enterprise data includes enterprise personnel data, enterprise indicator data, and enterprise indicator completion progress data. Step 2: Decompose the enterprise indicator data into multiple levels based on the enterprise structure and personnel data; Step 3: Add labels to the decomposed enterprise personnel data and enterprise indicator data. The labels include enterprise personnel data labels and enterprise indicator data labels. Step 4: Process and analyze the real-time collected enterprise data, while coordinating with various modules; Step 5: After processing and analyzing the enterprise data, issue corresponding early warning information based on the assessment results of the progress evaluation model of different levels of enterprise personnel and enterprise indicators. Step 6: Store the enterprise data information collected by the information collection module and the evaluation results of the indicator completion progress assessment model to the cloud, and establish a historical database.

2. The process monitoring method supporting multi-level indicator decomposition according to claim 1, characterized in that, The specific content of the indicator completion progress assessment model is as follows: The enterprise's performance indicators are quantified, with the target task size set as S and the completion time set as T. The target task size S and completion time T are substituted into the daily average target task size calculation formula to obtain the daily average target task size, which is set as D. Then, the actual daily average target task size is collected in real time and set as d. Next, the daily average target task size D and the actual daily average target task size d are substituted into the indicator completion change calculation model and the indicator completion deviation calculation model to obtain the indicator completion change factor, set as X, and the indicator completion deviation factor, set as Y. Based on the indicator completion progress... The system is configured with three states: silent, alert, and warning. For the calculation model of indicator change completion, the sub-threshold between the silent and alert states is set as X2, and the threshold between the alert and warning states is set as X1, where X1 < X2. Similarly, the sub-threshold between the silent and alert states is set as Y2, and the threshold between the alert and warning states is set as Y1, where Y1 < Y2. When X > X2 and Y > Y2, the system is in the silent state. When X1 ≥ X ≥ X2 or Y1 ≥ Y ≥ Y2, the system is in the alert state. When X2 > X or Y2 > Y, the system is in the warning state.

3. The process monitoring method supporting multi-level indicator decomposition according to claim 2, characterized in that, The specific details of the calculation model for the change of the indicator are as follows: Each time, the average daily completion amount d of the target task in the three most recent periods is captured, and d and D are substituted into the target completion change calculation formula. The target completion change calculation formula is as follows: X=(d n -D) / D+(d n-1 -D) / D+(d n-2 -D) / D; In the indicator change calculation model, after a prompt or warning state is reached once, it is necessary to calculate the result of X again after N intervals before determining whether it is in a silent state, prompt state, or warning state based on the calculation result of X again, where N is a natural number greater than 3.

4. The process monitoring method supporting multi-level indicator decomposition according to claim 1, characterized in that, The specific content of the indicator completion deviation calculation model is as follows: Capture the actual daily average completed target task amount d, and substitute d into the target completion deviation calculation formula. Target completion deviation calculation: Y=α*d, where the value of α ranges from 1.2 to 1.

5.

5. A process monitoring method supporting multi-level indicator decomposition according to claim 1, characterized in that, The formula for calculating the average daily target task is: D = S / T, where S is the enterprise's target task and T is the time required to complete the enterprise's target task.

6. A process monitoring system supporting multi-level indicator decomposition, comprising the process monitoring method supporting multi-level indicator decomposition as described in any one of claims 1 to 5, characterized in that, The system includes the following: The information collection module is used to collect enterprise data information. The information collection module includes an enterprise personnel data collection unit, an enterprise indicator data collection unit, and an enterprise indicator completion progress data collection unit. The decomposition module is used to decompose enterprise indicator data based on enterprise structure and personnel data. The identification module is used to attach identifications to the decomposed enterprise personnel data and enterprise indicator data, including enterprise personnel data identification units and enterprise indicator data identification units. The analysis module is used to process and analyze the enterprise data collected by the information collection module, while also coordinating with other modules. The early warning module is used to issue corresponding early warning information. The storage module is used to store the enterprise data information collected by the information collection module and the evaluation results of the indicator completion progress assessment model to the cloud, and to establish a historical database.

7. A process monitoring system supporting multi-level indicator decomposition according to claim 6, characterized in that, The enterprise personnel data collection unit is used to collect enterprise personnel data in real time, including basic information, intelligence information and capability information of enterprise personnel; The enterprise indicator data acquisition unit is used to collect enterprise indicator data in real time. The enterprise indicator completion progress data collection unit is used to collect enterprise personnel's progress data on enterprise indicator completion in real time.

8. A process monitoring system supporting multi-level indicator decomposition according to claim 6, characterized in that, The enterprise structure is divided into management level, functional level, team level and individual level. The decomposition module divides the enterprise indicators into first-level indicators, second-level indicators, third-level indicators and fourth-level indicators for management level, intelligence level, team level and individual level respectively.

9. A process monitoring system supporting multi-level indicator decomposition according to claim 8, characterized in that, The identification module sequentially adds a management-level master identifier, a functional-level master identifier, a team-level master identifier, and an individual-level master identifier to the enterprise architecture. Then, for the management-level master identifier, the intelligence-level master identifier, the team-level master identifier, and the individual-level master identifier, it adds a first-level indicator sub-identifier, a second-level indicator sub-identifier, a third-level indicator sub-identifier, and a fourth-level indicator sub-identifier to the enterprise indicators.

10. A process monitoring system supporting multi-level indicator decomposition according to claim 6, Its features are, When the warning module is in silent mode, it will not provide any prompts or warnings. When the warning module is in the prompt state, a pop-up reminder will be displayed; when the warning module is in the alert state, ... Then pop-up reminders, SMS reminders, and phone reminders will be sent.

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