TWI865201B - Early warning analysis system, method, and computer-readable medium based on device health status - Google Patents

Abstract

The invention discloses an early warning analysis system, method, and computer-readable medium based on device health status. A device status detection module detects device disconnection status, device recording status and device recording storage remaining space of a video surveillance device managed by a video surveillance platform, and a device health status prediction analysis module predictives analyze an early warning range interval of the device health status of the video surveillance device in a good status, attention status or emergency status. Furthermore, when the device health status of the video surveillance device has reached the early warning range interval of the attention status or the emergency status, an early warning module sends an early warning notification message to a user terminal or a device maintenance terminal, so that the user terminal or the device maintenance terminal can pre-monitor or pre-process an abnormal problem of the video surveillance device according to the early warning notification message of the early warning module.

Description

Early warning analysis system, method and computer-readable medium based on equipment health status

The present invention relates to an early warning analysis technology for image monitoring equipment, and in particular to an early warning analysis system, method and computer-readable medium based on the health status of the equipment.

Currently, the image monitoring platform uses the related status of multiple image monitoring devices as the basis for the system to automatically report faults. Although this method can instantly know which image monitoring device has failed through the image monitoring platform, there will still be a waiting window period for reporting the failed image monitoring device to the device maintenance end and then waiting for the device maintenance end to handle the failed image monitoring device, which is relatively lengthy.

At the same time, during the waiting window period for the equipment maintenance end to process the faulty video surveillance equipment, the faulty video surveillance equipment will be unable to record or miss the recorded images during this waiting window period, thereby reducing the equipment availability rate of the video surveillance equipment managed by the video surveillance platform and causing incomplete images.

Furthermore, existing technologies can only detect the disconnection status, recording status and remaining storage space of the video surveillance equipment managed by the video surveillance platform, and judge whether the video surveillance equipment is abnormal, but cannot quickly predict and analyze whether the health status of the video surveillance equipment is in a good state, a warning state or an emergency state, which is not immediate.

Therefore, how to provide an innovative early warning analysis technology for image monitoring equipment to solve any of the above problems and provide related systems or methods has become a major research topic for technical personnel in this field.

The device health status based early warning analysis system of the present invention comprises: a device status detection module, which detects the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device managed by an image monitoring platform; a device health status prediction analysis module, which is connected to the device status detection module by communication, so that the device health status prediction analysis module can detect the device health status detection module based on the device status detection module. The device health status of the image monitoring device is calculated based on the values of the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device. The device health status prediction and analysis module then uses the calculated value of the device health status of the image monitoring device to predict and analyze whether the device health status of the image monitoring device is in the warning range of good status, attention status or emergency status. and an early warning module, which is connected to the equipment health status prediction and analysis module in communication, so that when the equipment health status prediction and analysis module predicts and analyzes the equipment health status of the image monitoring equipment using the calculated value of the equipment health status of the image monitoring equipment and finds that the equipment health status of the image monitoring equipment has reached the early warning range of the attention state or the emergency state, the early warning module sends a warning message that the equipment health status of the image monitoring equipment has reached the attention state or the emergency state. Status warning A warning notification message of a range is sent to at least one of the user end of the video surveillance platform and the associated equipment maintenance end, so that at least one of the user end and the equipment maintenance end can monitor or handle abnormal problems of the video surveillance equipment in advance according to the warning notification message of a warning range sent by the warning module that the equipment health status of the video surveillance equipment has reached the warning state or emergency state.

The device health status based early warning analysis method of the present invention comprises: a device status detection module detects the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device managed by an image monitoring platform; a device health status prediction analysis module detects the device disconnection status of the image monitoring device detected by the device status detection module; The health status of the image monitoring device is calculated by using the values of the device recording status and the remaining storage space of the device recording. The device health status prediction and analysis module then uses the calculated health status of the image monitoring device to predict and analyze whether the health status of the image monitoring device is in a good state, a warning state or an emergency state. ; and when the equipment health status prediction and analysis module predicts and analyzes that the equipment health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state using the calculated equipment health status value of the image monitoring equipment, the warning module sends a warning notification message that the equipment health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state to at least one of the user end and the associated equipment maintenance end of the image monitoring platform, so that at least one of the user end and the equipment maintenance end can pre-monitor or pre-process abnormal problems of the image monitoring equipment according to the warning notification message sent by the warning module that the equipment health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state.

The computer-readable medium of the present invention is applied to a computing device or a computer, and stores instructions to execute the above-mentioned early warning analysis method based on the health status of the equipment.

Therefore, the present invention provides an innovative early warning analysis system, method and computer-readable medium based on the health status of the equipment, which can effectively detect the disconnection status, recording status and remaining storage space of the equipment of the image monitoring equipment managed by the image monitoring platform through the equipment status detection module, and can also automatically calculate the value of the equipment health status of the image monitoring equipment through the equipment health status prediction and analysis module, and can also quickly predict and analyze whether the equipment health status of the image monitoring equipment is in a good state, a warning state or an emergency state according to the value of the equipment health status.

At the same time, the present invention can automatically send a warning notification message to the user end or equipment maintenance end when the health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state by the warning module, so that the user end or the equipment maintenance end can pre-monitor or pre-process the abnormal problems of the image monitoring equipment in real time according to the warning notification message, thereby improving the equipment availability rate of the image monitoring equipment managed by the image monitoring platform.

In order to make the above features and advantages of the present invention more clearly understandable, the following examples are given and detailed descriptions are provided in conjunction with the attached drawings. The following description will partially explain the additional features and advantages of the present invention, and these features and advantages will be partially known from the description or can be learned through the practice of the present invention. It should be understood that both the general description above and the detailed description below are exemplary and explanatory, and are not intended to limit the scope of the present invention.

1: Early warning analysis system

10: Image management system detection module

11: Central monitoring unit

12: Operation status

20: Equipment status detection module

21: Device disconnected status

22: Device recording status

23: Device video storage remaining space

30: Equipment abnormal status exclusion module

31: Exclusion list

40: Equipment health status prediction and analysis module

41: Method for calculating health status values

42: Good condition

43: Attention status

44: Emergency

50: Early warning module

51: Communication unit

52: Warning notification message

A: Image monitoring platform

A1: Image management system

B: Database

B1: Status data

D: Open data platform of meteorological units

D1: Data Acquisition API

D2: Weather conditions

E1: User side

E2: Equipment maintenance end

S11 to S12, S21 to S24, S31 to S32: Steps

X: Image monitoring equipment

Figure 1 is a schematic diagram of the architecture of the early warning analysis system based on the health status of equipment described in the present invention.

Figure 2 is a schematic diagram of an embodiment of the early warning analysis system and method based on the health status of equipment described in the present invention.

Figures 3 and 4 are schematic diagrams of implementation examples of the operation modes of the equipment abnormal state exclusion module and the equipment health state prediction analysis module in the early warning analysis system and method based on the equipment health state described in the present invention, respectively.

The following describes the implementation of the present invention through a specific concrete implementation form. People familiar with this technology can understand other advantages and effects of the present invention from the content disclosed in this manual, and can also implement or use it through other different specific equivalent implementation forms.

FIG1 is a schematic diagram of the structure of the early warning analysis system 1 based on the equipment health status of the present invention. As shown in the figure, the early warning analysis system 1 based on the equipment health status includes at least an image management system detection module 10, an equipment status detection module 20, an equipment abnormal status exclusion module 30, an equipment health status prediction analysis module 40 and an early warning module 50, etc., which are interconnected and may further include a database B (see FIG2). In addition, the image management system detection module 10 may have a central monitoring unit 11, the equipment health status prediction analysis module 40 may have a health status value calculation method 41 as an equipment health status judgment model, and the early warning module 50 may have a communication unit 51.

In one embodiment, the image management system detection module 10, the equipment status detection module 20 and the equipment abnormal status exclusion module 30 can all be connected to the image monitoring platform A in communication, the equipment abnormal status exclusion module 30 can be connected to the image management system detection module 10 and the equipment status detection module 20 respectively, the equipment health status prediction and analysis module 40 can be connected to the image management system detection module 10, the equipment status detection module 20, the equipment abnormal status exclusion module 30 and the early warning module 50 respectively, and the early warning module 50 can be connected to the user end E1 and the equipment maintenance end E2 respectively.

In one embodiment, the image management system detection module 10 may be an image management system detector (chip/circuit), an image management system detection software (program), etc., and the central monitoring unit 11 may be a central monitoring program, a central monitoring software, a central monitoring application (APP), etc. The device state detection module 20 may be a device state detector (chip/circuit), a device state detection software (program), etc., and the device abnormal state exclusion module 30 may be a device abnormal state exclusion software (program), etc. The equipment health status prediction and analysis module 40 can be an equipment health status prediction and analysis device (chip/circuit), equipment health status prediction and analysis software (program), etc., or a computer host or server used to predict and analyze the health status of the equipment. The early warning module 50 can be an early warning device (chip/circuit), early warning software (program), an alarm module, an alarm device (chip/circuit), an alarm software (program), etc., and the database B can be a data storage device, a data memory, a data memory card, a data hard disk (such as a cloud hard disk), a data server (such as a cloud server), etc.

In one embodiment, the "at least one" mentioned in the present invention represents more than one (such as one, two or three), "plurality" represents more than two (such as two, three, four, ten or one hundred), and "communication connection" represents communication or connection through various methods such as data, signal, electrical, wired mode (such as wired network) or wireless mode (such as wireless network). However, the present invention is not limited to those mentioned in each embodiment.

In one embodiment, the early warning analysis system 1 based on the health status of equipment is applied to an image monitoring platform A, and the image monitoring platform A may include at least one (e.g., multiple) image management systems A1. The image monitoring platform A and the image management system A1 can both communicate and manage multiple image monitoring equipment X, and the image monitoring platform A can synchronously display the image screens (e.g., recording screens) of the multiple image monitoring equipment X through multiple display interfaces (e.g., display screens or TV walls). Multiple video surveillance devices X can be installed at various locations where video surveillance is required to obtain different video images (such as video images). For example, multiple video surveillance devices X can be installed at various roads (such as expressways/provincial roads/county roads/general roads/industrial roads), traffic intersections (such as crossroads/accident-prone intersections), vehicle traffic areas (such as stations/MRT stations/bus stations), personnel flow areas, outdoors, public areas (such as public places), activity spaces (such as government agencies/shopping malls), etc. according to different needs or actual situations.

In one embodiment, the image monitoring platform A can be an image monitoring management platform, a digital image monitoring platform, etc., the image management system A1 can be a remote image management system, a cloud image management system, an intelligent image monitoring analysis system, etc., and the image monitoring device X can be an image monitor, an image camera (image recorder), an IP camera, an analog surveillance camera, a digital surveillance camera, etc. The user end E1 can be a user of the image monitoring platform A or an electronic device used, the equipment maintenance end E2 can be an equipment maintainer, an equipment maintenance personnel, or an electronic device used by the image monitoring device X, and the electronic device can be a personal computer, a laptop, a tablet computer, a smart phone, a smart watch, a communicator, a communication device, etc.

In the device health status based early warning analysis system 1 and method described in the present invention, firstly, the device status detection module 20 detects the device disconnection status 21, the device recording status 22 and the device recording storage remaining space 23 of the image monitoring device X managed by the image monitoring platform A, and then the device health status prediction analysis module 40 uses the health status value calculation method 41 according to the device disconnection status of the image monitoring device X detected by the device status detection module 20. The value of the device health status of the image monitoring device X is calculated by using the values of the device health status 21, the device recording status 22 and the device recording storage remaining space 23. Then, the device health status prediction and analysis module 40 uses the value of the device health status of the image monitoring device X calculated by the health status value calculation method 41 to predict and analyze whether the device health status of the image monitoring device X is in the warning range of good status 42, attention status 43 or emergency status 44.

Then, when the equipment health status prediction and analysis module 40 predicts and analyzes the equipment health status of the image monitoring device X calculated by the health status value calculation method 41 and finds that the equipment health status of the image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44, the warning module 50 sends a warning message indicating that the equipment health status of the image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44. The early warning notification message 52 is sent to at least one of the user terminal E1 and the associated equipment maintenance terminal E2 of the image monitoring platform A, so that at least one of the user terminal E1 and the equipment maintenance terminal E2 can pre-monitor or pre-process the abnormal problem of the image monitoring device X according to the early warning notification message 52 sent by the early warning module 50 indicating that the device health status of the image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44.

Specifically, the equipment health status-based early warning analysis system 1 can automatically determine the equipment health status of the image monitoring equipment X managed by the image monitoring platform A, and includes [1] an image management system detection module 10, [2] an equipment status detection module 20, [3] an equipment abnormal status exclusion module 30, [4] an equipment health status prediction analysis module 40, and [5] an early warning module 50.

[1] Image management system detection module 10: can detect the operating status 12 of at least one (e.g., multiple) image management systems A1 of an image monitoring platform A that manages multiple image monitoring devices X. That is, on the image monitoring platform A, each image monitoring device X will be assigned and managed in at least one (e.g., multiple) image management systems A1 of the image monitoring platform A. Therefore, the image management system detection module 10 can detect and record the operating status 12 of all image management systems A1 in the image monitoring platform A through the scheduling of the central monitoring unit 11, so that the image management system detection module 10 Or the central monitoring unit 11 determines whether the problem is a fault in the front-end image monitoring device X or a fault in the image management system A1 of the back-end image monitoring platform A based on the operating status 12 of the image management system A1 of the image monitoring platform A, and the scheduled detection frequency of the image management system detection module 10 or the central monitoring unit 11 will be adjusted based on the actual operating status of the image monitoring platform A.

[2] Device status detection module 20: can detect multiple device statuses of the image surveillance device X managed by the image surveillance platform A, such as the device disconnection status 21, the device recording status 22, and the device recording storage remaining space 23. For example, the device disconnection status 21 indicates that the image surveillance device X is in a disconnected state (e.g., unable to connect to the network) or a connected state (e.g., connected to the network), the device recording status 22 indicates that the image surveillance device X is in a continuous recording state (e.g., recording) or a stopped recording state (e.g., not recording), and the device recording storage remaining space 23 indicates the remaining storage space or remaining memory capacity of the recording screen of the image surveillance device X.

That is, the device status detection module 20 can regularly schedule the detection of the device status (such as device disconnection status 21, device recording status 22 and device recording storage remaining space 23) of the image monitoring device X managed by the image monitoring platform A through the image management system A1 of the image monitoring platform A, so that the subsequent device health status prediction analysis module 40 can judge whether the image monitoring device X is in a normal state (such as a good state 42) or an abnormal state (such as a warning state 43 or an emergency state 44) according to the device status of the image monitoring device X, and the scheduled detection frequency of the device status detection module 20 or the image management system A1 will be adjusted according to the actual operation status of the image monitoring platform A.

[3] Equipment abnormal state exclusion module 30: An exclusion list 31 (such as an exclusion device list) related to at least one image monitoring device X can be set on the image monitoring platform A, so that the equipment abnormal state exclusion module 30 uses the exclusion list 31 to exclude (skip) the set at least one image monitoring device X, and the subsequent equipment health status prediction and analysis module 40 does not need to perform prediction and analysis on the equipment health status of at least one image monitoring device X in the exclusion list 31 set by the equipment abnormal state exclusion module 30. Furthermore, the device abnormal state exclusion module 30 can also interface with the data acquisition application programming interface (API) D1 of the open data platform D of the meteorological unit (such as the open data platform of the Meteorological Bureau) to obtain the weather conditions D2 of the location (such as the region) of at least one image monitoring device X, and then the device abnormal state exclusion module 30 excludes the detection abnormality of at least one image monitoring device X according to the weather conditions D2.

For example, the device abnormal state exclusion module 30 can provide the following two exclusion methods to reduce or avoid misjudgment of the normal state (such as good state 42) or abnormal state (such as attention state 43 or emergency state 44) of the image monitoring device X, for example, the image monitoring device X is in a normal state but is misjudged as an abnormal state.

(a) The first exclusion method: The equipment abnormal state exclusion module 30 can provide the user terminal E1 (such as the owner) on the page (such as the user interface or operation interface) of the image monitoring platform A, through manual or automatic methods to exclude the detection anomalies of the early warning analysis system 1 based on the equipment health status caused by known or possible external factors (such as power interruption, network interruption, regional construction and other fault factors), so as to eliminate the abnormal state of at least one image monitoring device X caused by external factors.

(b) The second exclusion method: The equipment abnormal state exclusion module 30 can obtain the weather state D2 of each image monitoring device X installed outdoors (such as roads/intersections/public areas) through the data acquisition application program interface D1 of the central or local meteorological unit's open data platform D (such as the Meteorological Bureau's open data platform), so as to eliminate the abnormal state of the image monitoring device X caused by the weather state D2.

[4] Equipment health status prediction and analysis module 40: can analyze and quantify the data obtained by the image management system detection module 10, the equipment status detection module 20 and the equipment abnormal status exclusion module 30 to predict the equipment health status of the image monitoring device X. That is, the equipment health status prediction and analysis module 40 can integrate the detection results or exclusion results of the above-mentioned image management system detection module 10, the equipment status detection module 20 and the equipment abnormal status exclusion module 30, so as to predict the equipment health status of the image monitoring device X according to the current and historical equipment disconnection status 21, equipment recording status 22 and equipment recording storage of the image monitoring device X. The remaining space 23 and other situations are considered, and the manual method (such as manual operation) of the equipment abnormal state exclusion module 30 and the weather state D2 of the image monitoring device X are excluded for analysis. Then, the equipment health state prediction and analysis module 40 divides the equipment health state of the image monitoring device X into three warning ranges: good state 42, attention state 43 and emergency state 44.

[5] Early warning module 50: The early warning module 50 can utilize the predicted analysis result of the device health status of the image monitoring device X by the above-mentioned [4] device health status prediction and analysis module 40, so that the early warning module 50 can send (push) the early warning notification message 52 about the device health status of the image monitoring device X to at least one of the user end E1 and the device maintenance end E2 through the communication unit 51, and then at least one of the user end E1 and the device maintenance end E2 can pre-process the image monitoring device X predicted and analyzed as an emergency state 44 by the device health status prediction and analysis module 40 according to the early warning notification message 52.

In one embodiment, the early warning module 50 can use the prediction results or information obtained by the equipment health status prediction analysis module 40. When the equipment health status of the image monitoring equipment X has reached the warning range of the attention state 43 or the emergency state 44, the early warning module 50 sends a warning notification message 52 to the user of the image monitoring platform A through the communication unit 51, indicating that the equipment health status of the image monitoring equipment X has reached the warning range of the attention state 43 or the emergency state 44. At least one of the user end E1 (such as the owner) and the associated equipment maintenance end E2 (such as the equipment maintenance company/equipment maintenance personnel) pre-monitors (pays attention to) or pre-processes abnormal problems of the image monitoring device X according to the early warning notification message 52 sent by the early warning module 50 through the communication unit 51, indicating that the device health status of the image monitoring device X has reached the early warning range of the attention state 43 or the emergency state 44. For example, the aforementioned communication unit 51 may be a communication software, such as e-mail communication software, multi-party communication software (such as Line), instant messaging software (such as Messenger), third-party communication software, video conferencing software (such as Microsoft Teams), chat robot software (such as Line Bot), etc.

In other words, the source of the value of the device health status of the image monitoring device X is generated by two system schedules, wherein the first system schedule is that the central monitoring unit 11 of the image management system detection module 10 regularly detects the operation status 12 of each image management system A1 of the image monitoring platform A that manages multiple image monitoring devices X, and the second system schedule is that the device status detection module 20 regularly detects the device disconnection status 21, device recording status 22 and device recording storage remaining space 23 of the image monitoring device X managed by each image management system A1 of the image monitoring platform A.

The equipment health status prediction and analysis module 40 can use the following health status value calculation method 41 as the equipment health status judgment model to calculate the equipment health status value of the image monitoring device X managed by the image monitoring platform A, and then classify the equipment health status value of the image monitoring device X managed by the image monitoring platform A into each predetermined (customized) health status interval.

。 Health status value calculation method 41: Health status value of image monitoring device X

.

In the above health status value calculation method 41, i represents any one of 1 to n, n represents the number of sampling times (such as a positive integer equal to or greater than 2), and X represents the image monitoring device. C represents the value of the device disconnection status 21 of the image monitoring device X, R represents the value of the device recording status 22 of the image monitoring device X, and S is the value of the device recording storage remaining space 23 of the image monitoring device X, and the value C of the device disconnection status 21 of the image monitoring device X, the value R of the device recording status 22, and the value S of the device recording storage remaining space 23 are all between 0 and 1. α represents the coefficient of variation of the device disconnection status 21 of the image monitoring device X, β represents the coefficient of variation of the device recording status 22 of the image monitoring device X, and γ represents the coefficient of variation of the device recording storage remaining space 23 of the image monitoring device X.

From the above health status value calculation method 41, it can be known that the health status value calculation method 41 of the device health status prediction and analysis module 40 can sample the value C of the device disconnection status 21 of the image monitoring device X, the value R of the device recording status 22, and the value S of the device recording storage remaining space 23 for multiple times (such as n times), add them up and then average them, and then multiply them by their respective coefficients of variation α, β and γ, and then add up and calculate the value of the device health status of the image monitoring device X between 0 and 1, and finally present it as a percentage (%). If the value of the device health status of the image monitoring device X is larger, it means that the device health status of the image monitoring device X is better (such as good status 42). On the contrary, if the value of the device health status of the image monitoring device X is smaller, it means that the device health status of the image monitoring device X is worse (such as attention status 43 or emergency status 44).

The equipment health status prediction and analysis module 40 can use the equipment health status value of the image monitoring device X calculated by the above health status value calculation method 41 as a reference, and deduct the weather condition D2 and manual method of the location (such as the region) of the image monitoring device X to exclude external factors (such as: regional power outage, regional network disconnection, regional construction, and human manual influence). After that, the equipment health status prediction and analysis module 40 compares the threshold value of the health status interval of the image monitoring device X, and defines the equipment health status of the image monitoring device X and determines whether there is an abnormal image monitoring device X, which is conducive to achieving the purpose of automatically predicting the failure of the image monitoring device X.

The equipment health status prediction and analysis module 40 can classify the equipment health status value of the image monitoring equipment X into three warning ranges, namely, a good state 42, a caution state 43, and an emergency state 44. If the equipment health status value of the image monitoring equipment X is within the warning range of the caution state 43 or the emergency state 44, the warning module 50 will send a warning notification message through the communication unit 51 that the equipment health status of the image monitoring equipment X has reached the warning range of the caution state 43 or the emergency state 44. 52 to at least one of the user end E1 (such as the owner) and the associated equipment maintenance end E2 (such as the equipment maintenance provider/equipment maintenance personnel) of the image monitoring platform A, and then at least one of the user end E1 and the equipment maintenance end E2 pre-monitors (pays attention to) or pre-processes the abnormal problems of the image monitoring device X according to the early warning notification message 52 sent by the early warning module 50 through the communication unit 51, indicating that the equipment health status of the image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44.

The early warning analysis system 1 based on the equipment health status can be applied to the image monitoring platform A, so it is necessary to ensure that the image monitoring equipment X managed by the image monitoring platform A can record all the time (such as 24 hours). At the same time, on the image monitoring platform A, the image screen (such as the recording screen) of each image monitoring equipment X is crucial, because it is impossible to predict the time and location of the emergency, so the image screen of each image monitoring equipment X needs to have a full-time (24-hour) recording function. Therefore, in order to ensure that the image of the image monitoring device X is not interrupted, the present invention can accurately predict the occurrence of a fault before the image monitoring device X fails, thereby reducing the average time interval between failures of multiple image monitoring devices X, and also improving the equipment availability rate of the image monitoring devices X managed by the image monitoring platform A.

For example, with the rapid development of technologies such as artificial intelligence (AI), big data analysis, and unmanned vehicles (such as drones), the number of image surveillance devices X that are combined with smart security applications is increasing. Therefore, in order to ensure that the image of the image surveillance device X is not interrupted and to achieve full-time (such as 24 hours) recording, the present invention can provide an effective mechanism for detecting and preventing abnormalities in the image surveillance device X, so as to effectively reduce the mean time between failures of multiple image surveillance devices X, and also improve the equipment availability rate of the image surveillance devices X managed by the image surveillance platform A.

The present invention can analyze various information data (such as status data) of the image monitoring device X currently and historically managed by the image monitoring platform A, and utilize the early warning module 50 to send out an early warning notification message 52 before the image monitoring device X becomes abnormal. That is, the present invention can use the operating status 12 of the image management system A1 of the image monitoring platform A, the device disconnection status 21 of the image monitoring device X, the device recording status 22 and the remaining storage space 23 of the device recording, and the weather status D2 as reference data, and use the device health status prediction and analysis module 40 to calculate the value of the device health status of the image monitoring device X through the health status value calculation method 41, and then use the predetermined (customized) health status range to determine the device health status of the image monitoring device X managed by the image monitoring platform A, so that the early warning module 50 can send the relevant early warning notification message 52 according to the classification of the device health status of the image monitoring device X.

The present invention can use statistical analysis technology to obtain the current and historical statistical data such as the operating status of the image management system A1 of the image monitoring platform A, the device disconnection status 21 of the image monitoring device X, the device recording status 22 and the device recording storage remaining space 23, and automatically analyze or judge the value of the device health status of the image monitoring device X after excluding the influencing factors of the non-image monitoring device X itself (such as excluding external factors) through the health status value calculation method 41. It can also be applied to the image management system A1 image monitoring platform A as an early warning of the failure of the image monitoring device X.

The present invention provides a health status value calculation method 41 and statistical analysis technology to automatically determine the health status of each image monitoring device X. It can also immediately notify the equipment maintenance end E2 (such as equipment maintenance provider/equipment maintenance personnel) to repair the image monitoring device X before the image monitoring device X fails. In addition to improving the fault handling process of the image monitoring device X, it can also improve the equipment availability rate of the image monitoring device X managed by the image monitoring platform A.

FIG2 is a schematic diagram of an embodiment of the early warning analysis system 1 based on the health status of equipment and its method described in the present invention, and is explained together with FIG1.

As shown in Figure 2, firstly, the image management system detection module 10 and the device status detection module 20 are used to obtain the relevant status data B1 of the image management system A1 and the image monitoring device X respectively. For example, the image management system detection module 10 can detect the operation status 12 of the image management system A1 of the image monitoring platform A to store it in the database B, and the device status detection module 20 can detect the device disconnection status 21, device recording status 22 and device recording storage remaining space 23 of the image monitoring device X respectively. Status data B1 (device status) are stored in the database B.

Next, the device abnormal state exclusion module 30 can determine whether the image monitoring device X needs to be excluded (see step S11)? If yes (need to exclude/skip this image monitoring device X), then the device abnormal state exclusion module 30 is directly terminated or returned to, because the subsequent device health status prediction and analysis module 40 does not need to predict and analyze the device health status of the image monitoring device X in the exclusion list 31 set by the device abnormal state exclusion module 30; on the contrary, if no (no exclusion is set or this image monitoring device X does not need to be excluded/skipped), then the device health status prediction and analysis module 40 further determines whether the prediction result of the device health status of the image monitoring device X is good (such as good status 42; see step S12)? Furthermore, if (the predicted result of the device health status of the image monitoring device X is a normal good state 42), then the device abnormal state exclusion module 30 is directly terminated or returned, because the subsequent device health status prediction and analysis module 40 does not need to hand over the normal image monitoring device X to the early warning module 50 for early warning; on the contrary, if not (the predicted result of the device health status of the image monitoring device X is an abnormal caution state 43 or an emergency state 44), then the device health status prediction and analysis module 40 will hand over the abnormal image monitoring device X to the early warning module 50 for early warning.

That is, the early warning module 50 sends the early warning notification message 52 about the device health status of the image monitoring device X reaching the early warning range of the attention state 43 or the emergency state 44 to at least one of the user end E1 (such as the owner) and the associated device maintenance end E2 (such as the device maintenance company/equipment maintenance personnel) of the image monitoring platform A through the communication unit 51, and then at least one of the user end E1 and the device maintenance end E2 pre-monitors (pays attention to) or pre-processes the abnormal problem of the image monitoring device X according to the early warning notification message 52 about the device health status of the image monitoring device X reaching the early warning range of the attention state 43 or the emergency state 44 sent by the early warning module 50 through the communication unit 51.

FIG3 and FIG4 are schematic diagrams of implementation examples of the operation modes of the equipment abnormal state exclusion module 30 and the equipment health state prediction analysis module 40 in the early warning analysis system 1 and method based on the equipment health state described in the present invention, and are described together with FIG1 and FIG2. In addition, the present invention will respectively describe the following situations for [1] the processing exclusion case of the equipment abnormal state exclusion module 30 and [2] the analysis of the equipment health state prediction analysis module 40 as abnormal.

[1] Case study of the device abnormal state exclusion module 30: As shown in FIG3 , the device abnormal state exclusion module 30 can provide the user terminal E1 (such as the owner) with the ability to manually or automatically set the power outage (such as a power outage in a certain area), network outage (such as a network outage in a certain area), regional construction (such as road construction), etc. on the page (such as a user interface or operation interface) of the image monitoring platform A. External factors cause the early warning analysis system 1 based on the health status of the equipment to detect abnormalities for at least one image monitoring device X in the exclusion list 31 (see step S21), and the abnormal status exclusion module 30 excludes the user end E1 manually or automatically based on external factors including power outages, network outages, regional construction, etc., and sets at least one image monitoring device X in the exclusion list 31.

That is, the device abnormal state exclusion module 30 can determine whether it needs to be excluded according to the exclusion list 31 (see step S22)? If it is (need to exclude/skip this image monitoring device X), it means that this image monitoring device X has been set in the exclusion list 31, then the device abnormal state exclusion module 30 is directly terminated and jumped out, and waits for the next new device state (such as state data B1) to be imported into the early warning analysis system 1 based on the device health state, and then the device abnormal state exclusion module 30 will further judge the new device state (such as state data B1). On the contrary, if no (no need to exclude/skip this image monitoring device X), it means that this image monitoring device X is not set in the exclusion list 31, then the device abnormal state exclusion module 30 obtains the weather state D2 of the location (such as the region) of the image monitoring device X through the data acquisition application program interface D1 of the open data platform D of the meteorological unit (such as the open data platform of the Meteorological Bureau) (see step S23).

Then, the device abnormal state exclusion module 30 determines whether it is necessary to exclude due to the weather state D2 (see step S24)? If yes (need to exclude/skip this image monitoring device X), then the device abnormal state exclusion module 30 is directly terminated and jumped out. On the contrary, if no (need not exclude/skip this image monitoring device X), the device abnormal state exclusion module 30 excludes the video data abnormality or network interruption caused by the weather state D2 at the location (such as the region) of the image monitoring device X. If the image monitoring device X is not in the exclusion list 31 for the weather state D2 (such as weather factors), the device state of the image monitoring device X (such as the state data B1) is imported into the device health state prediction analysis module 40.

[2] The analysis of the equipment health status prediction and analysis module 40 is abnormal: As shown in FIG4 , when the equipment status of the image monitoring device X (such as the status data B1) is imported into the equipment health status prediction and analysis module 40 (see step S31), the equipment health status prediction and analysis module 40 can use the health status value calculation method 41 to analyze or judge the equipment health status of the image monitoring device X (see step S32), and define the equipment health status of the image monitoring device X as (a) good status 42, (b) caution status 43, and (c) emergency status 44 according to the actual operation status of the image monitoring platform A.

For example, (a) Good status 42 is a status in which the early warning analysis system 1 based on the health status of the equipment predicts that the image monitoring equipment X does not need to be processed. (b) Attention status 43 is to remind the user end E1 (such as the owner) and the equipment maintenance end E2 (such as the equipment maintenance company/equipment maintenance personnel) of the image monitoring platform A that they need to monitor (pay attention to) the subsequent status of the image monitoring equipment X in advance, but they do not need to prioritize processing of such image monitoring equipment X. (c) Emergency status 44 is to predict that the image monitoring equipment X is about to be abnormal, and the equipment maintenance end E2 (such as the equipment maintenance company) needs to immediately dispatch equipment maintenance personnel to the site for processing or prioritize processing of such image monitoring equipment X.

If the device health status prediction and analysis module 40 predicts and analyzes that the device health status of the image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44, the warning module 50 can send a warning notification message 52 to the user of the image monitoring platform A through the communication unit 51 according to the predetermined (customized) value range, indicating that the device health status of the abnormal image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44. At least one of the end E1 (such as the owner) and the associated equipment maintenance end E2 (such as the equipment maintenance company/equipment maintenance personnel), and then at least one of the user end E1 and the equipment maintenance end E2 pre-monitors (pays attention to) or pre-processes the abnormal problem of the image monitoring device X according to the early warning notification message 52 sent by the early warning module 50 through the communication unit 51, indicating that the equipment health status of the image monitoring device X has reached the warning range of the attention state 43 or the emergency state 44. In addition, if the equipment health status prediction and analysis module 40 predicts and analyzes that the equipment health status of the image monitoring device X is in a good state 42, it means that there is no need to perform subsequent processing on this image monitoring device X.

。假定影像監控設備X之區間採樣次數為60次，其中有10次為影像監控設備X之設備斷連線狀態21之偵測異常(即60-10=50次為設備斷連線狀態21之偵測正常)，有5次為影像監控設備X之設備錄影狀態22之偵測異常(即60-5=55次為設備錄影狀態22之偵測正常)，有3次為影像監控設備X之設備錄影儲存剩餘空間23之偵測異常(即60-3=57次為設備錄影儲存剩餘空間23之偵測正常)。 For example, the health status value calculation method 41 of the equipment health status prediction and analysis module 40 is the value of the equipment health status of the image monitoring equipment X.

Assume that the interval sampling times of the image surveillance device X is 60 times, of which 10 times are detection abnormalities of the device disconnection status 21 of the image surveillance device X (i.e. 60-10=50 times are detection normal of the device disconnection status 21), 5 times are detection abnormalities of the device recording status 22 of the image surveillance device X (i.e. 60-5=55 times are detection normal of the device recording status 22), and 3 times are detection abnormalities of the device recording storage remaining space 23 of the image surveillance device X (i.e. 60-3=57 times are detection normal of the device recording storage remaining space 23).

， 影像監控設備X之設備錄影狀態22之偵測正常之數值為

0.916，且影像監控設備X之設備錄影儲存剩餘空間23之偵測正常之數值為

。 Therefore, the equipment health status prediction and analysis module 40 can bring the above-mentioned normal detection times into the health status value calculation method 41 to obtain the following values. For example: The normal detection value of the equipment disconnection status 21 of the image monitoring device X is

, The normal value of the device recording status 22 of the video surveillance device X is

0.916, and the remaining space 23 of the video storage of the video surveillance device X is normal.

.

89.96%。 Next, the health status value calculation method 41 of the device health status prediction analysis module 40 can multiply the normal values of the device disconnection status 21, the device recording status 22 and the device recording storage remaining space 23 of the image monitoring device X by the corresponding coefficient of variation α, coefficient of variation β and coefficient of variation γ respectively. For example, the coefficient of variation α, coefficient of variation β and coefficient of variation γ each account for 1/3. Finally, the total value of the device health status of the image monitoring device X between 0 and 1 is calculated as

89.96%.

Finally, the equipment health status prediction analysis module 40 can apply the predetermined (customized) range of values of the equipment health status of the image monitoring device X, and determine that the value of the equipment health status of the image monitoring device X (e.g., 89.96%) is within the range of values of the attention state 43 shown in Table 1 below (e.g., 85 to 90%). Therefore, the early warning module 50 can send a warning message through the communication unit 51 that the equipment health status of the image monitoring device X has reached the early warning range of the attention state 43. The warning notification message 52 is sent to at least one of the user terminal E1 (such as the owner) and the associated equipment maintenance terminal E2 (such as the equipment maintenance provider/equipment maintenance personnel) of the image monitoring platform A. Then at least one of the user terminal E1 and the equipment maintenance terminal E2 monitors (pays attention to) the abnormal problem of the image monitoring device X in advance according to the warning notification message 52 sent by the warning module 50 through the communication unit 51, indicating that the device health status of the image monitoring device X has reached the warning range of the attention status 43.

Table 1: Equipment health status and value range of video surveillance equipment X

In addition, the present invention also provides a computer-readable medium for an early warning analysis method based on the health status of equipment, which is applied to a computing device or a computer having a processor and a memory, and the computer-readable medium stores instructions, and the computing device or the computer can execute the computer-readable medium through the processor and the memory to execute the above content when executing the computer-readable medium. In one embodiment, the computer-readable medium is a non-transitory computer-readable storage medium.

In one embodiment, the processor may be a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MPU), a microcontroller (MCU), etc., the memory may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a memory card, a hard drive (such as a cloud/network/external hard drive), an optical disk, a flash drive, a database, etc., and the computing device or computer may be a computer, a smartphone, a tablet computer, a personal computer, a laptop, a desktop computer, a server (such as a cloud/remote/network server), etc.

In summary, the early warning analysis system, method and computer-readable medium based on the health status of equipment described in the present invention have at least the following characteristics, advantages or technical effects.

1. The device status detection module of the present invention can effectively detect the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device managed by the image monitoring platform, so that the device health status prediction and analysis module can automatically calculate the device health status value of the image monitoring device using the health status value calculation method, and can also quickly predict and analyze whether the device health status of the image monitoring device is in a good state, a warning state or an emergency state based on the device health status value.

2. The present invention can automatically send a warning notification message to the user end or equipment maintenance end when the equipment health status prediction and analysis module predicts and analyzes that the equipment health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state using the health status numerical calculation method, so that the user end or equipment maintenance end can pre-monitor (pay attention to) or pre-process the abnormal problems of the image monitoring equipment in real time according to the warning notification message, thereby improving the equipment availability rate of the image monitoring equipment managed by the image monitoring platform.

3. The present invention can give an early warning before the image surveillance equipment fails, so as to facilitate real-time processing of the image surveillance equipment in advance, which is beneficial to reduce the omission of recordings during the waiting window period of the image surveillance equipment, and can also improve the equipment availability rate of the image surveillance equipment managed by the image surveillance platform.

4. In order to ensure that the image of the video surveillance equipment is not interrupted and to achieve full-time (such as 24 hours) recording, the present invention can accurately predict the occurrence of a fault before the video surveillance equipment fails, and can also provide an effective mechanism for detecting and preventing abnormalities in the video surveillance equipment, so as to reduce the average failure time interval of multiple video surveillance equipment, and can also improve the equipment availability rate of the video surveillance equipment managed by the video surveillance platform.

5. The equipment abnormal state exclusion module of the present invention can set an exclusion list on the image monitoring platform to exclude (skip) the set image monitoring equipment, and can also interface with the open data platform of the meteorological unit to obtain the weather conditions at the location of the image monitoring equipment to exclude abnormal conditions detected by the image monitoring equipment, and can also reduce or avoid misjudgment of the normal state (such as good state) or abnormal state (such as warning state or emergency state) of the image monitoring equipment.

6. The equipment health status prediction and analysis module of the present invention can effectively know which image monitoring equipment needs to be processed in advance through automated data analysis (such as health status value calculation method), which is beneficial to improve the equipment availability rate of the image monitoring equipment managed by the image monitoring platform.

7. The equipment health status prediction and analysis module of the present invention can calculate the equipment health status (such as good status, warning status, emergency status) of the image monitoring equipment through the health status value calculation method (equipment health status judgment model), and exclude the influence of other external factors such as power outage (such as local power outage), network outage (such as local network outage), regional construction (such as road construction), and human manual influence, and compare the threshold value of the health status interval of the image monitoring equipment. Based on this, the equipment health status of the image monitoring equipment is defined to achieve the purpose of automatically predicting the failure of the image monitoring equipment.

8. The present invention can use statistical analysis technology to obtain statistical data such as the operating status of the image management system of the image monitoring platform, the device disconnection status of the image monitoring equipment, the device recording status and the remaining storage space of the device recording, and effectively exclude the influencing factors of non-image monitoring equipment itself (such as excluding external factors) through the health status value calculation method, and can also automatically analyze or judge the value of the device health status of the image monitoring equipment, and can also be applied to the image monitoring platform as an early warning of image monitoring equipment failure.

9. The present invention can provide a health status value calculation method and statistical analysis technology to automatically determine the health status of each image monitoring device. It can also immediately notify the equipment maintenance department to repair the image monitoring device before the image monitoring device fails. In addition to improving the fault handling process of the image monitoring device, it can also improve the equipment availability rate of the image monitoring equipment managed by the image monitoring platform.

The above implementation forms are only illustrative of the principles, features and effects of the present invention, and are not intended to limit the scope of implementation of the present invention. Anyone familiar with this technology can modify and change the above implementation forms without violating the spirit and scope of the present invention. Any equivalent changes and modifications completed using the content disclosed by the present invention should still be covered by the scope of the patent application. Therefore, the scope of protection of the present invention should be as listed in the scope of the patent application.

1: Early warning analysis system

10: Image management system detection module

11: Central monitoring unit

12: Operation status

20: Equipment status detection module

21: Device disconnected status

22: Device recording status

23: Device video storage remaining space

30: Equipment abnormal status exclusion module

31: Exclusion list

40: Equipment health status prediction and analysis module

41: Method for calculating health status values

42: Good condition

43: Attention status

44: Emergency

50: Early warning module

51: Communication unit

52: Warning notification message

A: Image monitoring platform

A1: Image management system

D: Open data platform of meteorological units

D1: Data Acquisition API

D2: Weather conditions

E1: User side

E2: Equipment maintenance end

X: Image monitoring equipment

Claims (11)

A device health status based early warning analysis system includes: a device status detection module, which detects the device disconnection status, device recording status and device recording storage remaining space of an image monitoring device managed by an image monitoring platform; a device health status prediction analysis module, which is connected to the device status detection module, so that the device health status prediction analysis module can predict the device health status of the image monitoring device according to the device disconnection status, device recording status and device recording storage remaining space detected by the device status detection module. The device health status of the image monitoring device is calculated by using the value of the device health status of the image monitoring device, and then the device health status prediction and analysis module uses the calculated value of the device health status of the image monitoring device to predict and analyze whether the device health status of the image monitoring device is in a warning range of a good state, a warning state, or an emergency state; and an early warning module is communicatively connected to the device health status prediction and analysis module to use the calculated value of the device health status of the image monitoring device in the device health status prediction and analysis module to predict and analyze whether the device health status of the image monitoring device is in a good state, a warning state, or an emergency state. When the device health status of the image monitoring device is predicted and analyzed to have reached the warning range of the attention state or the emergency state, the warning module sends a warning notification message about the device health status of the image monitoring device having reached the warning range of the attention state or the emergency state to at least one of the user end of the image monitoring platform and the associated device maintenance end, so that at least one of the user end and the device maintenance end can respond to the warning message sent by the warning module about the device health status of the image monitoring device having reached the warning range. The warning notification message in the warning range of the attention state or emergency state pre-monitors or pre-processes the abnormal problem of the image monitoring device. Among them, the attention state reminds the user end and the equipment maintenance end of the image monitoring platform to pre-monitor the subsequent status of the image monitoring device, but it is not necessary to prioritize the image monitoring device. The emergency state predicts that the image monitoring device is about to be abnormal, and the equipment maintenance end needs to immediately dispatch equipment maintenance personnel to the scene to handle or prioritize the image monitoring device. The early warning analysis system as described in claim 1 further includes an image management system detection module having a central monitoring unit, so that the central monitoring unit of the image management system detection module regularly detects the operation status of the image management system of the image monitoring platform that manages the image monitoring device, and the device status detection module regularly detects the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device managed by the image management system of the image monitoring platform. The early warning analysis system as described in claim 1 further includes an image management system detection module with a central monitoring unit, which detects the image management system of the image monitoring platform that manages the image monitoring equipment, wherein the image management system detection module detects and records the operating status of the image management system of the image monitoring platform through the scheduling of the central monitoring unit, so that the image management system detection module or the central monitoring unit can determine whether it is a fault problem of the image monitoring equipment or a fault problem of the image management system of the image monitoring platform according to the operating status of the image management system of the image monitoring platform. The early warning analysis system as described in claim 1 further includes a device abnormal state exclusion module, which sets an exclusion list related to at least one image monitoring device on the image monitoring platform, so that the device abnormal state exclusion module uses the exclusion list to exclude the set at least one image monitoring device, and the device health status prediction analysis module does not need to perform prediction analysis on the device health status of the at least one image monitoring device in the exclusion list set by the device abnormal state exclusion module. The early warning analysis system as described in claim 1, wherein the early warning module has a communication unit, so that when the health status of the image monitoring device has reached the early warning range of the emergency state, the early warning module sends the early warning notification message about the health status of the image monitoring device has reached the early warning range of the emergency state to at least one of the user end and the associated device maintenance end of the image monitoring platform through the communication unit, and then at least one of the user end and the device maintenance end pre-processes the abnormal problem of the image monitoring device according to the early warning notification message about the health status of the image monitoring device has reached the early warning range of the emergency state sent by the early warning module through the communication unit. A device health status based early warning analysis method includes: a device status detection module detects the device disconnection status, device recording status and device recording storage remaining space of an image monitoring device managed by an image monitoring platform; a device health status prediction analysis module calculates the device health status of the image monitoring device according to the values of the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device detected by the device status detection module. The equipment health status prediction and analysis module uses the calculated equipment health status value of the image monitoring device to predict and analyze whether the equipment health status of the image monitoring device is in a warning range of a good state, a warning state, or an emergency state; and when the equipment health status prediction and analysis module uses the calculated equipment health status value of the image monitoring device to predict and analyze whether the equipment health status of the image monitoring device has reached the warning state or the emergency state, When the warning range of the equipment health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state, the warning module sends a warning notification message to at least one of the user end of the image monitoring platform and the associated equipment maintenance end, so that at least one of the user end and the equipment maintenance end can respond to the warning message sent by the warning module that the equipment health status of the image monitoring equipment has reached the warning range of the attention state or the emergency state. The warning notification message in the interval pre-monitors or pre-processes the abnormal problems of the image monitoring equipment. Among them, the attention status reminds the user end and the equipment maintenance end of the image monitoring platform to pre-monitor the subsequent status of the image monitoring equipment, but it is not necessary to prioritize the image monitoring equipment. The emergency status predicts that the image monitoring equipment is about to be abnormal, and the equipment maintenance end needs to immediately dispatch equipment maintenance personnel to the scene to handle or prioritize the image monitoring equipment. The early warning analysis method as described in claim 6 further includes a central monitoring unit of an image management system detection module periodically detecting the operation status of the image management system of the image monitoring platform that manages the image monitoring device, and the device status detection module periodically detecting the device disconnection status, device recording status and device recording storage remaining space of the image monitoring device managed by the image management system of the image monitoring platform. The early warning analysis method as described in claim 6 further includes an image management system detection module having a central monitoring unit detecting the image management system of the image monitoring platform for managing the image monitoring device, wherein the image management system detection module detects and records the operating status of the image management system of the image monitoring platform through the scheduling of the central monitoring unit, so that the image management system detection module or the central monitoring unit determines whether it is a fault problem of the image monitoring device or a fault problem of the image management system of the image monitoring platform according to the operating status of the image management system of the image monitoring platform. The early warning analysis method as described in claim 6 further includes setting an exclusion list of at least one image monitoring device on the image monitoring platform by a device abnormal state exclusion module, so that the device abnormal state exclusion module uses the exclusion list to exclude the set at least one image monitoring device, and the device health status prediction analysis module does not need to perform prediction analysis on the device health status of the at least one image monitoring device in the exclusion list set by the device abnormal state exclusion module. The early warning analysis method as described in claim 6 further includes that when the health status of the image monitoring device has reached the early warning range of the emergency state, the early warning module sends the early warning notification message about the health status of the image monitoring device having reached the early warning range of the emergency state to at least one of the user end and the associated device maintenance end of the image monitoring platform through a communication unit, and then at least one of the user end and the device maintenance end pre-processes the abnormal problem of the image monitoring device according to the early warning notification message about the health status of the image monitoring device having reached the early warning range of the emergency state sent by the early warning module through the communication unit. A computer-readable medium, used in a computing device or a computer, stores instructions for executing an early warning analysis method based on equipment health status as described in any one of claims 6 to 10.

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