Visual management method for intelligent venue inspection equipment

Abstract

The invention relates to the technical field of intelligent venue management, and discloses an intelligent venue inspection equipment visual management method comprising the following steps: S1, setting a plurality of inspection points in an intelligent venue; s2, setting a preset inspection action of each inspection point and a detection parameter of each inspection point; s3, acquiring image information data of the inspection equipment during inspection through an image acquisition unit; s4, acquiring detection data and data transmission data; s5, performing self-inspection analysis on the detection data, the image information data and the data transmission data to obtain a state type of the inspection equipment, and sending the state type to the management terminal for visual display; and S6, performing task allocation by the management personnel according to the state type of the inspection equipment, so that the management personnel can obtain the state type of the inspection equipment in time and make task allocation adjustment in time, thereby ensuring that the inspection task of the smart venue is completed in time, ensuring smooth use of the smart venue and improving user satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent venue management, and particularly relates to a visual management method for inspection equipment in an intelligent venue. Background Art

[0002] With the rapid development of Internet of Things technology and artificial intelligence technology, the concept and application of intelligent venues have been continuously deepened and expanded.

[0003] In order to ensure the normal operation of an intelligent venue, inspection equipment is usually used to automatically inspect the intelligent venue. The inspection equipment sequentially arrives at designated positions according to the inspection plan route for inspection, and collects detection data into the analysis module of the inspection equipment. The analysis module judges whether the intelligent venue can operate normally according to the detection data.

[0004] After long-term use of the inspection equipment, some abnormalities that cannot be observed by the naked eye will occur. If the management personnel do not discover them in time and use the abnormal inspection equipment for inspection, the accuracy rate of the inspection data may be relatively low. Therefore, it is impossible to accurately judge the operating status of the intelligent venue, which may increase the fault risk during the use of the intelligent venue and reduce the satisfaction of users. Summary of the Invention

[0005] The purpose of the present invention is to provide a visual management method for inspection equipment in an intelligent venue to solve the above technical problems: The purpose of the present invention can be achieved by the following technical solutions: A visual management method for inspection equipment in an intelligent venue includes the following steps: S1: Set a number of inspection points in the intelligent venue; S2: Set the preset inspection actions of the inspection action execution module on the inspection equipment at each inspection point and the detection parameters of each inspection point through a setting module; S3: Set up image acquisition units at each inspection point to collect image information data when the inspection equipment is inspecting; S4: The inspection equipment sequentially arrives at each inspection point according to the inspection route, executes the preset inspection actions at each inspection point for inspection and collects detection parameters to obtain detection data; and transmits the detection and image information data to the data storage module of the cloud platform and obtains data transmission data; S5: Obtain the status type of the inspection equipment through self-check analysis of the detection data, image information data and data transmission data in the data storage module, and send the status type to the management terminal for visual display; S6: The management personnel perform task allocation according to the status type of the inspection equipment.

[0006] As a further solution of the present invention: The data transmission data includes the The actual transmission duration for transmitting the detection data of each inspection point to the data storage module.

[0007] As a further solution of the present invention: The state types of the inspection equipment include normal, first-level abnormality, second-level abnormality, and third-level abnormality.

[0008] As a further solution of the present invention: The self-check analysis includes the following steps: S10: By analyzing the actual transmission duration for transmitting the detection data of each inspection point to the data storage module, obtain the first characteristic state index of the inspection equipment; S20: By analyzing the image information data and the monitoring data, obtain the second characteristic state index of the inspection equipment; S30: By analyzing the first characteristic state index and the second characteristic state index, obtain the state type of the inspection equipment.

[0009] As a further solution of the present invention: Through the formula:

[0010] Calculate the first characteristic state index of the inspection equipment ; Wherein, is the first judgment function. When , ; When , ; is the second judgment function. When , ; When , ; is the number of inspection points, ; is the actual transmission duration for transmitting the most recent detection data of the th inspection point to the data storage module; is the transmission duration adjustment coefficient of the th inspection point; is the preset transmission duration; is the first characteristic state first weight coefficient; is the first characteristic state second weight coefficient; is the first characteristic state third weight coefficient; is the first preset constant; is the second preset constant.

[0011] As a further solution of the present invention: The Transmission duration adjustment coefficient of each inspection point The obtaining process is as follows: S100: Obtain the real-time network signal strength of each inspection point through the network signal strength monitor of each inspection point device: S200: Analyze the real-time network signal strength of each inspection point to obtain the transmission duration adjustment coefficient of the th inspection point; As a further solution of the present invention: In step S200, through the formula:

[0012] Calculate the transmission duration adjustment coefficient of the th inspection point ; Wherein, is the real-time network signal strength; is the preset network strength; is the preset proportionality coefficient.

[0013] As a further solution of the present invention: Through the formula:

[0014] Calculate the second characteristic state index of the inspection device ; Wherein, is the number of detection parameters of the th inspection point, ; is the number of action influence coefficients of the th detection parameter of the th inspection point; is the preset accuracy of the inspection action; is the accuracy of the inspection action of the th inspection point; is the number of the most recent detection parameters of the th inspection point obtained by the data storage module; is the first weight coefficient of the second characteristic state; is the second weight coefficient of the second characteristic state; is the third preset constant; is the fourth preset constant.

[0015] As a further solution of the present invention: The accuracy of the inspection action of the th inspection point The obtaining process includes the following steps: S1000: By setting multiple marking points on the inspection operation execution module of the inspection device; S2000: By the image acquisition unit of the th inspection point acquires the image information data of the inspection device executing the preset inspection operation; S3000: By analyzing the image information data, the actual movement trajectories of each marking point are obtained; S4000: Analyze the actual movement trajectories of each marking point and the preset movement trajectories of each marking point to obtain the accuracy of the inspection operation of the

[0016] th inspection point.

[0017] As a further solution of the present invention: In step S4000, through the formula: calculate the accuracy of the inspection operation of the th inspection point; where is the lowest value among the trajectory coincidence degrees of the actual movement trajectories and the preset movement trajectories of all marking points of the inspection device at the

[0018] th inspection point. As a further solution of the present invention: The judgment process of the state type of the inspection device is as follows: When and When and the state type of the inspection device is normal; When and the state type of the inspection device is primary anomaly; When and the state type of the inspection device is secondary anomaly;

[0019] The beneficial effects of the present invention: The present invention first sets a number of inspection points in a smart venue; then, through a setting module, sets the preset inspection actions of the inspection action execution module on the inspection device at each inspection point and the detection parameters of each inspection point; then, by respectively setting an image acquisition unit at each inspection point to collect the image information data during the inspection of the inspection device; then, the inspection device sequentially reaches each inspection point according to the inspection route to execute the preset inspection actions at each inspection point for inspection and collect the detection parameters to obtain the detection data; and transmits the detection and image information data to the data storage module of the cloud platform and obtains the data transmission data; then, through self-check analysis of the detection data, image information data, and data transmission data in the data storage module, obtains the status type of the inspection device, and sends the status type to the management terminal for visual display; finally, the management personnel allocate inspection tasks according to the status type of the inspection device; enabling the management personnel to timely obtain the status type of the inspection device before allocating inspection tasks and make timely adjustments to the task allocation, thereby ensuring the timely completion of the inspection tasks in the smart venue, improving the accuracy of the detection data, thus realizing the accurate judgment of the operation status of the smart venue, reducing the failure risk during the use of the smart venue, and improving user satisfaction; (2) In the present invention, the management personnel can intuitively judge whether the inspection device is abnormal according to the status type and obtain the abnormal type, and the maintenance personnel can perform targeted maintenance according to the abnormal type, greatly shortening the maintenance time of the inspection device. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 It is a flowchart of the method according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 As shown, in one embodiment, a method for visual management of inspection devices in a smart venue is provided, including the following steps: S1: Set a number of inspection points in the smart venue; S2: Through a setting module, set the preset inspection actions of the inspection action execution module on the inspection device at each inspection point and the detection parameters of each inspection point; S3: By respectively setting an image acquisition unit at each inspection point to collect the image information data during the inspection of the inspection device; S4: The inspection device sequentially arrives at each inspection point according to the inspection route, executes the preset inspection actions at each inspection point for inspection, and collects detection parameters to obtain detection data; and transmits the detection and image information data to the data storage module of the cloud platform and obtains data transmission data; S5: By performing self-check analysis on the detection data, image information data, and data transmission data in the data storage module, obtain the status type of the inspection device, and send the status type to the management terminal for visual display; S6: The management personnel perform task allocation according to the status type of the inspection device; Through the above technical solution, in this embodiment, first, a number of inspection points are set in the intelligent venue; then, through the setting module, the preset inspection actions and the detection parameters of each inspection point of the inspection action execution module on the inspection device are set; then, image acquisition units are respectively arranged at each inspection point to collect image information data when the inspection device conducts inspections; then, the inspection device sequentially arrives at each inspection point according to the inspection route, executes the preset inspection actions at each inspection point for inspection, and collects detection parameters to obtain detection data; and transmits the detection and image information data to the data storage module of the cloud platform and obtains data transmission data; then, by performing self-check analysis on the detection data, image information data, and data transmission data in the data storage module, obtain the status type of the inspection device, and send the status type to the management terminal for visual display; finally, the management personnel perform inspection task allocation according to the status type of the inspection device; enabling the management personnel to timely obtain the status type of the inspection device before inspection task allocation, make timely adjustments to task allocation, thus ensuring the timely completion of the inspection tasks in the intelligent venue, improving the accuracy of detection data, thereby realizing the accurate judgment of the operation status of the intelligent venue, reducing the failure risk during the use of the intelligent venue, and improving user satisfaction.

[0024] As an implementation manner of the present invention, the data transmission data includes the actual transmission duration of the detection data of the

[0025] As an implementation manner of the present invention, the self-check analysis includes the following steps: S10: By analyzing the actual transmission duration of the detection data of each inspection point transmitted to the data storage module, obtain the first characteristic status index of the inspection device; S20: By analyzing the image information data and the detection data, obtain the second characteristic status index of the inspection device; S30: By analyzing the first characteristic status index and the second characteristic status index, obtain the status type of the inspection device.

[0026] Through the above technical solution, in this embodiment, the actual transmission duration of the detection data of each inspection point transmitted to the data storage module is first analyzed to obtain the first characteristic state index of the inspection device; then the image information data and the detection data are analyzed to obtain the second characteristic state index of the inspection device; finally, the first characteristic state index and the second characteristic state index are analyzed to obtain the state type of the inspection device.

[0027] As an implementation manner of the present invention, through the formula:

[0028] Calculate the first characteristic state index of the inspection device ; Wherein, is the first judgment function. When , ; when , ; is the second judgment function. When , ; when , ; is the number of inspection points, ; is the th actual transmission duration of the most recent detection data of the th inspection point transmitted to the data storage module; is the transmission duration adjustment coefficient of the th inspection point; is the preset transmission duration; is the first characteristic state first weight coefficient; is the first characteristic state second weight coefficient; is the first characteristic state third weight coefficient; is the first preset constant; is the second preset constant; Through the above technical solution, in this embodiment is the th preset transmission duration of the detection data of the th inspection point transmitted to the data storage module; is the difference between the actual transmission duration of the most recent detection data of the th inspection point transmitted to the data storage module and the preset transmission duration of the detection data of the th inspection point transmitted to the data storage module; in the formula in the first judgment function Refer to ; When , it indicates that the actual transmission duration of the most recent detection data of the -th inspection point transmitted to the data storage module is not greater than the preset transmission duration of the detection data of the -th inspection point transmitted to the data storage module, indicating that the data transmission status of the inspection device at the -th inspection point is good. ; When , it indicates that the actual transmission duration of the most recent detection data of the -th inspection point transmitted to the data storage module is greater than the preset transmission duration of the detection data of the -th inspection point transmitted to the data storage module, indicating that the data transmission status of the inspection device at the -th inspection point is poor. ; The difference between the actual transmission duration of the most recent detection data of the -th inspection point transmitted to the data storage module and the preset transmission duration of the detection data of the -th inspection point transmitted to the data storage module is larger, indicating that the data transmission status of the inspection device at the -th inspection point is worse; The first characteristic state index is larger; In the formula , in the second judgment function refers to ; When , it indicates that the actual transmission duration of the most recent detection data of the -th inspection point transmitted to the data storage module is not greater than the preset transmission duration of the detection data of the -th inspection point transmitted to the data storage module, indicating that the data transmission status of the inspection device at the -th inspection point is good. ; When , it indicates that the actual transmission duration of the most recent detection data of the -th inspection point transmitted to the data storage module is greater than the preset transmission duration of the detection data of the -th inspection point transmitted to the data storage module, indicating that the data transmission status of the inspection device at the -th inspection point is poor. ; is the total number of inspection points in which the actual transmission duration of the most recent detection data transmitted to the data storage module is greater than the preset transmission duration of the detection data of the -th inspection point transmitted to the data storage module; The actual transmission time of the most recent detection data transmitted to the data storage module at the inspection point is longer than The total number of preset transmission time for the detection data of each inspection point to be transmitted to the data storage module The more there are, the more inspection equipment is in the The worse the data transmission status of each inspection point is, the higher the first characteristic status index is. The bigger; for The actual transmission time of the latest detection data of each inspection point to the data storage module is The standard deviation of the difference in the preset transmission time of the detection data of each inspection point to the data storage module indicates the stability of the data transmission status of the inspection equipment. for The actual transmission time of the latest detection data of each inspection point to the data storage module is The standard deviation of the difference between the preset transmission time length of the detection data transmitted to the data storage module by each inspection point and the preset standard deviation; in the formula In the first judgment function of Reference ;when When The actual transmission time of the latest detection data of each inspection point to the data storage module is The standard deviation of the difference in the preset transmission time of the detection data of each inspection point to the data storage module is not higher than the preset standard deviation, and the data transmission state of the inspection equipment is relatively stable. ;when When The actual transmission time of the latest detection data of each inspection point to the data storage module is The standard deviation of the difference in the preset transmission time of the detection data from each inspection point to the data storage module is higher than the preset standard deviation, and the stability of the data transmission state of the inspection equipment is low. ; The actual transmission time of the latest detection data of each inspection point to the data storage module is The difference between the standard deviation of the preset transmission time length of the detection data of each inspection point transmitted to the data storage module and the preset standard deviation The larger the value, the lower the stability of the data transmission state of the inspection equipment. The bigger; It should be noted that the preset transmission time , preset standard deviation , the weight coefficient of the first characteristic state No. 1 , the weight coefficient of the second characteristic state of the first , the weight coefficient of the third characteristic state of the first , the first preset constant and the second preset constant are preset values, obtained according to experience and not elaborated here.

[0029] As an implementation manner of the present invention, the transmission duration adjustment coefficient of the th inspection point is obtained as follows: S100: By using the network signal strength monitor of each inspection point device, obtain the real-time network signal strength of each inspection point: S200: Then, through the formula calculate the transmission duration adjustment coefficient of the th inspection point; where is the real-time network signal strength; is the preset network strength; is the preset proportional coefficient; Through the above technical solution, in this embodiment, first, by using the network signal strength monitor of each inspection point device, obtain the real-time network signal strength of each inspection point: Then, through the formula calculate the transmission duration adjustment coefficient of the th inspection point; when the real-time network signal strength is greater than the preset network strength, the transmission duration adjustment coefficient of the th inspection point is less than 1, and the preset transmission duration for the detection data of the th inspection point to be transmitted to the data storage module is shorter; when the real-time network signal strength is greater than the preset network strength, the transmission duration adjustment coefficient of the th inspection point is greater than 1, and the preset transmission duration for the detection data of the th inspection point to be transmitted to the data storage module is longer; It should be noted that the real-time network signal strength , the preset network strength are; the preset proportional coefficient is a preset value, obtained according to experience and not elaborated here.

[0030] As an implementation manner of the present invention, through the formula:

[0031] Calculate the second characteristic state index of the inspection device ; Among them, is the number of detection parameters at the th inspection point, ; is the number action influence coefficient of the th detection parameter at the th inspection point; is the preset accuracy of the inspection action; is the accuracy of the inspection action at the th inspection point; is the number of the most recent detection parameters obtained by the data storage module at the th inspection point; is the first weight coefficient of the second characteristic state; is the second weight coefficient of the second characteristic state; is the third preset constant; is the fourth preset constant; Through the above technical solution, in this embodiment is the preset accuracy of the inspection action of the th detection parameter at the th inspection point; is the difference between the preset accuracy of the inspection action of the th detection parameter at the th inspection point and the accuracy of the inspection action at the th inspection point; In the formula , in the first judgment function of refers to ; When , it indicates that the accuracy of the inspection action at the th inspection point is not lower than the preset accuracy of the inspection action of the th detection parameter at the th inspection point. Therefore, the accuracy of the th detection parameter at the th inspection point is not affected, ; When , it indicates that the accuracy of the inspection action at the th inspection point is lower than the preset accuracy of the inspection action of the th detection parameter at the th inspection point. Therefore, the accuracy of the th detection parameter at the th inspection point decreases, ; the th inspection point, the preset accuracy of the inspection action for each inspection parameter of the th inspection point and the difference in accuracy of the inspection action of the greater the difference, the lower the accuracy of each inspection parameter of the th inspection point; is the cumulative amount of the difference between the preset accuracy of the inspection action for each inspection parameter of the th inspection point and the accuracy of the inspection action of the th inspection point; the greater the cumulative amount of the difference between the preset accuracy of the inspection action for each inspection parameter of the th inspection point and the accuracy of the inspection action of the th inspection point, the lower the accuracy of each inspection parameter of the th inspection point; the second characteristic state index of the inspection device is greater; is the data integrity of the th inspection point; in the formula , in the first judgment function the refers to ; when , it indicates that the data integrity of the th inspection point is not less than 1, and the collected data is complete, ; when , it indicates that the data integrity of the th inspection point is less than 1, the collected data is incomplete, and the greater the , the lower the data integrity, and the greater the second characteristic state index of the inspection device; the greater the second characteristic state index of the inspection device, it indicates that the quality of the data collected by the inspection device is worse; It should be noted that the quantity action influence coefficient of each inspection parameter of the th inspection point, the preset accuracy of the inspection action , the first weight coefficient of the second characteristic state , the second weight coefficient of the second characteristic state , the third preset constant and the fourth preset constant are preset values obtained based on experience and will not be elaborated here.

[0032] As an implementation manner of the present invention, for the accuracy of the inspection operation of the th inspection point the obtaining process includes the following steps: S1000: Set a plurality of marked points on the inspection operation execution module of the inspection device; S2000: Obtain the image information data of the inspection device performing the preset inspection operation through the image acquisition unit of the th inspection point; S3000: Analyze the image information data to obtain the actual movement trajectories of the marked points; S4000: Analyze the actual movement trajectories and the preset movement trajectories of the marked points to obtain the accuracy of the inspection operation of the th inspection point ; Through the above technical solution, in this embodiment, first, a plurality of marked points are set on the inspection operation execution module of the inspection device; then, the image acquisition unit of the th inspection point is used to obtain the image information data of the inspection device performing the preset inspection operation; then, the image information data is analyzed to obtain the actual movement trajectories of the marked points; finally, the actual movement trajectories and the preset movement trajectories of the marked points are analyzed to obtain the accuracy of the inspection operation of the th inspection point .

[0033] As an implementation manner of the present invention, in step S4000, through the formula:

[0034] calculate the accuracy of the inspection operation of the th inspection point ; wherein, is the lowest value among the trajectory coincidence degrees of the actual movement trajectories and the preset movement trajectories of all the marked points of the inspection device at the th inspection point; It should be noted that obtaining the trajectory coincidence degree based on the actual movement trajectory and the preset movement trajectory is a prior art and will not be elaborated here.

[0035] As an implementation manner of the present invention, the judgment process of the state type of the inspection device is as follows: When and the state type of the inspection device is normal; When and the state type of the inspection device is primary abnormality; When and , the status type of the inspection device is secondary anomaly; When and , the status type of the inspection device is tertiary anomaly; Through the above technical solution, in this embodiment, when and , it indicates that the data transmission status and data quality are good, and the status type of the inspection device is normal; when and , it indicates that the data transmission status is poor, but it does not affect the data quality. Normal inspection tasks can be executed, but the inspection speed will be affected; when and , it indicates that the data transmission status is good, but there is an anomaly in the inspection action execution module, and the inspection execution module needs to be repaired; when and , there are anomalies in both the data transmission module and the inspection action execution module, and both need to be repaired; The status types of the four inspection devices are sent to the management terminal for visual display, which is convenient for the administrator to arrange tasks for the inspection devices.

[0036] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equal changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A visualization management method for intelligent venue inspection equipment, characterized in that, It includes the following steps: S1: Set several inspection points in the intelligent venue; S2: Use the setting module to set the preset inspection actions of the inspection action execution module on the inspection device at each inspection point and the detection parameters of each inspection point; S3: Set up image acquisition units at each inspection point to collect image information data during the inspection of the inspection device; S4: The inspection device sequentially reaches each inspection point according to the inspection route, executes the preset inspection actions at each inspection point for inspection and collects detection parameters to obtain detection data; and transmits the detection and image information data to the data storage module of the cloud platform and obtains data transmission data; S5: Through self-check analysis of the detection data, image information data and data transmission data in the data storage module, obtain the status type of the inspection device, and send the status type to the management terminal for visual display; S6: The management personnel allocate tasks according to the status type of the inspection device.

2. The visualization management method for the intelligent venue inspection equipment according to claim 1, wherein, The data transmission data includes the actual transmission duration of transmitting the detection data of the th inspection point to the data storage module.

3. A visualization management method for intelligent venue inspection equipment according to claim 1, characterized in that, The status types of the inspection device include normal, first-level abnormality, second-level abnormality and third-level abnormality.

4. The visualization management method of an intelligent venue inspection device according to claim 2, wherein The self-check analysis includes the following steps: S10: Analyze the actual transmission duration of the detection data of each inspection point transmitted to the data storage module to obtain the first characteristic status index of the inspection device; S20: Analyze the image information data and monitoring data to obtain the second characteristic status index of the inspection device; S30: Analyze the first characteristic status index and the second characteristic status index to obtain the status type of the inspection device.

5. A visualization management method for intelligent venue inspection equipment according to claim 4, characterized in that, Through the formula: ; Calculate the first characteristic status index of the inspection equipment ; Among them, is the first judgment function. When happens, ; when happens, ; is the second judgment function. When happens, ; when happens, ; is the number of inspection points, ; is the actual transmission duration for the most recent detection data of the th inspection point to be transmitted to the data storage module; is the transmission duration adjustment coefficient of the th inspection point; is the preset transmission duration; is the preset standard deviation; is the first characteristic state first weight coefficient; is the first characteristic state second weight coefficient; is the first characteristic state third weight coefficient; is the first preset constant; is the second preset constant.

6. The visualization management method for the intelligent venue inspection equipment according to claim 5, the transmission duration adjustment coefficient of the first inspection point is obtained as follows: S100: Obtain the real-time network signal strength of each inspection point by installing network signal strength monitors at the devices of each inspection point; S200: By analyzing the real-time network signal strength of each inspection point, obtain the transmission duration adjustment coefficient of the th inspection point .

7. A visualization management method for intelligent venue inspection equipment according to claim 6, characterized in that, In step S200, through the formula: ; Calculate the transmission duration adjustment coefficient of the th inspection point; Among them, is the real-time network signal strength; is the preset network strength; is the preset proportionality coefficient.

8. A visualization management method for intelligent venue inspection equipment according to claim 7, characterized in that, Through the formula: ; Calculate the second characteristic status index of the inspection equipment ; Wherein, is the number of detection parameters of the th inspection point; ; is the number action influence coefficient of the th inspection point and the th detection parameter; is the preset accuracy of the inspection action; is the accuracy of the inspection action of the th inspection point; is the number of the most recent detection parameters of the th inspection point obtained by the data storage module; is the first weight coefficient of the second characteristic state; is the second weight coefficient of the second characteristic state; is the third preset constant; is the fourth preset constant.

9. A visualization management method for intelligent venue inspection equipment according to claim 8, characterized in that Accuracy of the inspection operation of the nth inspection point is obtained through the following steps: S1000: Set multiple marking points on the inspection action execution module of the inspection device; S2000: Obtain the image information data of the inspection equipment performing the preset inspection actions through the image acquisition unit of the th inspection point; S3000: Analyze the image information data to obtain the actual movement trajectories of each marking point; S4000: Analyze the actual movement trajectories of each marked point and the preset movement trajectories of each marked point to obtain the accuracy of the inspection actions at the th inspection point .

10. A visualization management method for intelligent venue inspection equipment according to claim 9, characterized in that, In step S4000, through the formula: ; Calculate the accuracy of the inspection action for the th inspection point ; Among them, is the lowest value among the trajectory coincidence degrees of the actual movement trajectories and the preset movement trajectories of all the marked points of the inspection equipment at the th inspection point.

11. A visualization management method for intelligent venue inspection equipment according to claim 10, characterized in that, The judgment process of the status type of the inspection device is: When and the status type of the inspection device is normal; When and , the status type of the inspection device is a first-level anomaly; When and , the status type of the inspection device is a secondary anomaly; When and , the status type of the inspection equipment is a third-level anomaly.

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