Safety Monitoring System and Method Based on Intelligent Jacking Platform

Abstract

The present invention relates to the technical field of safety supervision, and specifically discloses a safety monitoring system and method based on an intelligent jacking platform. The method includes determining monitoring points on the intelligent jacking platform, installing monitoring devices at the monitoring points, and synchronously constructing a data matrix; activating the monitoring devices, receiving the monitoring data uploaded by the monitoring devices, filling it into the data matrix to obtain the data matrix at each moment; determining the operating state of the intelligent jacking platform according to the data matrix, and synchronously determining the object positions of each object on the intelligent jacking platform; constructing a three-dimensional model containing prompt information according to the operating state of the intelligent jacking platform and the object positions of each object on the intelligent jacking platform; and sending the three-dimensional model containing prompt information to the management terminal. The present invention analyzes the collected data, then performs three-dimensional modeling, converts the data analysis result into a display parameter, and directly reflects it on the three-dimensional model, with extremely strong intuitiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of safety supervision, and specifically to a safety monitoring system and method based on an intelligent jacking platform. Background Art

[0002] The safety monitoring system and method based on an intelligent jacking platform mainly relate to the fields of construction machinery, intelligent control, and safety monitoring, and are usually applied to scenarios such as bridge jacking, building jacking, and heavy equipment maintenance.

[0003] The intelligent jacking platform involves safety issues and requires real-time monitoring. However, most of the existing monitoring processes are limited to the data collection stage. After screening the collected data and feeding it back to the management personnel, the intuitiveness is very poor, and the professional requirements for the management personnel are also very high. Most of the staff on the intelligent jacking platform are not professional data analysts. When they receive some numerical values, they are very likely to not know the meaning of the numerical values. Therefore, how to improve the intuitiveness of the safety monitoring results and facilitate the understanding of the staff who often use the intelligent jacking platform is the technical problem that the technical solution of the present invention wants to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide a safety monitoring system and method based on an intelligent jacking platform to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A safety monitoring method based on an intelligent jacking platform, the method includes:

[0007] Determine monitoring points on the intelligent jacking platform, install monitoring devices at the monitoring points, and synchronously construct a data matrix;

[0008] Activate the monitoring devices, receive the monitoring data uploaded by the monitoring devices, and fill it into the data matrix to obtain the data matrix at each moment;

[0009] Determine the operating state of the intelligent jacking platform according to the data matrix, and synchronously determine the object positions of each object on the intelligent jacking platform;

[0010] Construct a three-dimensional model containing prompt information according to the operating state of the intelligent jacking platform and the object positions of each object on the intelligent jacking platform;

[0011] Send the three-dimensional model containing prompt information to the management terminal.

[0012] As a further solution of the present invention: The step of determining monitoring points on the intelligent jacking platform, installing monitoring devices at the monitoring points, and synchronously constructing a data matrix includes:

[0013] Receive the monitoring density of each data type input by the administrator, and determine the monitoring points on the intelligent jacking platform according to the monitoring density; the monitoring density represents the number of monitoring points per unit area; the data types include pressure, displacement, and vibration signals;

[0014] Install monitoring devices at the monitoring points; the monitoring devices include pressure sensors, displacement sensors, and vibration sensors;

[0015] Create a data matrix according to the positional relationship of the monitoring points; each row and column position in the data matrix corresponds to a monitoring point; the data matrix contains labels representing data types, called type labels.

[0016] As a further solution of the present invention: the steps of activating the monitoring devices, receiving the monitoring data uploaded by the monitoring devices, and filling it into the data matrix to obtain the data matrix at each moment include:

[0017] For any type of monitoring device, activate the monitoring device according to the dynamic frequency;

[0018] Receive the monitoring data uploaded by the monitoring devices;

[0019] Fill the monitoring data into the corresponding row and column positions of the monitoring device;

[0020] Use the activation time of the monitoring device as the time label and insert it into the data matrix;

[0021] Among them, the determination process of the dynamic frequency is:

[0022] ; In the formula, represents the frequency of the th data acquisition process, represents the data change amount at the row and column position in the data matrix obtained in the th data acquisition process. The data change amount is obtained by subtracting the data obtained in the th data acquisition process from the data obtained in the th data acquisition process, and respectively represent the sizes of the data matrix, is a preset frequency standard value.

[0023] As a further solution of the present invention: the steps of determining the operating state of the intelligent jacking platform according to the data matrix and synchronously determining the object positions of each object on the intelligent jacking platform include:

[0024] Read the data matrix of each type at the current moment, compare it with the preset standard matrix, and calculate the similarity;

[0025] Determine and record the abnormality degree at the current moment according to the minimum value of the similarity degree;

[0026] Read the data matrix with the data type of pressure, cluster the element values in the data matrix, identify each type of element value, and determine the object type;

[0027] Statistically analyze the clustering results and their identification results to obtain the object positions of the objects.

[0028] As a further solution of the present invention: the step of constructing a three-dimensional model containing prompt information according to the operating state of the intelligent lifting platform and the object positions of each object on the intelligent lifting platform includes:

[0029] Read the abnormality degrees of each type of data matrix within a preset time period;

[0030] Identify the abnormality degrees, determine the reference color value, construct a three-dimensional model of the intelligent lifting platform, and use the reference color value as the display parameter of the three-dimensional model;

[0031] Read the object positions of each object on the intelligent lifting platform within a preset time period, and determine the slipping probability according to the object positions of each object;

[0032] Determine the additional color value according to the slipping probability, perform a slipping simulation on each object in the three-dimensional model to obtain the slipping area, and use the additional color value as the display parameter of the slipping path;

[0033] The determination process of the abnormality degree is as follows: ; where is the abnormality degree, is the minimum value of the similarity degree; is a preset correction coefficient.

[0034] As a further solution of the present invention: the method further includes:

[0035] Select visual sampling points on the intelligent lifting platform, and install cameras at the visual sampling points; the union of the acquisition ranges of the installed cameras is not less than the platform area of the intelligent lifting platform;

[0036] Based on the cameras, regularly obtain platform images, identify the platform images, and determine the object positions of each object;

[0037] Use the object positions of each object identified by the cameras as the standard data at this moment;

[0038] Compare the standard data with the identification results of the data matrix with the data type of pressure to judge the accuracy of the identification results of the data matrix with the data type of pressure;

[0039] Adjust the timing acquisition period of the camera according to the accuracy; the timing acquisition period is directly proportional to the accuracy.

[0040] The technical solution of the present invention also provides a safety monitoring system based on an intelligent lifting platform, and the system includes:

[0041] A monitoring point setting module, which is used to determine monitoring points on the intelligent lifting platform, install monitoring devices at the monitoring points, and synchronously construct a data matrix;

[0042] A data filling module, which is used to activate the monitoring devices, receive the monitoring data uploaded by the monitoring devices, and fill it into the data matrix to obtain the data matrix at each moment;

[0043] A data application module, which is used to determine the operating state of the intelligent lifting platform according to the data matrix, and synchronously determine the object positions of each object on the intelligent lifting platform;

[0044] A three-dimensional modeling module, which is used to construct a three-dimensional model containing prompt information according to the operating state of the intelligent lifting platform and the object positions of each object on the intelligent lifting platform;

[0045] An information feedback module, which is used to send the three-dimensional model containing prompt information to the management end.

[0046] As a further solution of the present invention: the monitoring point setting module includes:

[0047] A monitoring point determination unit, which is used to receive the monitoring density of each data type input by the administrator, and determine the monitoring points on the intelligent lifting platform according to the monitoring density; the monitoring density represents the number of monitoring points per unit area; the data types include pressure, displacement, and vibration signals;

[0048] An equipment installation unit, which is used to install monitoring devices at the monitoring points; the monitoring devices include pressure sensors, displacement sensors, and vibration sensors;

[0049] A matrix creation unit, which is used to create a data matrix according to the positional relationship of the monitoring points; each row and column position in the data matrix corresponds to a monitoring point; the data matrix contains labels representing data types, called type labels.

[0050] As a further solution of the present invention: the data filling module includes:

[0051] An equipment activation unit, which is used to activate the monitoring devices of any type according to a dynamic frequency;

[0052] A data receiving unit, which is used to receive the monitoring data uploaded by the monitoring devices;

[0053] A filling execution unit, which is used to fill the monitoring data into the corresponding row and column positions of the monitoring devices;

[0054] A label insertion unit, configured to insert the activation time of the monitoring device as a time label into the data matrix;

[0055] Wherein, the process of determining the dynamic frequency is as follows:

[0056] ; In the formula, represents the frequency of the th data acquisition process, represents the data change amount at the row and column position in the data matrix obtained by the th data acquisition process. The data change amount is obtained by subtracting the data obtained by the th data acquisition process from the data obtained by the th data acquisition process, and respectively represent the sizes of the data matrix, is a preset frequency standard value.

[0057] As a further solution of the present invention: the data application module includes:

[0058] A comparison unit, configured to read the data matrices of each type at the current moment, compare them with a preset standard matrix, and calculate the similarity;

[0059] An abnormality degree calculation unit, configured to determine and record the abnormality degree at the current moment according to the minimum value of the similarity;

[0060] A data clustering unit, configured to read the data matrix with the data type of pressure, cluster the element values in the data matrix, identify each type of element value, and determine the object type;

[0061] A result statistics unit, configured to statistically analyze the clustering result and its identification result to obtain the object position of the object;

[0062] The process of determining the abnormality degree is as follows: ; In the formula, is the abnormality degree, is the minimum value of the similarity; is a preset correction coefficient.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention analyzes the collected data, then performs three-dimensional modeling, converts the data analysis result into a display parameter, and directly reflects it on the three-dimensional model, which is extremely intuitive, convenient for the staff to understand, and does not require the staff to spend too much time understanding. Compared with numerical values, this method hardly requires thinking, while numerical values require a certain amount of thinking time, which will distract the staff, and this also improves a certain degree of risk. Brief Description of the Drawings

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0065] Figure 1 It is a flowchart of a safety monitoring method based on an intelligent lifting platform.

[0066] Figure 2 It is the first sub - flowchart of a safety monitoring method based on an intelligent lifting platform.

[0067] Figure 3 It is the second sub - flowchart of a safety monitoring method based on an intelligent lifting platform.

[0068] Figure 4 It is the third sub - flowchart of a safety monitoring method based on an intelligent lifting platform.

[0069] Figure 5 It is the fourth sub - flowchart of a safety monitoring method based on an intelligent lifting platform.

[0070] Figure 6 It is a block diagram of the composition structure of a safety monitoring system based on an intelligent lifting platform. Detailed Embodiments

[0071] In order to make the technical problems to be solved, technical solutions and beneficial effects of the present invention more clearly understood, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0072] Figure 1 It is a flowchart of a safety monitoring method based on an intelligent lifting platform. In an embodiment of the present invention, a safety monitoring method based on an intelligent lifting platform, the method includes:

[0073] Step S100: Determine monitoring points on the intelligent lifting platform, install monitoring devices at the monitoring points, and synchronously construct a data matrix;

[0074] Intelligent lifting platforms are usually applied to scenarios such as bridge jacking, building jacking, and heavy equipment maintenance. Intelligent lifting platforms are related to the safety of staff and need to be monitored in real - time. Determine monitoring points on the intelligent lifting platform and install monitoring devices at the monitoring points. The monitoring devices collect the operation data of the intelligent lifting platform. Since the number of monitoring points is not fixed and they are distributed on the platform, the operation data obtained at the same moment is not unique. Construct a data matrix according to the positions of the monitoring points, and use the data matrix to count these operation data.

[0075] Step S200: Activate the monitoring device, receive the monitoring data uploaded by the monitoring device, and fill it into the data matrix to obtain the data matrix at each moment;

[0076] In practical applications, when activating the monitoring device and receiving the monitoring data uploaded by the monitoring device, when creating the data matrix, the row and column positions in the data matrix correspond one-to-one with the monitoring device itself. Input the monitoring data into the corresponding row and column positions to obtain the data matrix; when the monitoring device acquires the monitoring data, it is necessary to obtain the time tag of the monitoring data. The same monitoring device uses the same frequency, and the time difference of the acquired monitoring data is extremely small (the error caused by transmission and other reasons can be ignored). After inserting the acquired monitoring data into the corresponding row and column positions in the data matrix, the data matrix is obtained, and the time tag of the data matrix adopts the acquisition time of the monitoring data.

[0077] Step S300: Determine the operating state of the intelligent jacking platform according to the data matrix, and synchronously determine the object positions of each object on the intelligent jacking platform;

[0078] The data matrix itself is the operating data of the intelligent jacking platform. Analyzing the data matrix can obtain the operating state of the intelligent jacking platform; in practical applications, the monitoring devices are generally some sensors, including pressure sensors, displacement sensors, inclination sensors, temperature sensors, vibration sensors, etc. Analyzing these data can, on the one hand, obtain the operating state of the intelligent jacking platform, and on the other hand, can also predict the object positions of each object on the intelligent jacking platform. The objects include the staff and their work, which can be obtained by means of a pressure sensor.

[0079] Step S400: Construct a three-dimensional model containing prompt information according to the operating state of the intelligent jacking platform and the object positions of each object on the intelligent jacking platform;

[0080] Construct a three-dimensional model according to the operating state of the intelligent jacking platform and the object positions of each object on the intelligent jacking platform. Since the operating state of the intelligent jacking platform and the object positions of each object on the intelligent jacking platform are all parameters at each moment, the constructed three-dimensional model is also the three-dimensional model at each moment. Displaying the three-dimensional models at each moment, a dynamic three-dimensional model is obtained. Marking some abnormal areas in the three-dimensional model is the prompt information; of course, the prompt information can also be some arrows and descriptive texts, and its format is various, which is used to characterize abnormal phenomena.

[0081] Step S500: Send the three-dimensional model containing prompt information to the management terminal;

[0082] When the three-dimensional model containing prompt information is created, it can be sent to the management terminal; the management terminal can intuitively observe the operating process of the intelligent jacking platform.

[0083] Figure 2 It is the first sub - process block diagram of the safety monitoring method based on an intelligent lifting platform. The steps of determining monitoring points on the intelligent lifting platform, installing monitoring devices at the monitoring points, and synchronously constructing a data matrix include:

[0084] Step S101: Receive the monitoring density of each data type input by the administrator, and determine monitoring points on the intelligent lifting platform according to the monitoring density; the monitoring density represents the number of monitoring points per unit area; the data types include pressure, displacement, and vibration signals;

[0085] Step S102: Install monitoring devices at the monitoring points; the monitoring devices include pressure sensors, displacement sensors, and vibration sensors;

[0086] Step S103: Create a data matrix according to the positional relationship of the monitoring points; each row - column position in the data matrix corresponds to a monitoring point; the data matrix contains labels representing data types, called type labels.

[0087] The data types to be monitored by the intelligent lifting platform are not unique. In an example of the technical solution of the present invention, they are limited to pressure, displacement, and vibration signals. Correspondingly, the installed monitoring devices include pressure sensors, displacement sensors, and vibration sensors. Specifically, for each data type, receive the monitoring density input by the administrator. The monitoring density represents the number of monitoring points per unit area. The conventional determination method is to use a corner point of the intelligent lifting platform as the origin, and construct a plane grid based on the origin and a preset unit size. Each grid point of the plane grid can be used as a monitoring point. Among them, the unit size is inversely proportional to the monitoring density. The greater the monitoring density, the smaller the unit size.

[0088] After installing monitoring devices at the monitoring points, a data matrix can be created according to the positional relationship of the monitoring points. When using a plane grid to select monitoring points, the monitoring points themselves have a "horizontal and vertical" positional relationship and can be directly mapped into a data matrix.

[0089] Figure 3 It is the second sub - process block diagram of the safety monitoring method based on an intelligent lifting platform. The steps of activating the monitoring devices, receiving the monitoring data uploaded by the monitoring devices, and filling it into the data matrix to obtain the data matrix at each moment include:

[0090] Step S201: Activate any type of monitoring device according to a dynamic frequency;

[0091] Step S202: Receive the monitoring data uploaded by the monitoring devices;

[0092] Step S203: Fill the monitoring data into the corresponding row and column positions of the monitoring device;

[0093] Step S204: Use the activation time of the monitoring device as a time tag and insert it into the data matrix.

[0094] In an example of the technical solution of the present invention, the process of acquiring and filling the monitoring data is described. For any type of monitoring device, the monitoring device is activated according to a dynamic frequency. Each time the monitoring device is activated, a monitoring data is acquired, and the monitoring data is filled into the corresponding row and column positions of the monitoring device to obtain a data matrix; then, the activation time of the monitoring device is used as a time tag and inserted into the data matrix; it should be noted that although there may be a certain difference between the time when the monitoring device feeds back data and the activation time, this difference is very small and can be ignored.

[0095] In addition, for each type of monitoring device, its acquisition frequency is dynamic, that is, it can be adjusted according to the actual situation. For example, if the data stability is relatively high, the acquisition frequency can be slightly decreased to reduce energy consumption; this process is of great significance because almost all the monitoring devices in the intelligent jacking platform are independently powered, using an independent power source such as a battery. Reducing energy consumption can increase the battery life.

[0096] Among them, the determination process of the dynamic frequency is as follows:

[0097] ; In the formula, represents the frequency of the th data acquisition process, represents the data change amount at the row and column position in the data matrix obtained in the th data acquisition process. The data change amount is obtained by subtracting the data obtained in the th data acquisition process from the data obtained in the th data acquisition process, and respectively represent the sizes of the data matrix, is a preset frequency standard value.

[0098] The above content explains the determination process of the frequency. The frequency at the current moment is determined by the existing data matrix at the latest moment. In the existing data matrix at the latest moment, calculate the data change amount at each row and column position. The data change amount is the difference between the data at this row and column position and the data at the same position at the previous moment, and then calculate the absolute value to ensure it is a positive value. Calculate the sum of the absolute values of the differences at all row and column positions, and then compound a function from zero to one to obtain , this function and The item is directly proportional to the function. Multiply this function by the preset standard frequency value to calculate the final frequency. The principle of the calculation process is that the more drastic the change, the higher the calculated frequency.

[0099] Figure 4 It is the third sub - process block diagram of the safety monitoring method based on the intelligent lifting platform. The steps of determining the operating state of the intelligent lifting platform according to the data matrix and synchronously determining the object positions of each object on the intelligent lifting platform include:

[0100] Step S301: Read the data matrix of each type at the current moment, compare it with the preset standard matrix, and calculate the similarity.

[0101] Step S302: Determine and record the abnormality degree at the current moment according to the minimum value of the similarity.

[0102] Step S303: Read the data matrix with the data type of pressure, cluster the element values in the data matrix, identify each type of element value, and determine the object type.

[0103] Step S304: Statistically analyze the clustering results and their identification results to obtain the object positions of the objects.

[0104] In an example of the technical solution of the present invention, the application process of the data is described. Read the data matrix of each type at the current moment, compare it with the preset standard matrix. The comparison process can adopt the conventional matrix comparison process. For example, calculate the similarity of the data at the same position and then sum them up. When summing up, weights can be introduced. The closer to the center, the lower the weight, because the closer to the center of the platform, the higher the safety level, and there is only a risk of falling at the edge. In addition, each type has a standard matrix, which is preset and represents the data of each monitoring point of this type in the standard state. After comparing the data matrix of each type with its corresponding standard matrix, the similarity can be obtained, and each type corresponds to a similarity.

[0105] Read the minimum value of the similarity and use it as a reference for the operating state. Its principle follows the "barrel principle". The lower the similarity, the higher the abnormality degree. The abnormality degree determined according to the minimum value of the similarity is the maximum abnormality degree and is used as the abnormality degree of the entire platform at the current moment.

[0106] The process of determining the abnormality degree is as follows: ; where is the abnormality degree, is the minimum value of the similarity; is the preset correction coefficient; The calculation principle of the abnormality degree is very simple, just inversely proportional to the minimum value of the similarity.

[0107] Further, separately read the data matrix with the data type of pressure. Each value in the data matrix with the data type of pressure represents how much pressure there is at each position on the platform at the current moment. In actual situations, an object may correspond to one monitoring point or multiple monitoring points. For example, a person's feet may step on one monitoring point or multiple monitoring points, and these will all be reflected in the data matrix. Compare the element values at each row and column position in the data matrix, and group the element values with a small enough difference into one category, obtaining multiple combinations of row and column positions. The element values corresponding to each group of row and column positions are similar. Calculate the mean value of the element values, and the mean value reflects the weight of the object. Based on the mean value, the object type can be determined. Finally, count the clustering results and their recognition results to obtain the object position of the object. Among them, the clustering result corresponds to the object position, which is the area composed of each category of row and column positions, and the recognition result corresponds to the object, which is the object type determined according to the mean value.

[0108] Figure 5 It is the fourth sub-process block diagram of the safety monitoring method based on the intelligent lifting platform. The step of constructing a three-dimensional model containing prompt information according to the operating state of the intelligent lifting platform and the object positions of each object on the intelligent lifting platform includes:

[0109] Step S401: Read the abnormality degree of each type of data matrix within a preset time period;

[0110] Step S402: Identify the abnormality degree, determine the reference color value, construct a three-dimensional model of the intelligent lifting platform, and use the reference color value as the display parameter of the three-dimensional model;

[0111] Step S403: Read the object positions of each object on the intelligent lifting platform within a preset time period, and determine the sliding probability according to the object positions of each object;

[0112] Step S404: Determine the additional color value according to the sliding probability, perform a sliding simulation on each object in the three-dimensional model to obtain the sliding area, and use the additional color value as the display parameter of the sliding path.

[0113] In an example of the technical solution of the present invention, the construction process of the three-dimensional model is specifically described. The instantaneous risk analysis process is relatively rare and has great contingency. Therefore, the risk analysis process often analyzes the situation within a certain period of time, such as within 10 seconds, half a minute, or one minute; read all the abnormality degrees of each type of data matrix within the preset period (each time the monitoring data is obtained, the data is filled in the data matrix and the abnormality degree is calculated once), identify the abnormality degree, determine the reference color value, read the three-dimensional model of the intelligent jacking platform (the creation stage of the three-dimensional model is very simple and also belongs to the prior art. Even when the platform is delivered, the three-dimensional model will be delivered together. In this application, it is regarded as known data and can be directly read), and use the determined reference color value as the display parameter of the three-dimensional model, which is equivalent to "coloring"; this process actually limits the prompt information to the display parameter, improves the intuitiveness, and there is no need to add additional markings such as arrows and texts, and the prompt information is more clear.

[0114] Furthermore, this application also introduces a slipping prediction and prompt function. Read the object positions of each object on the intelligent jacking platform within the preset period, and determine the slipping probability according to the object positions of each object. The slipping probability is related to the distance between the object position and the center. The farther away from the center, the greater the slipping probability. In addition, it is also related to the object type. The easier the object is to slip, the greater the slipping probability; determine the additional color value based on the slipping probability, and then perform a slipping simulation on each object in the three-dimensional model. Some areas can be extended on the basis of the three-dimensional model, and this area also has a display parameter, indicating the possible impact of the objects on the current intelligent jacking platform on people outside due to high-altitude throwing objects.

[0115] It is worth mentioning the relationship between the abnormality degree, the reference color value, and the display parameter:

[0116] The abnormality degree considered is the abnormality degree within a certain period of time. The mode value of the abnormality degree can be selected. Each type corresponds to a mode value of the abnormality degree. There are three types involved in this application. Convert the three mode values of the abnormality degree into hue, saturation, and lightness, which can be used as the reference color value and then as the display parameter. It can be considered that the reference color value and the display parameter are the same; when the number of types is more than three, any two need to be combined. For example, both the inclination angle and the pressure are represented by hue, and their mode values of the abnormality degree jointly determine the reference color value, which is then used as the display parameter.

[0117] Further, the relationship between the slipping probability, the additional color value, and the display parameter:

[0118] The conventional calculation scheme for the slipping probability is as follows: The slipping probability is related to the distance between the object's position and the center. The farther away from the center, the greater the slipping probability. This is actually a bit unreasonable. The platform is rectangular. At the same distance, one position is in the diagonal direction and the other is in the direction perpendicular to the side. The two slipping probabilities are different. For this, another scheme can be adopted in this application, that is, for any position, obtain the distances from this position to each side (the distance from a point to a line is itself the perpendicular distance), select the minimum distance, and determine the slipping probability according to the inverse ratio of the minimum distance. The smaller the minimum distance, the greater the slipping probability; then, select the hue as 0 degrees, which corresponds to red, set the saturation to 100%, indicating pure red, and determine the brightness according to the slipping probability. The greater the slipping probability, the higher the brightness, so as to obtain the additional color value and use it as the display parameter.

[0119] In addition, the hue can also be selected as 0 degrees, which corresponds to red, set the brightness to 100%, indicating the brightest, and then correspond the slipping probability to the saturation. The greater the slipping probability, the higher the saturation, so as to obtain the additional color value and use it as the display parameter.

[0120] Generally speaking, each parameter in the reference color value is related to a type of abnormality, and a certain parameter in the additional color value is related to the slipping probability.

[0121] As a preferred embodiment of the technical solution of the present invention, regarding the determination process of the slipping area, the slipping area is an extended area based on the slipping path, and the slipping path is the free-fall path of the object with an initial velocity. The extension radius is related to the object type. The description of this process is as follows:

[0122] First, determine an initial velocity. The initial velocity is a rate with a direction and can be set to zero. At this time, the slipping path is free fall, or a velocity away from the center of the platform can also be used. At this time, the slipping path is a type of oblique projectile motion with resistance. After obtaining the slipping path, determine an extension radius based on the object type and expand the path into a cylindrical area, which is the extended area; It is worth mentioning that the initial velocity and resistance are generally preset at a port to receive the setting request input by the user for independent setting.

[0123] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0124] Select visual sampling points on the intelligent lifting platform and install cameras at the visual sampling points; the union of the acquisition ranges of the installed cameras is not less than the platform area of the intelligent lifting platform;

[0125] Regularly obtain platform images based on the cameras and identify the platform images to determine the object positions of each object;

[0126] Take the object positions of each object recognized by the camera as the standard data at this moment;

[0127] Compare the standard data with the recognition result of the data matrix of the data type of pressure, and judge the accuracy of the recognition result of the data matrix of the data type of pressure;

[0128] Adjust the timing acquisition period of the camera according to the accuracy; the timing acquisition period is directly proportional to the accuracy.

[0129] In an example of the technical solution of the present invention, a visual recognition solution is provided. Select visual sampling points on the intelligent lifting platform, such as one or several corner points at the top of the platform, and install cameras at the visual sampling points. No matter how many cameras are installed, as long as all positions on the platform can be monitored; based on the camera to regularly acquire the platform image, recognize the platform image to determine the object positions of each object. The visual detection result is definitely more accurate than the detection result of the pressure sensor, but it consumes more resources. When the object position is recognized by the camera, first take it as the standard data to replace the original recognition result based on pressure. At the same time, compare the two to judge the accuracy of the recognition result based on pressure. The higher the accuracy, the lower the application frequency of the camera can be, and the longer its timing acquisition recognition period is, simply called the timing acquisition period; generally speaking, the timing acquisition period is directly proportional to the accuracy.

[0130] Figure 6 As the composition structure block diagram of the safety monitoring system based on the intelligent lifting platform, in an embodiment of the present invention, a safety monitoring system based on the intelligent lifting platform, the system 10 includes:

[0131] A monitoring point setting module 11, configured to determine monitoring points on the intelligent lifting platform, install monitoring devices at the monitoring points, and synchronously construct a data matrix;

[0132] A data filling module 12, configured to activate the monitoring devices, receive the monitoring data uploaded by the monitoring devices, and fill it into the data matrix to obtain the data matrix at each moment;

[0133] A data application module 13, configured to determine the operating state of the intelligent lifting platform according to the data matrix, and synchronously determine the object positions of each object on the intelligent lifting platform;

[0134] A three-dimensional modeling module 14, configured to construct a three-dimensional model containing prompt information according to the operating state of the intelligent lifting platform and the object positions of each object on the intelligent lifting platform;

[0135] An information feedback module 15, configured to send the three-dimensional model containing prompt information to the management end.

[0136] Furthermore, the monitoring point setting module 11 includes:

[0137] A monitoring point determination unit, configured to receive the monitoring density of each data type input by an administrator, and determine monitoring points on the intelligent jacking platform according to the monitoring density; the monitoring density represents the number of monitoring points per unit area; the data types include pressure, displacement, and vibration signals.

[0138] An equipment installation unit, configured to install monitoring equipment at the monitoring points; the monitoring equipment includes a pressure sensor, a displacement sensor, and a vibration sensor.

[0139] A matrix creation unit, configured to create a data matrix according to the positional relationship of the monitoring points; each row and column position in the data matrix corresponds to a monitoring point; the data matrix contains labels representing data types, called type labels.

[0140] Specifically, the data filling module 12 includes:

[0141] An equipment activation unit, configured to activate the monitoring equipment according to a dynamic frequency for any type of monitoring equipment.

[0142] A data receiving unit, configured to receive the monitoring data uploaded by the monitoring equipment.

[0143] A filling execution unit, configured to fill the monitoring data into the corresponding row and column positions of the monitoring equipment.

[0144] A label insertion unit, configured to insert the activation time of the monitoring equipment as a time label into the data matrix.

[0145] Wherein, the determination process of the dynamic frequency is as follows:

[0146] ; in the formula, represents the frequency of the th data acquisition process, represents the data change amount at the row and column position in the data matrix obtained in the th data acquisition process. The data change amount is obtained by subtracting the data obtained in the th data acquisition process from the data obtained in the th data acquisition process. and respectively represent the dimensions of the data matrix, is a preset frequency standard value.

[0147] Furthermore, the data application module 13 includes:

[0148] A comparison unit, configured to read the data matrix of each type at the current moment, compare it with a preset standard matrix, and calculate the similarity.

[0149] An abnormality degree calculation unit for determining and recording the abnormality degree at the current moment according to the minimum value of the similarity degree;

[0150] A data clustering unit for reading a data matrix with a data type of pressure, clustering the element values in the data matrix, identifying each type of element value, and determining the object type;

[0151] A result statistics unit for statistically analyzing the clustering result and its identification result to obtain the object position of the object;

[0152] The process of determining the abnormality degree is as follows: ; where is the abnormality degree, is the minimum value of the similarity degree; is a preset correction coefficient.

[0153] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A safety monitoring method based on an intelligent lifting platform, characterized in that: The method comprises: Determine the monitoring points on the intelligent lifting platform, install monitoring equipment at the monitoring points, and simultaneously build a data matrix; Activate the monitoring device, receive the monitoring data uploaded by the monitoring device, fill it into the data matrix, and obtain the data matrix at each moment; Determine the operating status of the intelligent lifting platform according to the data matrix, and simultaneously determine the position of each object on the intelligent lifting platform; Construct a three-dimensional model containing prompt information according to the operation status of the intelligent lifting platform and the position of each object on the intelligent lifting platform; Send the three-dimensional model containing prompt information to the management end; The steps of activating the monitoring device, receiving the monitoring data uploaded by the monitoring device, filling the data matrix, and obtaining the data matrix at each moment include: For any type of monitoring equipment, the monitoring equipment is activated according to the dynamic frequency; Receive monitoring data uploaded by monitoring equipment; Fill the monitoring data into the row and column positions corresponding to the monitoring equipment; The activation time of the monitoring device is used as a time tag and inserted into the data matrix; The dynamic frequency determination process is as follows: ; In the formula, Indicates The frequency of the data acquisition process, Indicates The row and column positions of the data matrix obtained in the data acquisition process The data change at the The data obtained in the first data acquisition process is subtracted from the The data obtained in the data acquisition process is and denote the size of the data matrix, It is the preset frequency standard value.

2. The safety monitoring method based on the intelligent lifting platform according to claim 1 is characterized in that: The steps of determining monitoring points on the intelligent lifting platform, installing monitoring equipment at the monitoring points, and synchronously constructing a data matrix include: Receive the monitoring density of each data type input by the administrator, and determine the monitoring points on the intelligent jacking platform according to the monitoring density; the monitoring density represents the number of monitoring points per unit area; the data types include pressure, displacement and vibration signals; Installing monitoring equipment at the monitoring point; the monitoring equipment includes a pressure sensor, a displacement sensor and a vibration sensor; A data matrix is ​​created based on the positional relationship of the monitoring points; each row and column position in the data matrix corresponds to a monitoring point; the data matrix contains labels indicating the data type, called type labels.

3. The safety monitoring method based on the intelligent lifting platform according to claim 1 is characterized in that: The step of determining the operating state of the intelligent lifting platform according to the data matrix and synchronously determining the position of each object on the intelligent lifting platform comprises: Read the data matrix of each type at the current moment, compare it with the preset standard matrix, and calculate the similarity; Determine and record the abnormality at the current moment according to the minimum value of the similarity; Read a data matrix whose data type is pressure, cluster the element values ​​in the data matrix, identify each type of element value, and determine the object type; Statistically cluster the results and the recognition results to obtain the object position of the object; The process of determining abnormality is: ; In the formula, is the abnormality, is the minimum value of similarity; is the preset correction factor.

4. The safety monitoring method based on the intelligent lifting platform according to claim 1 is characterized in that: The step of constructing a three-dimensional model containing prompt information according to the operating state of the intelligent lifting platform and the object position of each object on the intelligent lifting platform includes: Read the abnormality of each type of data matrix within a preset time period; Identify the abnormality, determine the reference color value, build a three-dimensional model of the intelligent lifting platform, and use the reference color value as a display parameter of the three-dimensional model; Read the object position of each object on the intelligent lifting platform within a preset period of time, and determine the sliding probability according to the object position of each object; The additional color value is determined according to the sliding probability, and the sliding of each object is simulated in the three-dimensional model to obtain the sliding area, and the additional color value is used as the display parameter of the sliding path.

5. The safety monitoring method based on the intelligent lifting platform according to claim 1 is characterized in that: The method further comprises: Select visual sampling points on the intelligent lifting platform and install cameras at the visual sampling points; the union of the acquisition ranges of the installed cameras shall not be less than the platform area of ​​the intelligent lifting platform; Based on the camera, the platform image is acquired regularly, the platform image is recognized, and the object position of each object is determined; The object position of each object recognized by the camera is used as the standard data at that moment; Compare the standard data with the recognition result of the data matrix whose data type is pressure, and determine the accuracy of the recognition result of the data matrix whose data type is pressure; The timing acquisition period of the camera is adjusted according to the accuracy; the timing acquisition period is proportional to the accuracy.

6. A safety monitoring system based on an intelligent lifting platform, characterized in that: The system comprises: The monitoring point setting module is used to determine the monitoring points on the intelligent lifting platform, install monitoring equipment at the monitoring points, and simultaneously build a data matrix; The data filling module is used to activate the monitoring device, receive the monitoring data uploaded by the monitoring device, fill it into the data matrix, and obtain the data matrix at each moment; A data application module, used to determine the operating status of the intelligent lifting platform according to the data matrix, and simultaneously determine the object position of each object on the intelligent lifting platform; A three-dimensional modeling module, used to construct a three-dimensional model containing prompt information according to the operation status of the intelligent lifting platform and the object position of each object on the intelligent lifting platform; An information feedback module is used to send the three-dimensional model containing prompt information to the management end; The data filling module comprises: A device activation unit, used for activating the monitoring device of any type according to the dynamic frequency; A data receiving unit, used for receiving monitoring data uploaded by the monitoring device; A filling execution unit, used to fill the monitoring data into the row and column positions corresponding to the monitoring device; A label inserting unit, used for inserting the activation time of the monitoring device as a time label into the data matrix; The dynamic frequency determination process is as follows: ; In the formula, Indicates The frequency of the data acquisition process, The row and column positions of the data matrix obtained in the data acquisition process The data change at the The data obtained in the first data acquisition process is subtracted from the The data obtained in the data acquisition process is and denote the size of the data matrix, It is the preset frequency standard value.

7. The safety monitoring system based on the intelligent lifting platform according to claim 6 is characterized in that: The monitoring point setting module includes: A monitoring point determination unit, used to receive the monitoring density of each data type input by the administrator, and determine the monitoring points on the intelligent jacking platform according to the monitoring density; the monitoring density represents the number of monitoring points per unit area; the data types include pressure, displacement and vibration signals; An equipment installation unit, used to install monitoring equipment at the monitoring point; the monitoring equipment includes a pressure sensor, a displacement sensor and a vibration sensor; The matrix creation unit is used to create a data matrix according to the positional relationship of the monitoring points; each row and column position in the data matrix corresponds to a monitoring point; the data matrix contains a label indicating the data type, which is called a type label.

8. The safety monitoring system based on the intelligent lifting platform according to claim 6 is characterized in that: The data application module includes: A comparison unit is used to read the data matrix of each type at the current moment, compare it with the preset standard matrix, and calculate the similarity; An abnormality calculation unit, used to determine and record the abnormality at the current moment according to the minimum value of the similarity; A data clustering unit is used to read a data matrix whose data type is pressure, cluster the element values ​​in the data matrix, identify each type of element value, and determine the object type; A result statistics unit, used to count the clustering results and the recognition results, and obtain the object position of the object; The process of determining abnormality is: ; In the formula, is the abnormality, is the minimum value of similarity; is the preset correction factor.

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