Safety monitoring system and method based on intelligent jacking platform

Abstract

The invention relates to the technical field of safety supervision, and particularly discloses a safety monitoring system and method based on an intelligent jacking platform, and the method comprises the steps: determining a monitoring point on the intelligent jacking platform, installing monitoring equipment at the monitoring point, and synchronously constructing a data matrix; activating monitoring equipment, receiving monitoring data uploaded by the monitoring equipment, and filling the monitoring data into the data matrix to obtain a data matrix at each moment; judging the operation state of the intelligent jacking platform according to the data matrix, and synchronously determining the object position of each object on the intelligent jacking platform; constructing a three-dimensional model containing prompt information according to the operation state of the intelligent jacking platform and the object position of each object on the intelligent jacking platform; and sending the three-dimensional model containing the prompt information to a management end. According to the method, the collected data is analyzed, then three-dimensional modeling is carried out, the data analysis result is converted into display parameters, the display parameters are directly reflected on the three-dimensional model, and the intuition is extremely high.

Description

Technical Field

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

[0002] The safety monitoring system and method based on the intelligent jacking platform mainly involve the fields of engineering machinery, intelligent control and safety monitoring, and are usually used in scenarios such as bridge jacking, building jacking, and heavy equipment maintenance.

[0003] The intelligent lifting platform involves safety issues and needs to be monitored in real time. However, the existing monitoring process is mostly limited to the data collection stage. After the collected data is screened, it is fed back to the management personnel, which has poor intuitiveness and high requirements on the professionalism of the management personnel. Most of the staff on the intelligent lifting platform are not professional data analysts. When they receive some values, they may not know the meaning of the values. Therefore, how to improve the intuitiveness of the safety monitoring results and facilitate the understanding of the staff who frequently use the intelligent lifting 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 lifting platform to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A safety monitoring method based on an intelligent jacking platform, the method comprising: 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.

[0006] As a further solution of the present invention: 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.

[0007] As a further solution of the present invention: 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.

[0008] As a further solution of the present invention: the step of determining the operating state of the intelligent lifting platform according to the data matrix and synchronously determining the object 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; The clustering results and recognition results are statistically analyzed to obtain the object position of the object.

[0009] 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 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; Determine the additional color value according to the sliding probability, simulate the sliding of each object in the three-dimensional model, obtain the sliding area, and use the additional color value as the display parameter of the sliding path; The process of determining abnormality is: ; In the formula, is the abnormality, is the minimum value of similarity; is the preset correction factor.

[0010] As a further solution of the present invention: 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.

[0011] The technical solution of the present invention also provides a safety monitoring system based on an intelligent jacking platform, the system comprising: 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; The information feedback module is used to send the three-dimensional model containing prompt information to the management end.

[0012] As a further solution of the present invention: 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.

[0013] As a further solution of the present invention: the data filling module includes: 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, 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.

[0014] As a further solution of the present invention: 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.

[0015] Compared with the prior art, the beneficial effects of the present invention are: the present invention analyzes the collected data, then performs three-dimensional modeling, converts the data analysis results into display parameters, and directly reflects them on the three-dimensional model. It is highly intuitive and easy for staff to understand, and does not require the staff to spend too much time on understanding. Compared with numerical values, this method requires almost no thinking, while numerical values ​​will require a certain amount of thinking time, which will distract the staff, and this also increases a certain degree of risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0017] Figure 1 It is a flowchart of the safety monitoring method based on the intelligent jacking platform.

[0018] Figure 2 This is the first sub-process flowchart of the safety monitoring method based on the intelligent jacking platform.

[0019] Figure 3 This is the second sub-process flowchart of the safety monitoring method based on the intelligent jacking platform.

[0020] Figure 4 This is the third sub-process flowchart of the safety monitoring method based on the intelligent jacking platform.

[0021] Figure 5 This is the fourth sub-process flowchart of the safety monitoring method based on the intelligent jacking platform.

[0022] Figure 6 This is a structural block diagram of the safety monitoring system based on the intelligent jacking platform. DETAILED DESCRIPTION

[0023] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0024] Figure 1 The flowchart of the safety monitoring method based on the intelligent lifting platform is as follows. In an embodiment of the present invention, a safety monitoring method based on the intelligent lifting platform includes: Step S100: determining monitoring points on the intelligent lifting platform, installing monitoring equipment at the monitoring points, and synchronously constructing a data matrix; Smart jacking platforms are usually used in scenarios such as bridge jacking, building jacking and heavy equipment maintenance. Smart jacking platforms are related to the safety of workers and require real-time monitoring. Monitoring points are determined on the smart jacking platforms, and monitoring equipment is installed at the monitoring points. The monitoring equipment collects the operating data of the smart jacking platform. Since the number of monitoring points is not fixed and it is distributed on the platform, the operating data obtained at the same time is not unique. A data matrix is ​​constructed according to the location of the monitoring points, and the data matrix is ​​used to count these operating data.

[0025] Step S200: activating the monitoring device, receiving the monitoring data uploaded by the monitoring device, filling it into the data matrix, and obtaining the data matrix at each moment; In actual applications, the monitoring device is activated and the monitoring data uploaded by the monitoring device is received. When creating the data matrix, the row and column positions in the data matrix correspond one-to-one to the monitoring device itself. The monitoring data is input into the corresponding row and column positions to obtain the data matrix. When the monitoring device obtains 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 uses the acquisition time of the monitoring data.

[0026] Step S300: determining the operating state of the intelligent lifting platform according to the data matrix, and simultaneously determining the object position of each object on the intelligent lifting platform; The data matrix itself is the operating data of the intelligent jacking platform. By analyzing the data matrix, the operating status of the intelligent jacking platform can be obtained. In practical applications, the monitoring equipment is generally some sensors, including pressure sensors, displacement sensors, inclination sensors, temperature sensors and vibration sensors. By analyzing these data, on the one hand, the operating status of the intelligent jacking platform can be obtained, and on the other hand, the position of each object on the intelligent jacking platform can be predicted, including the staff and their work, which can be obtained with the help of pressure sensors.

[0027] Step S400: 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; A three-dimensional model is constructed according to the operating status of the intelligent jacking platform and the object position of each object on the intelligent jacking platform. Since the operating status of the intelligent jacking platform and the object position of each object on the intelligent jacking platform are parameters at each moment, the constructed three-dimensional model is also a three-dimensional model at each moment. The three-dimensional model at each moment is displayed to obtain a dynamic three-dimensional model. Some abnormal areas are marked in the three-dimensional model, which is prompt information. Of course, the prompt information can also be some arrows and descriptive texts, which have many formats and are used to characterize abnormal phenomena.

[0028] Step S500: sending the three-dimensional model containing the prompt information to the management end; When the three-dimensional model containing prompt information is created, it can be sent to the management end; the management end can intuitively observe the operation process of the intelligent jacking platform.

[0029] Figure 2 The first sub-flow chart of the safety monitoring method based on the intelligent lifting platform is as follows. The steps of determining the monitoring points on the intelligent lifting platform, installing the monitoring equipment at the monitoring points, and synchronously constructing the data matrix include: Step S101: receiving the monitoring density of each data type input by the administrator, and determining 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; Step S102: installing monitoring equipment at the monitoring point; the monitoring equipment includes a pressure sensor, a displacement sensor and a vibration sensor; Step S103: 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, called a type label.

[0030] The types of data that need to be monitored by the intelligent jacking platform are not unique. In one example of the technical solution of the present invention, they are limited to pressure, displacement and vibration signals. Accordingly, the installed monitoring equipment includes pressure sensors, displacement sensors and vibration sensors. Specifically, for each data type, a monitoring density input by an administrator is received. The monitoring density represents the number of monitoring points per unit area. The conventional determination method is to take a corner point of the intelligent jacking 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, wherein the unit size is inversely proportional to the monitoring density. The larger the monitoring density, the smaller the unit size.

[0031] After installing the monitoring equipment at the monitoring points, a data matrix can be created based on the positional relationship of the monitoring points. When a plane grid is used to select monitoring points, the monitoring points themselves have a "horizontal and vertical" positional relationship and can be directly simulated into a data matrix.

[0032] Figure 3 The second sub-process flowchart of the safety monitoring method based on the intelligent lifting platform is shown in FIG. 1 . 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: Step S201: for any type of monitoring device, activating the monitoring device according to the dynamic frequency; Step S202: receiving monitoring data uploaded by the monitoring device; Step S203: Fill the monitoring data into the row and column positions corresponding to the monitoring device; Step S204: Use the activation time of the monitoring device as a time tag and insert it into the data matrix.

[0033] In an example of the technical solution of the present invention, the process of acquiring and filling monitoring data is described. For any type of monitoring equipment, the monitoring equipment is activated according to a dynamic frequency. Each time the monitoring equipment is activated, monitoring data is acquired and filled into the row and column positions corresponding to the monitoring equipment to obtain a data matrix. Then, the activation time of the monitoring equipment is used as a time label and inserted into the data matrix. It should be noted that, although the time when the monitoring equipment feedbacks data may be somewhat different from the activation time, such differences are very small and can be ignored.

[0034] In addition, for each type of monitoring equipment, its collection frequency is dynamic, that is, it can be adjusted according to actual conditions. For example, if the data stability is high, the collection frequency can be slightly reduced to reduce energy consumption. This process is of great significance because the monitoring equipment in the intelligent jacking platform is almost all independently powered and powered by an independent power supply, such as a battery, which reduces energy consumption and can increase battery life.

[0035] 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.

[0036] The above content explains the process of determining 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, the data change at each row and column position is calculated. The data change is the difference between the data at the row and column position and the data at the previous moment at the same position. Then the absolute value is calculated to ensure that it is a positive value. The sum of the absolute values ​​of the differences at all row and column positions is calculated, and then a zero-to-one function is compounded to obtain , this function is similar to The term is directly proportional to the frequency. The function is multiplied by the preset frequency standard value to calculate the final frequency. The principle of the calculation process is that the more drastic the change, the higher the calculated frequency.

[0037] Figure 4 The third sub-flow chart of the safety monitoring method based on the intelligent lifting platform, wherein the step of determining the operation 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: Step S301: reading the data matrix of each type at the current moment, comparing it with the preset standard matrix, and calculating the similarity; Step S302: determining and recording the abnormality at the current moment according to the minimum value of the similarity; Step S303: reading a data matrix whose data type is pressure, clustering the element values ​​in the data matrix, identifying each type of element value, and determining the object type; Step S304: Count the clustering results and the recognition results to obtain the object position of the object.

[0038] In an example of the technical solution of the present invention, the data application process is described, and the data matrix of each type at the current moment is read and compared with the preset standard matrix. The comparison process can adopt a conventional matrix comparison process. For example, the similarity of the data in the same position is calculated and then summed. When summing, weights can be introduced. The closer the position is to the center, the lower the weight, because the closer to the center of the platform, the higher the safety level, and there is a risk of falling only 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 under 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.

[0039] Read the minimum value of similarity and use it as a reference for the running status. The principle follows the "barrel principle". The lower the similarity, the higher the abnormality. The abnormality determined based on the minimum value of similarity is the maximum abnormality, which is the abnormality of the entire platform at the current moment.

[0040] The process of determining abnormality is: ; In the formula, is the abnormality, is the minimum value of similarity; is the preset correction coefficient; the calculation principle of the abnormality is very simple, and it can be inversely proportional to the minimum value of the similarity.

[0041] Furthermore, a data matrix with the data type of pressure is read separately. Each numerical value in the data matrix with the data type of pressure is the pressure 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. These will be reflected in the data matrix. The element values ​​at each row and column position in the data matrix are compared, and the element values ​​with sufficiently small differences are classified into one category. A combination of multiple row and column positions can be obtained. The element values ​​corresponding to each group of row and column positions are similar. The mean of the element values ​​is calculated. The mean reflects the weight of the object. The object type can be determined based on the mean. Finally, the clustering results and their recognition results are statistically analyzed to obtain the object position of the object, wherein the clustering result corresponds to the object position, which is the area composed of each type of row and column positions, and the recognition result corresponds to the object, which is the object type determined based on the mean.

[0042] Figure 5 The fourth sub-flow chart of the safety monitoring method based on the intelligent lifting platform, wherein 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 comprises: Step S401: reading the abnormality of each type of data matrix within a preset time period; Step S402: Identify the abnormality, determine a reference color value, construct a three-dimensional model of the intelligent lifting platform, and use the reference color value as a display parameter of the three-dimensional model; Step S403: reading the object position of each object on the intelligent lifting platform within a preset period of time, and determining the sliding probability according to the object position of each object; Step S404: determining an additional color value according to the sliding probability, performing sliding simulation on each object in the three-dimensional model to obtain a sliding area, and using the additional color value as a display parameter of the sliding path.

[0043] 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 is highly accidental. Therefore, the risk analysis process is often performed to analyze the situation within a period of time, such as 10 seconds, half a minute or one minute; all abnormalities of various types of data matrices within a preset time period are read (each time monitoring data is obtained, data is filled in the data matrix and the abnormality is calculated), the abnormality is identified, the reference color value is determined, and the three-dimensional model of the intelligent jacking platform is read (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 read directly), and the determined reference color value is used as the display parameter of the three-dimensional model, which is equivalent to "coloring"; this process actually limits the prompt information to display parameters, improves intuitiveness, and does not require additional annotations such as arrows and texts, and the prompt information is clearer.

[0044] Furthermore, the present application also introduces a slip prediction and prompt function, which reads the object position of each object on the intelligent jacking platform within a preset time period, and determines the slip probability according to the object position of each object. The slip probability is related to the distance between the object position and the center. The farther away from the center, the greater the slip probability. In addition, it is also related to the object type. The easier it is to slip, the greater the slip probability. Based on the slip probability, an additional color value is determined, and then, the slippage of each object is simulated in the three-dimensional model. Based on the three-dimensional model, some areas can be expanded. The area also has display parameters, indicating what kind of high-altitude throwing impact the objects on the current intelligent jacking platform may cause to people outside.

[0045] It is worth mentioning that the relationship between abnormality, reference color value and display parameters is: The abnormality degree refers to the abnormality degree within a period of time. The abnormality degree mode value can be selected. Each type corresponds to an abnormality degree mode value. There are three types involved in this application. The three abnormality degree modes are converted into hue, saturation and brightness, which can be used as reference color values, and then as display parameters. The reference color value and the display parameter can be considered to be the same; when there are more than three types, it is necessary to combine any two of them. For example, the inclination angle and pressure are both represented by hue, and the reference color value is determined by their abnormality degree modes, which is then used as the display parameter.

[0046] Furthermore, the relationship between the slip probability, additional color value and display parameters is: The conventional calculation scheme for the slip probability is: the slip probability is related to the distance between the object position and the center. The farther away from the center, the greater the slip probability. This is actually somewhat unreasonable. The platform is a rectangle. For the same distance, one position is in the diagonal direction and the other position is in the direction perpendicular to the side. The two slip probabilities are different. For this, the present application can adopt another scheme, that is, for any position, obtain the distance between the position and each side (the distance between the point and the line itself is the vertical distance), select the minimum distance, and determine the slip probability according to the inverse ratio of the minimum distance. The smaller the minimum distance, the greater the slip probability; then, select the hue as 0 degrees, which corresponds to red, and set the saturation to 100%, indicating pure red. The brightness is determined by the slip probability. The greater the slip probability, the higher the brightness, thereby obtaining the additional color value as a display parameter.

[0047] In addition, you can also select the hue as 0 degrees, which corresponds to red, and set the brightness to 100%, which means the brightest, and then correspond the slip probability to the saturation. The greater the slip probability, the higher the saturation, thereby obtaining an additional color value as a display parameter.

[0048] In summary, each parameter in the base color value is associated with a type of abnormality, and a parameter in the additional color value is associated with the probability of slipping.

[0049] As a preferred embodiment of the technical solution of the present invention, regarding the determination process of the sliding area, the sliding area is an extended area based on the sliding path, the sliding path is a free fall path of the object with an initial velocity, and the extension radius is related to the object type. The description of this process is as follows: First, determine an initial velocity, which is a rate with direction. It can be set to zero, in which case the sliding path is a free fall. Alternatively, you can use a velocity away from the center of the platform, in which case the sliding path is a quasi-projectile motion with resistance. After obtaining the sliding path, determine an expansion radius based on the object type, and expand the path into a cylindrical area, which is the expansion area. It is worth mentioning that the initial velocity and resistance are generally preset in a port to receive the setting request input by the user for autonomous setting.

[0050] As a preferred embodiment of the technical solution of the present invention, the method further includes: 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.

[0051] In an example of the technical solution of the present invention, a visual recognition solution is provided. Visual sampling points are selected on an intelligent lifting platform, such as one or several corner points at the top of the platform. Cameras are installed at the visual sampling points. No matter how many cameras are installed, as long as all positions on the platform can be monitored, the platform image is acquired based on the camera at a fixed time, the platform image is recognized, and the object position of each object is determined. The visual detection result is definitely more accurate than the detection result of the pressure sensor, but it consumes more resources. When the camera recognizes the position of the object, it is first used as standard data to replace the original pressure-based recognition result. At the same time, the two are compared to determine the accuracy of the pressure-based recognition result. The higher the accuracy, the lower the application frequency of the camera can be, and the longer the period of its regular acquisition and recognition, which is referred to as the regular acquisition cycle. In summary, the regular acquisition cycle is proportional to the accuracy.

[0052] Figure 6 : is a structural block diagram of a safety monitoring system based on an intelligent jacking platform. In an embodiment of the present invention, a safety monitoring system based on an intelligent jacking platform, the system 10 includes: A monitoring point setting module 11 is used to determine monitoring points on the intelligent lifting platform, install monitoring equipment at the monitoring points, and synchronously construct a data matrix; The data filling module 12 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; The data application module 13 is used to determine the operation 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 14, used to construct a three-dimensional model containing prompt information according to the operation status of the intelligent jacking platform and the object position of each object on the intelligent jacking platform; The information feedback module 15 is used to send the three-dimensional model containing the prompt information to the management end.

[0053] Furthermore, the monitoring point setting module 11 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.

[0054] Specifically, the data filling module 12 includes: 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, 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.

[0055] Furthermore, the data application module 13 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.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in 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.

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 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.

4. 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.

5. 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.

6. 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.

7. 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; The information feedback module is used to send the three-dimensional model containing prompt information to the management end.

8. The safety monitoring system based on the intelligent lifting platform according to claim 7 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.

9. The safety monitoring system based on the intelligent lifting platform according to claim 7 is characterized in that: 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, 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.

10. The safety monitoring system based on the intelligent lifting platform according to claim 7 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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