A data early warning method fusing multiple devices and multiple terminals

Abstract

The application belongs to the technical field of city management, and provides a data early warning method fusing multiple devices and multiple terminals, comprising: receiving first video data shot by a first camera of a city monitoring system, analyzing whether preset abnormal data exist in the first video data, and obtaining a first abnormal analysis probability; determining a first number and a second number according to the first abnormal analysis probability, screening a second number of second cameras belonging to the city monitoring system and a second number of third cameras not belonging to the city monitoring system in a preset area where the single camera shooting the first video data is located; based on the first video data, second video data shot by each second camera, and third video data shot by each third camera, performing secondary analysis on the preset abnormal data, and obtaining a second abnormal analysis probability; if the second abnormal analysis probability is higher than an abnormal threshold, outputting alarm information. The application realizes more accurate abnormal analysis.

Description

Technical Field

[0001] This invention relates to the field of urban management technology, and more specifically, to a data early warning method that integrates multiple devices and terminals. Background Technology

[0002] To achieve smarter and more efficient urban management, many cities have deployed data early warning systems based on surveillance videos. By analyzing the behavior of objects in the surveillance videos and identifying anomalies, alarms are triggered when anomalies are detected, so that relevant personnel can take timely action.

[0003] However, existing data early warning systems rely solely on urban surveillance systems deployed by city management departments. These systems often fail to provide complete coverage of the city, resulting in many abnormal objects and behaviors going undetected. Furthermore, existing systems typically analyze abnormal objects and behaviors based on a single surveillance video source, leading to insufficient accuracy and a high risk of false alarms.

[0004] It is evident that improving the comprehensiveness and accuracy of data early warning systems is a pressing technical problem that needs to be addressed. Summary of the Invention

[0005] In response, the present invention provides a data early warning method, electronic device, computer storage medium and computer program product that integrate multiple devices and terminals to solve at least one of the above-mentioned technical problems.

[0006] This invention provides a data early warning method integrating multiple devices and terminals, comprising the following steps: receiving first video data captured by a first camera of an urban monitoring system; analyzing whether preset abnormal data exists in the first video data to obtain a first anomaly analysis probability; wherein, the abnormal data refers to abnormal objects or abnormal behaviors; determining a first number and a second number based on the first anomaly analysis probability; within a preset area where the single camera capturing the first video data is located, filtering the first number of second cameras belonging to the urban monitoring system and filtering the second number of third cameras not belonging to the urban monitoring system; performing a second analysis on the preset abnormal data based on the first video data, the second video data captured by each of the second cameras, and the third video data captured by each of the third cameras to obtain a second anomaly analysis probability; if the second anomaly analysis probability is higher than an anomaly threshold, outputting an alarm message.

[0007] As an example, receiving the first video data captured by the first camera of the urban monitoring system includes: determining the selection information of the management personnel for a certain monitoring area; determining a number of cameras belonging to the urban monitoring system located in the monitoring area based on the selection information; determining a number of monitoring sub-areas based on the monitoring type corresponding to the selection information; determining the camera with the largest coverage angle in each of the monitoring sub-areas as the first camera; and receiving the first video data captured by each of the first cameras.

[0008] As an example, the step of analyzing whether there is preset abnormal data in the first video data to obtain a first abnormal analysis probability includes: filtering out the corresponding abnormal object analysis model and abnormal behavior analysis model from the database according to the monitoring type, inputting the first video data into the abnormal object analysis model and the abnormal behavior analysis model respectively, and obtaining the first abnormal object probability and the first abnormal behavior probability respectively; the first abnormal object probability and the first abnormal behavior probability constitute the first abnormal analysis probability.

[0009] As an example, determining the first number and the second number based on the first anomaly analysis probability includes: calculating a third number of cameras used for collaborative analysis based on the first anomaly analysis probability; further extracting the motion intensity of preset anomaly data from the first video data, determining an adjustment value based on the motion intensity, wherein the adjustment value is greater than or equal to 1; calculating a fourth number based on the third number and the adjustment value; obtaining the overall monitoring coverage rate of each camera belonging to the urban monitoring system within the monitoring area, determining a first percentage based on the overall monitoring coverage rate, and further calculating a second percentage = 1 - the first percentage; multiplying the fourth number by the first percentage to obtain the first number, and multiplying the fourth number by the second percentage to obtain the second number.

[0010] As an example, the calculation of the third number of cameras used for collaborative analysis based on the first anomaly analysis probability includes: ; In the formula, The third number of cameras used for collaborative analysis. The probability of the first anomaly analysis. This is the average of the first anomaly analysis probabilities for each monitoring type corresponding to the preset abnormal data. The median value of the first anomaly analysis probability in each monitoring type corresponding to the preset abnormal data; A preset number related to the number of cameras used for collaborative analysis; For weight values, .

[0011] As an example, the method further includes: obtaining carrier information of each of the third cameras that are not selected from the urban monitoring system; performing a handshake connection with the corresponding carrier based on the carrier information; and retrieving the third video data captured by the corresponding third camera after the handshake connection is successful.

[0012] As an example, the abnormal object analysis model and the abnormal behavior analysis model are built based on generative adversarial networks.

[0013] The present invention also provides a computer storage medium storing a computer program that can be executed by a processor to implement the method steps used by the processing apparatus as described in any of the preceding claims.

[0014] The present invention also provides a computer program product comprising a computer program executable by a processor to implement the method steps implemented by the processing apparatus as described in any of the preceding claims.

[0015] The beneficial effects of the present invention are as follows: the solution of the present invention achieves a more accurate anomaly analysis effect by fusing and analyzing video surveillance data from multiple devices and terminals, compared with the anomaly analysis method in the background technology that only relies on video data captured by a single camera in an urban monitoring system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a data early warning method integrating multiple devices and terminals disclosed in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the process of receiving first video data captured by the first camera of the urban monitoring system, as disclosed in an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram of the process for determining the first number and the second number based on the first anomaly analysis probability, as disclosed in an embodiment of the present invention. Detailed Implementation

[0020] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0022] Existing data-driven early warning systems rely solely on urban surveillance systems deployed by city management departments. However, these systems often fail to provide complete coverage of the city, leaving many abnormal objects and behaviors undetected. Furthermore, existing systems typically analyze abnormal objects and behaviors based on a single surveillance video source, resulting in insufficient accuracy and a high risk of false alarms. Therefore, improving the comprehensiveness and accuracy of data-driven early warning systems is a pressing technical challenge.

[0023] like Figure 1 As shown in the figure, an embodiment of the present invention discloses a data early warning method integrating multiple devices and terminals, including the following steps: receiving first video data captured by a first camera of an urban monitoring system; analyzing whether there is preset abnormal data in the first video data to obtain a first anomaly analysis probability; wherein, the abnormal data refers to abnormal objects or abnormal behaviors; determining a first number and a second number based on the first anomaly analysis probability; within a preset area where the single camera capturing the first video data is located, filtering the first number of second cameras belonging to the urban monitoring system and filtering the second number of third cameras not belonging to the urban monitoring system; performing a second analysis on the preset abnormal data based on the first video data, the second video data captured by each of the second cameras, and the third video data captured by each of the third cameras to obtain a second anomaly analysis probability; if the second anomaly analysis probability is higher than an anomaly threshold, then outputting an alarm message.

[0024] Compared to existing technologies, this invention simultaneously uses video data captured by cameras in urban surveillance systems and video data captured by cameras in non-urban surveillance systems to determine whether there are truly abnormal objects or behaviors in a corresponding area. In other words, it achieves accurate anomaly detection through the fusion analysis of monitoring data from multiple devices and terminals. Specifically, it receives first video data captured by the first camera in the urban surveillance system. Based on urban management needs, urban management departments will deploy many cameras distributed throughout the city, forming an urban surveillance system. These cameras include, for example, PTZ cameras and bullet cameras. The first video data is analyzed according to a preset analysis method to determine if preset abnormal data exists, obtaining a first anomaly analysis probability. Here, abnormal data refers to abnormal objects and behaviors. That is, the first video data is detected and analyzed for the presence of abnormal objects and behaviors, and the corresponding probabilities are output. Abnormal objects include, for example, wanted persons or persons under surveillance; abnormal behaviors include, for example, illegal street vending or graffiti.

[0025] Then, based on the first anomaly analysis probability, a first number and a second number are determined. Subsequently, a subset of cameras within a preset area (e.g., 300 meters) of the single camera capturing the first video data are selected. This includes the first number of second cameras belonging to the city surveillance system and the second number of third cameras not belonging to the city surveillance system. The third cameras not belonging to the city surveillance system refer to cameras installed in street-facing shops and shopping malls, as well as vehicle-mounted cameras with network transmission capabilities.

[0026] Finally, based on the aforementioned first video data and the video data captured by the selected second and third cameras, a second analysis is performed on the pre-determined preset abnormal data to obtain a second anomaly analysis probability. If this second anomaly analysis probability is higher than the anomaly threshold, it indicates that an anomaly does exist. In this case, an alarm message can be output to relevant personnel or terminal devices to remind them to take action.

[0027] As can be seen, the solution of the present invention achieves a more accurate anomaly analysis effect by fusing and analyzing video surveillance data from multiple devices and terminals, compared with the anomaly analysis method in the background technology that only relies on video data captured by a single camera in an urban monitoring system.

[0028] As an example, such as Figure 2As shown, receiving the first video data captured by the first camera of the urban monitoring system includes: determining the selection information of the management personnel for a certain monitoring area; determining a number of cameras belonging to the urban monitoring system located in the monitoring area based on the selection information; determining a number of monitoring sub-areas based on the monitoring type corresponding to the selection information; determining the camera with the largest coverage angle in each of the monitoring sub-areas as the first camera; and receiving the first video data captured by each of the first cameras.

[0029] In this embodiment, the management personnel at the monitoring center can select a monitoring area on the monitoring map based on actual monitoring needs. Upon selection of this monitoring area, several cameras belonging to the city's monitoring system within that area can be identified. Simultaneously, before or after selecting the monitoring area, the management personnel can select or input the monitoring type, such as fugitive tracking, anti-theft monitoring, or urban management. Different monitoring types can determine several different monitoring sub-areas. For example, for fugitive tracking, road areas can be used as monitoring sub-areas; for anti-theft monitoring, street-facing shops can be used; and for urban management, pedestrian areas between roads and buildings, alleyways, etc., can be used. Finally, the camera with the largest coverage angle within each monitoring sub-area is designated as the first camera. These first cameras achieve maximum coverage within the monitoring sub-area, reducing the probability of missing abnormal objects or behaviors.

[0030] As an example, the step of analyzing whether there is preset abnormal data in the first video data to obtain a first abnormal analysis probability includes: filtering out the corresponding abnormal object analysis model and abnormal behavior analysis model from the database according to the monitoring type, inputting the first video data into the abnormal object analysis model and the abnormal behavior analysis model respectively, and obtaining the first abnormal object probability and the first abnormal behavior probability respectively; the first abnormal object probability and the first abnormal behavior probability constitute the first abnormal analysis probability.

[0031] In this embodiment, the database contains various pre-set abnormal object analysis models and abnormal behavior analysis models. These models are pre-built and trained for different types of monitoring needs, enabling targeted analysis of abnormal objects and behaviors in areas such as fugitive tracking, theft monitoring, and urban management. Compared to traditional composite models, the classification-based pre-set models in this invention have smaller model sizes and faster analysis speeds, meeting the timeliness requirements of urban monitoring and management.

[0032] It is worth noting that abnormal object analysis models and abnormal behavior analysis models are preferably built based on generative adversarial networks (GANs). GANs are a type of deep learning model that has been widely used in the field of artificial intelligence since its introduction by Ian Goodfellow et al. in 2014. They consist of two neural networks: a generator and a discriminator, which compete against each other through adversarial training to achieve the goal of generating realistic data. The generator aims to generate artificial samples that are as close as possible to the real data distribution, while the discriminator aims to determine whether the input data is real or generated by the generator.

[0033] The core of GANs lies in their generators' ability to learn the distribution of real data and generate realistic samples. This characteristic enables GANs to generate samples highly similar to normal data in the anomaly detection of this invention, thus helping to more accurately identify anomalous objects and behaviors that deviate from normal data. Moreover, since GANs can use unsupervised learning in anomaly detection without labeled data, they can still perform effective anomaly detection in scenarios lacking labeled data, greatly reducing the difficulty of model construction.

[0034] It should be noted that the secondary analysis adopts the same method as this embodiment. The main difference is that the inputs of the abnormal object analysis model and the abnormal behavior analysis model are changed to the first video data, the second video data, and the third video data, which will not be elaborated further.

[0035] As an example, such as Figure 3 As shown, determining the first number and the second number based on the first anomaly analysis probability includes: calculating a third number of cameras used for collaborative analysis based on the first anomaly analysis probability; further extracting the motion intensity of preset anomaly data from the first video data, determining an adjustment value based on the motion intensity, wherein the adjustment value is greater than or equal to 1; calculating a fourth number based on the third number and the adjustment value; obtaining the overall monitoring coverage rate of each camera belonging to the urban monitoring system within the monitoring area, determining a first percentage based on the overall monitoring coverage rate, and further calculating a second percentage = 1 - the first percentage; multiplying the fourth number by the first percentage to obtain the first number, and multiplying the fourth number by the second percentage to obtain the second number.

[0036] In this embodiment, the secondary anomaly analysis is performed using cameras from both urban and non-urban surveillance systems, requiring the determination of the number of cameras in both systems. Specifically, firstly, the total number of cameras used for collaborative analysis, i.e., the third number, is calculated based on the first anomaly analysis probability. Here, the third number is negatively correlated with the first anomaly analysis probability; that is, the higher the first anomaly analysis probability, the higher the anomalousness of the abnormal object or behavior (e.g., a person is setting up a stall on the roadside, and some customers are surrounding the stall, and this behavior has continued for an hour). In this case, it is not necessary to acquire more video data for secondary analysis, and the third number is set to a smaller value accordingly. Conversely, the lower the first anomaly analysis probability, the lower the anomalousness of the abnormal object or behavior (e.g., an object's walking posture has a certain similarity to a fugitive being tracked, but facial information cannot be collected because the object is wearing a hat or mask). In this case, it is necessary to acquire more video data for secondary analysis, and the third number is set to a larger value accordingly.

[0037] Simultaneously, the motion intensity of preset abnormal data is extracted from the first video data. This motion intensity refers to the speed of movement involved in abnormal objects or behaviors, such as an abnormal object running (escaping), a drunk driver speeding, or erratic driving. This motion intensity can be evaluated based on preset rules or a video semantic understanding model (such as the BERT model), details of which will not be elaborated further. Then, an adjustment value can be determined based on the obtained motion intensity. The adjustment value is a value greater than or equal to 1, which can appropriately increase the third number to the fourth number. A higher determined motion intensity indicates that the object involved in the abnormal behavior is less likely to have its key information captured, or that the behavior is more complex; in this case, a larger adjustment value is set, and vice versa.

[0038] Next, the overall monitoring coverage rate of each camera belonging to the city's monitoring system within the monitored area is obtained. Here, the overall monitoring coverage rate refers to the ratio of the actual coverage area of ​​each camera in the city's monitoring system to the area of ​​the monitored area. Clearly, a higher overall monitoring coverage rate means fewer blind spots within the monitored area, and vice versa. Therefore, this invention sets up a method to determine a first percentage based on the overall monitoring coverage rate, and multiplies the fourth number by this first percentage to obtain the first number of cameras in the city's monitoring system that need to be accessed. Correspondingly, multiplying the fourth number by the second percentage, i.e., (1 - the first percentage), yields the second number of cameras in the non-city monitoring system that need to be accessed.

[0039] Therefore, the above-mentioned solution of the present invention determines the proportion of second and third cameras to be deployed based on the overall monitoring coverage of the urban monitoring system in the monitored area.

[0040] It should be noted that since accessing cameras in urban surveillance systems is significantly easier than accessing cameras in shops, shopping malls, or vehicles (where there are issues like refusal to access or vehicles leaving the monitored area), when overall surveillance coverage is low, priority is given to accessing more third-camera footage. This also allows more time to communicate with the devices carrying the third cameras, increasing the success rate of communication. Conversely, when overall surveillance coverage is high, priority is given to accessing more second-camera footage. An example of comparing overall surveillance coverage with the first and second percentages is shown in the table below:

[0041] It should be noted that the above comparison example table is only for illustrative purposes and is not intended to limit the comparison relationship between the overall monitoring coverage rate and the first percentage and the second percentage in this invention to the above-described pattern.

[0042] As an example, the calculation of the third number of cameras used for collaborative analysis based on the first anomaly analysis probability includes: ; In the formula, The third number of cameras used for collaborative analysis. The probability of the first anomaly analysis. This is the average of the first anomaly analysis probabilities for each monitoring type corresponding to the preset abnormal data. The median value of the first anomaly analysis probability in each monitoring type corresponding to the preset abnormal data; A preset number related to the number of cameras used for collaborative analysis; For weight values, .

[0043] In this embodiment, based on the above calculation formula, when calculating the total number of cameras used for collaborative analysis, i.e., the third number, the present invention comprehensively considers the general situation of the first anomaly analysis probability and the monitoring type corresponding to the preset anomaly data, which is represented by the sum of the average and median values ​​of the first anomaly analysis probabilities. This general situation can be considered as a fixed value. Therefore, the third number is negatively correlated with the first anomaly analysis probability. Since the foregoing has already provided a detailed analysis, it will not be repeated here.

[0044] As an example, the method further includes: obtaining carrier information of each of the third cameras that are not selected from the urban monitoring system; performing a handshake connection with the corresponding carrier based on the carrier information; and retrieving the third video data captured by the corresponding third camera after the handshake connection is successful.

[0045] In this embodiment, after identifying the third cameras that do not belong to the city's surveillance system, the carrier information of these cameras is further obtained. The carriers are, for example, vehicles, businesses, or shopping malls, and the corresponding carrier information is the vehicle, business, or shopping mall's encoding information. Based on this carrier information, a connection can be established with the corresponding carrier. Specifically, a handshake signal is sent to these carriers, and these carriers respond to the handshake signal, thereby establishing a communication connection with them. Then, the third video data captured by the corresponding third camera is retrieved.

[0046] In addition, regarding the connection with each second camera belonging to the city's surveillance system, if the second camera and the management personnel belong to the same system, they can be directly dispatched; if the second camera and the management personnel belong to different systems, the second video data can be retrieved in the same way as the connection with the third camera described above, and the specifics will not be elaborated further.

[0047] The present invention also discloses an electronic device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program, when executed by the processor, implements the method steps as described in any of the preceding claims.

[0048] The present invention also discloses a computer storage medium storing a computer program that can be executed by a processor to implement the method steps used by the processing apparatus as described in any of the preceding claims.

[0049] The present invention also discloses a computer program product comprising a computer program executable by a processor to implement the method steps implemented by the processing apparatus as described in any of the preceding claims.

[0050] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0051] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data early warning method integrating multiple devices and terminals, characterized in that, The method includes the following steps: receiving first video data captured by a first camera of an urban surveillance system; analyzing whether there is preset abnormal data in the first video data to obtain a first anomaly analysis probability; wherein, the abnormal data refers to abnormal objects or abnormal behaviors; determining a first number and a second number based on the first anomaly analysis probability; within a preset area where the single camera capturing the first video data is located, filtering out the first number of second cameras belonging to the urban surveillance system and filtering out the second number of third cameras not belonging to the urban surveillance system; performing a second analysis on the preset abnormal data based on the first video data, the second video data captured by each of the second cameras, and the third video data captured by each of the third cameras to obtain a second anomaly analysis probability; if the second anomaly analysis probability is higher than an anomaly threshold, outputting an alarm message; determining the first number and the second number based on the first anomaly analysis probability includes: root A third number of cameras used for collaborative analysis is calculated based on the first anomaly analysis probability, and the third number is negatively correlated with the first anomaly analysis probability. Furthermore, the motion intensity of preset anomaly data is extracted from the first video data, and an adjustment value is determined based on the motion intensity, wherein the adjustment value is greater than or equal to 1. A higher motion intensity indicates that the object involved in the anomaly behavior is less likely to have its key information captured, or that the behavior is more complex, thus a larger adjustment value is set; conversely, a lower motion intensity indicates a smaller adjustment value is set. A fourth number is calculated based on the third number and the adjustment value. The overall monitoring coverage rate of each camera belonging to the urban monitoring system within the monitoring area is obtained, and a first percentage is determined based on the overall monitoring coverage rate. A second percentage is further calculated as 1 - the first percentage. The fourth number is multiplied by the first percentage to obtain the first number, and the fourth number is multiplied by the second percentage to obtain the second number.

2. The data early warning method integrating multiple devices and terminals according to claim 1, characterized in that: Receiving first video data captured by a first camera of an urban surveillance system includes: determining the selection information of a management personnel for a certain monitoring area; determining, based on the selection information, a number of cameras belonging to the urban surveillance system located in the monitoring area; determining, based on the monitoring type corresponding to the selection information, a number of monitoring sub-areas; and determining, based on the camera with the largest coverage angle in each of the monitoring sub-areas, the first camera; and receiving the first video data captured by each of the first cameras.

3. The data early warning method integrating multiple devices and terminals according to claim 1, characterized in that: Receiving first video data captured by a first camera of an urban surveillance system includes: determining the selection information of a management personnel for a certain monitoring area; determining, based on the selection information, a number of cameras belonging to the urban surveillance system located in the monitoring area; determining, based on the monitoring type corresponding to the selection information, a number of monitoring sub-areas; and determining, based on the camera with the largest coverage angle in each of the monitoring sub-areas, the first camera; and receiving the first video data captured by each of the first cameras.

4. The data early warning method integrating multiple devices and terminals according to claim 1, characterized in that: The third number of cameras used for collaborative analysis is calculated based on the first anomaly analysis probability, including: In the formula, The third number of cameras used for collaborative analysis. The probability of the first anomaly analysis. This is the average of the first anomaly analysis probabilities for each monitoring type corresponding to the preset abnormal data. The median value of the first anomaly analysis probability in each monitoring type corresponding to the preset abnormal data; , A preset number related to the number of cameras used for collaborative analysis; , For weight values, .

5. The data early warning method integrating multiple devices and terminals according to claim 4, characterized in that: The method further includes: obtaining carrier information of each of the third cameras that are not selected from the urban monitoring system; performing a handshake connection with the corresponding carrier based on the carrier information; and retrieving the third video data captured by the corresponding third camera after the handshake connection is successful.

6. The data early warning method integrating multiple devices and terminals according to claim 4, characterized in that: The abnormal object analysis model and the abnormal behavior analysis model are constructed based on generative adversarial networks.

7. An electronic device, characterized in that: The electronic device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the computer program, when executed by the processor, implements the steps of the method as claimed in any one of claims 1-6.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program that can be executed by a processor to implement the steps of the method as described in any one of claims 1-6.

9. A computer program product, characterized in that: The computer program product includes a computer program that can be executed by a processor to perform the steps of the method as described in any one of claims 1-6.

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