Intelligent electric meter data transmission method and system and storage medium

Abstract

The invention discloses an intelligent electric meter data transmission method and system and a storage medium, and relates to the technical field of intelligent electric meters. The method comprises the following steps that an analysis unit obtains an image in an elevator through an elevator vision module, executes a user recognition program to obtain user head portraits, presets a similar threshold value, and sends the similar threshold value to an elevator through the elevator vision module, and the user head portraits comprise the left face user head portraits, the right face user head portraits and the front face user head portraits; the analysis unit compares the head portrait of the user with the face image stored in the database to calculate the similarity Si, and if the similarity between the head portrait of the user and the face image is smaller than a similarity threshold value, the visual unit is used for conducting recognition statistics on each person using the elevator, then the relevance between each person and the portal is calculated, and the correlation between each person and the portal is calculated. According to the elevator telegram charging system, foreign personnel can be effectively brought into the elevator telegram charging system, the apportionment index can directly reflect the use efficiency of the elevator, and the apportionment of the electric charge of the elevator is more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of smart meters, and in particular to a smart meter data transmission method, system and storage medium. Background Art

[0002] With the increasing number of high-rise buildings, the frequency of use and energy consumption of elevators are also increasing. Residents on different floors have different degrees of elevator use. If a simple average sharing method is adopted, it is obviously unfair to residents on lower floors or who use the elevator less frequently. How to reasonably share the elevator meter fees has always been a technically challenging problem.

[0003] Patent A [Publication No.: CN112707259B] provides an elevator billing device and a billing method thereof. The device calculates the amount of electricity required to be borne by passengers according to the weight of the passengers and the floor they leave, thereby calculating the electricity charges required to be borne. However, it is not reasonable to calculate the elevator based solely on the weight of the passengers and the floor they arrive at. The efficiency of the elevator is not taken into account. If a single user rides from the lowest floor to the highest floor, the load capacity of the elevator is not high but the vacancy rate of the elevator is high, and the electricity consumption of the elevator is also huge. The electricity billing of Patent A for this situation is low and cannot reasonably allocate the electricity charges. In addition, the billing method of Patent A is only for users within the community. The electricity gap caused by outsiders such as couriers, takeouts and visitors cannot be included in the statistics. Therefore, adding the elevator efficiency factor and the outsider factor to the elevator meter billing is a technical problem that technical personnel in this field need to solve. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a smart meter data transmission method, system and storage medium, which solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A smart meter data transmission method is implemented based on a user meter, an elevator meter, an analysis unit, an elevator visual module, a portal visual module and a load module, comprising the following steps:

[0006] Step 1: Analyze the user avatar. The analysis unit obtains the image in the elevator through the elevator vision module. The analysis unit executes the user recognition program to obtain the user avatar. The user avatar includes the left-face user avatar, the right-face user avatar and the front-face user avatar. The analysis unit presets a similarity threshold of 90%. The analysis unit compares the user avatar with the face image stored in the database to calculate the similarity Si. If the similarity between the user avatar and the face image is less than the similarity threshold of 90%, the analysis unit marks the user avatar less than the similarity threshold of 90% as a new face image and assigns a user ID. The user ID is unique and corresponds to the face image one by one. The analysis unit binds the newly marked face image and the corresponding user ID and saves it in the database. If the similarity Si between the user avatar and the face image is greater than or equal to the similarity threshold of 90%, the analysis unit classifies the user avatar into the face image. Each time the analysis unit calculates the similarity Si between the user avatar and the face image, it accumulates the number of appearances Nu of the face image in the image in the elevator.

[0007] The analysis unit presets a recording period of 7 days. If the number of times Nu of a face image appears in the recording period is 0, the analysis unit deletes the face image with the number of times Nu appearing is 0 and the corresponding user ID from the database, and recycles the deleted user ID, and assigns it to the user portrait with a similarity Si lower than the similarity threshold when the user recognition program is executed next time;

[0008] Step 2: Calculate the user usage index. The analysis unit obtains the load g and the number of floors f of the elevator through the load module. The analysis unit executes the elevator usage analysis program to obtain the user usage index Use corresponding to each user ID. The visual unit identifies and counts each person using the elevator, and then calculates the correlation between each person and the portal. This can effectively include outsiders in the elevator telegram billing system, making the elevator's electricity bill more reasonable.

[0009] Step 3: Matching association, the analysis unit numbers the portals on each floor, executes a user identification program to obtain a user portrait, records the number of occurrences Mn and duration Mt of each user ID in the image of the portal visual module, and executes an association analysis program to obtain an association index Rel between the user ID and each numbered portal;

[0010] Step 4: The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portals with different numbers. When the association index Rel between the user ID and the portal is not zero, the analysis unit marks the user ID as the associated target of the portal. The analysis unit accumulates the allocation indexes of all associated targets of the portal to obtain the portal index. The analysis unit adds up all portal indices to obtain the total power consumption index.

[0011] Step 5: The analysis unit obtains the electricity consumption of the elevator from the elevator electricity meter. The analysis unit multiplies the electricity consumption of the elevator by the portal index of each portal and then divides it by the total electricity consumption index to obtain the shared electricity consumption of each portal. The analysis unit sends the calculated shared electricity consumption to the user electricity meter corresponding to the portal, so that the electricity consumption of the elevator can be more reasonably allocated to the user electricity meter of each portal to calculate the electricity bill.

[0012] Furthermore, the user identification procedure specifically includes the following steps:

[0013] The database contains three face models with different viewing angles: left face at a downward angle of 45°, front face at a downward angle of 45°, and right face at a downward angle of 45°.

[0014] Step 11: The analysis unit measures the length of the elevator image as L1 and the width of the elevator image as L2. The analysis unit establishes a coordinate system in the elevator image using pixels as the measurement unit, and the intersection of the diagonal lines of the elevator image rectangle is the origin of the coordinate system.

[0015] Step 12: The analysis unit establishes a vortex line in the coordinate system. The analysis unit presets the number of turns of the vortex line as a, and the value range of a is (3<a<20). The initial diameter of the inner circle of the vortex line is preset as b, and the value range of b is The expression formula of vortex line is t is a pixel point on the vortex line. The analysis unit presets a moving window, which is an ellipse. The vertical major axis of the moving window is The horizontal minor axis of the moving window is The analysis unit randomly selects values ​​from a value range according to a combination formula for the number of circles a and the initial diameter b of the inner circle to generate a vortex line. Each time the analysis unit generates a vortex line, step 121 is executed once until all the combination methods of the combination formula are used up. When all the combination methods are used up, the process jumps to step 13.

[0016] Step 121: the analysis unit presets a first trigger threshold of 50%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel on the spiral line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces of the three viewing angles. The pixels in the moving window are the pixels circled by the moving window in the image in the elevator. When the similarity Si is greater than or equal to the first trigger threshold, the analysis unit records a trigger event. When the moving window moves to the end of the outer circle of the spiral line, the analysis unit counts the number of trigger events corresponding to the spiral line.

[0017] Step 13: The analysis unit selects the vortex line with the most trigger events and marks it as a trajectory line. The analysis unit presets a second trigger threshold of 90%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel on the trajectory line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels of the elevator image in the moving window and the model faces of the three viewing angles. When any one of the similarities Si of the model faces of the three viewing angles is greater than or equal to the second trigger threshold, the analysis unit marks the pixels of the elevator image in the moving window as the user avatar.

[0018] Step 14: The analysis unit selects the maximum values ​​of the three model face similarities Si to classify the user avatars, namely left-face user avatars, right-face user avatars and front-face user avatars. The pixel with the maximum value of the similarity Si is marked as the corresponding user avatar type.

[0019] Furthermore, the similarity Si is calculated by the following steps:

[0020] Step 15: The analysis unit converts both the user portrait and the face image into grayscale images, with the grayscale value ranging from 0 to 255. The analysis unit marks the grayscale image of the user portrait as x and the grayscale image of the face image as y.

[0021] Step 16: The analysis unit calculates the pixel mean ux of the grayscale image x, calculates the pixel mean uy of the grayscale image y, and calculates the variance σx of the grayscale image x. 2 , calculate the variance σy of the grayscale image y 2 , calculate the covariance σxy of the grayscale image x and the grayscale image y;

[0022] Step 17: Analyze the cells according to the formula Calculate the similarity index SSIM between the user's avatar and the face image, where C1 = (K1 × E) 2 , C2=(K2×E) 2 , C1 and C2 are constants set to avoid the denominator being zero, E is the pixel value random coefficient, the value range of E is (0-255), K1 and K2 are constants, K1 is 0.01, K2 is 0.03;

[0023] The calculation of similarity Si is affected by three factors: brightness d1, contrast d2, and structure d3. Therefore, it is necessary to comprehensively analyze the similarity index SSIM of the user's head portrait and face image from brightness d1, contrast d2, and structure d3. The calculation formula of brightness d1 is: The calculation formula of contrast d2 is: The calculation formula of structure d3 is

[0024] Step 18: The value range of the similarity index SSIM after calculation is (-1, 1), and the analysis unit is based on the formula The similarity index SSIM is normalized to obtain the similarity Si.

[0025] Furthermore, the elevator usage analysis program specifically includes the following steps:

[0026] Step 21: The analysis unit establishes a load curve graph, the horizontal axis of the load curve graph is time, the time unit is seconds, the vertical axis is weight, the weight unit is kilograms, the analysis unit obtains the load weight g from the load module and inputs it into the load curve graph to generate a load curve, the analysis unit determines the increase or decrease of users according to the change of the load curve, when the load curve rises in a step-by-step manner, it is determined that users are added in the elevator, when the load curve drops in a step-by-step manner, it is determined that users are reduced in the elevator, when the number of users in the elevator increases or decreases, the analysis unit executes a user analysis process to obtain the floor index Fu corresponding to each user ID, the increase or decrease of users in the elevator means that the elevator stops at one of the floors, and the floor index Fu at this time is a factor affecting the user use index Use of each user ID, and the user analysis process is not executed for the floor where the elevator does not stop, and the floor index Fu of the floor will not be calculated accordingly;

[0027] Step 22: The analysis unit defines each round trip of the elevator from the lowest floor to the highest floor as a lifting cycle, and the analysis unit adds the floor index Fu of each user ID in a lifting cycle to obtain the lifting index Sj corresponding to each user ID;

[0028] Step 23: The analysis unit adds the lifting index Sj of each user ID in each recording period to obtain the user usage index Use corresponding to each user ID. The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portals with different numbers. By identifying the number of users in the elevator, the allocation index of each user is calculated. On the basis of considering the load capacity and the number of floors, the allocation index can directly reflect the use efficiency of the elevator, making the elevator electricity meter billing more reasonable.

[0029] The user analysis process specifically includes the following steps:

[0030] Step 221: The analysis unit presets a condition coefficient λ, the value range of λ is 0-1, and the analysis unit presets adjustment coefficients h, w1, w2, w3 and w4, and the specific value is determined according to the actual specifications of the elevator, and the value range of h is 2-3;

[0031] Step 222: The analysis unit presets a load weight coefficient wg and a floor weight coefficient wf, and obtains the total number of floors and the maximum load value of the elevator from the load module. The analysis unit divides the maximum load value of the elevator by the load g to obtain a specific value of the load weight coefficient wg, and divides the number of floors f by the total number of floors to obtain a specific value of the floor weight coefficient wf;

[0032] Step 223: Analyze the unit according to the formula The floor index Fu corresponding to each user ID is calculated.

[0033] Furthermore, the correlation analysis procedure specifically includes the following steps:

[0034] Step 31: The analysis unit presets a frequency ratio coefficient wn and a duration ratio coefficient wt, the value range of the frequency ratio coefficient wn and the duration ratio coefficient wt are both 0 to 1, and the specific values ​​are determined according to the number of doors in a floor. The analysis unit marks the maximum value in the number of occurrences Mn as Max(Mn), and marks the maximum value in the duration Mt as Max(Mt);

[0035] Step 32: Analyze the cells according to the formula The correlation index Rel between each user ID and each numbered portal is calculated, the value of the time proportional coefficient wt is 0.3, and the value of the duration proportional coefficient wt is 0.1.

[0036] A smart meter data transmission system, comprising a user meter, an elevator meter, an analysis unit, a visual unit and a load module, wherein the visual unit comprises an elevator visual module and a portal visual module, the output end of the elevator meter is connected to the input end of the analysis unit, the output end of the analysis unit is connected to the input end of the user meter, and the output ends of the elevator visual module, the portal visual module and the load module are all connected to the input end of the analysis unit;

[0037] The elevator vision module is installed on the top surface of the elevator interior, and the image acquisition direction of the elevator vision module faces away from the elevator door and is tilted 45° downward. The portal vision module is installed on the top of the building aisle, and the image acquisition direction of the portal vision module faces the portal and is tilted 45° downward. The elevator vision module acquires the image inside the elevator and transmits it to the analysis unit, and the portal vision module acquires the image of the aisle in front of the portal and transmits it to the analysis unit. The load module is installed at the bottom of the elevator, and the load module acquires the load g and the number of floors f of the elevator and transmits them to the analysis unit;

[0038] The elevator electricity meter records the electricity consumption of the elevator and transmits it to the analysis unit, and the user electricity meter is used to receive the shared electricity consumption sent by the analysis unit.

[0039] A smart meter storage medium includes a database, a port of the database establishes data communication with a port of an analysis unit, a computer program or instruction is stored in the database, and when the computer program or instruction is executed, a method as described in any one of the contents is implemented.

[0040] The present invention has the following beneficial effects:

[0041] 1. By identifying and counting each person using the elevator through the visual unit, and then calculating the correlation between each person and the portal, outsiders can be effectively included in the elevator telegram billing system, making the elevator's electricity bill more reasonable.

[0042] 2. By identifying the number of users in the elevator, the allocation index of each user is calculated. Based on the load capacity and the number of floors, the allocation index can directly reflect the efficiency of the elevator, making the elevator electricity meter billing more reasonable.

[0043] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 This is a block diagram of a smart meter data transmission system of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The present invention provides a technical solution: a smart meter data transmission method, which is implemented based on a user meter, an elevator meter, an analysis unit, an elevator visual module, a portal visual module and a load module, and includes the following steps:

[0048] Step 1: Analyze the user avatar. The analysis unit obtains the image in the elevator through the elevator vision module. The analysis unit executes the user recognition program to obtain the user avatar. The user avatar includes the left-face user avatar, the right-face user avatar and the front-face user avatar. The analysis unit presets a similarity threshold of 90%. The analysis unit compares the user avatar with the face image stored in the database to calculate the similarity Si. If the similarity between the user avatar and the face image is less than the similarity threshold of 90%, the analysis unit marks the user avatar less than the similarity threshold of 90% as a new face image and assigns a user ID. The user ID is unique and corresponds to the face image one by one. The analysis unit binds the newly marked face image and the corresponding user ID and saves it in the database. If the similarity Si between the user avatar and the face image is greater than or equal to the similarity threshold of 90%, the analysis unit classifies the user avatar into the face image. Each time the analysis unit calculates the similarity Si between the user avatar and the face image, it accumulates the number of appearances Nu of the face image in the image in the elevator.

[0049] The analysis unit presets a recording period of 7 days. If the number of times Nu of a face image appears in the recording period is 0, the analysis unit deletes the face image with the number of times Nu appearing is 0 and the corresponding user ID from the database to prevent the user head portrait with too low frequency from occupying the storage resources of the database. The analysis unit recycles the deleted user ID and assigns it to the user head portrait with similarity Si lower than the similarity threshold when the user recognition program is executed next time.

[0050] Step 2: Calculate the user usage index. The analysis unit obtains the load g and the number of floors f of the elevator through the load module. The analysis unit executes the elevator usage analysis program to obtain the user usage index Use corresponding to each user ID;

[0051] Step 3: Matching association, the analysis unit numbers the portals on each floor, executes a user identification program to obtain a user portrait, records the number of occurrences Mn and duration Mt of each user ID in the image of the portal visual module, and executes an association analysis program to obtain the association index Rel between the user ID and each numbered portal;

[0052] Step 4: The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portals with different numbers. When the association index Rel between the user ID and the portal is not zero, the analysis unit marks the user ID as the associated target of the portal. The analysis unit accumulates the allocation indexes of all associated targets of the portal to obtain the portal index. The analysis unit adds up all portal indices to obtain the total power consumption index.

[0053] Step 5: The analysis unit obtains the electricity consumption of the elevator from the elevator electricity meter. The analysis unit multiplies the electricity consumption of the elevator by the portal index of each portal and then divides it by the total electricity consumption index to obtain the shared electricity consumption of each portal. The analysis unit sends the calculated shared electricity consumption to the user electricity meter corresponding to the portal, so that the electricity consumption of the elevator can be more reasonably allocated to the user electricity meter of each portal to calculate the electricity bill.

[0054] The user identification procedure specifically includes the following steps:

[0055] The database contains three face models with different viewing angles: left face at a downward angle of 45°, front face at a downward angle of 45°, and right face at a downward angle of 45°.

[0056] Step 11: The analysis unit measures the length of the elevator image as L1 and the width of the elevator image as L2. The analysis unit establishes a coordinate system in the elevator image using pixels as the measurement unit, and the intersection of the diagonal lines of the elevator image rectangle is the origin of the coordinate system.

[0057] Step 12: The analysis unit establishes a vortex line in the coordinate system. The analysis unit presets the number of turns of the vortex line as a, and the value range of a is (3<a<20). The initial diameter of the inner circle of the vortex line is preset as b, and the value range of b is The expression formula of vortex line is t is a pixel point on the vortex line. The analysis unit presets a moving window, which is an ellipse. The vertical major axis of the moving window is The horizontal minor axis of the moving window is The analysis unit randomly selects values ​​from a value range according to a combination formula for the number of circles a and the initial diameter b of the inner circle to generate a vortex line. Each time the analysis unit generates a vortex line, step 121 is executed once until all the combination methods of the combination formula are used up. When all the combination methods are used up, the process jumps to step 13.

[0058] There is no order in which the combination formula takes values. The average aspect ratio of a face is 34:21. In order to increase the chance of successful matching between the moving window and the model face, the moving window is set to an ellipse close to the aspect ratio of the face, which can improve the recognition error tolerance.

[0059] Step 121: the analysis unit presets a first trigger threshold of 50%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel on the spiral line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces of the three viewing angles. The pixels in the moving window are the pixels circled by the moving window in the image in the elevator. When the similarity Si is greater than or equal to the first trigger threshold, the analysis unit records a trigger event. When the moving window moves to the end of the outer circle of the spiral line, the analysis unit counts the number of trigger events corresponding to the spiral line.

[0060] Step 13: The analysis unit selects the vortex line with the most trigger events and marks it as a trajectory line. The analysis unit presets a second trigger threshold of 90%. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel on the trajectory line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels of the elevator image in the moving window and the model faces of the three viewing angles. When any one of the similarities Si of the model faces of the three viewing angles is greater than or equal to the second trigger threshold, the analysis unit marks the pixels of the elevator image in the moving window as the user avatar.

[0061] Step 14: The analysis unit selects the maximum values ​​of the three model face similarities Si to classify the user avatars, namely left-face user avatars, right-face user avatars and front-face user avatars. The pixel with the maximum value of the similarity Si is marked as the corresponding user avatar type.

[0062] The similarity Si is calculated by the following steps:

[0063] Step 15: The analysis unit converts both the user portrait and the face image into grayscale images, with the grayscale value ranging from 0 to 255. The analysis unit marks the grayscale image of the user portrait as x and the grayscale image of the face image as y.

[0064] Step 16: The analysis unit calculates the pixel mean ux of the grayscale image x, calculates the pixel mean uy of the grayscale image y, and calculates the variance σx of the grayscale image x. 2 , calculate the variance σy of the grayscale image y 2 , calculate the covariance σxy of the grayscale image x and the grayscale image y;

[0065] Step 17: Analyze the cells according to the formula Calculate the similarity index SSIM between the user's avatar and the face image, where C1 = (K1 × E) 2 , C2=(K2×E) 2 , C1 and C2 are constants set to avoid the denominator being zero, E is the pixel value random coefficient, the value range of E is (0-255), K1 and K2 are constants, K1 is 0.01, K2 is 0.03;

[0066] The calculation of similarity Si is affected by three factors: brightness d1, contrast d2, and structure d3. Therefore, it is necessary to comprehensively analyze the similarity index SSIM of the user's head portrait and face image from brightness d1, contrast d2, and structure d3. The calculation formula of brightness d1 is: The calculation formula of contrast d2 is: The calculation formula of structure d3 is

[0067] Step 18: The value range of the similarity index SSIM after calculation is (-1, 1), and the analysis unit is based on the formula The similarity index SSIM is normalized to obtain the similarity Si.

[0068] The elevator usage analysis program specifically includes the following steps:

[0069] Step 21: The analysis unit establishes a load curve graph, the horizontal axis of the load curve graph is time, the time unit is seconds, the vertical axis is weight, the weight unit is kilograms, the analysis unit obtains the load weight g from the load module and inputs it into the load curve graph to generate a load curve, the analysis unit determines the increase or decrease of users according to the change of the load curve, when the load curve rises in a step-by-step manner, it is determined that users are added in the elevator, when the load curve drops in a step-by-step manner, it is determined that users are reduced in the elevator, when the number of users in the elevator increases or decreases, the analysis unit executes a user analysis process to obtain the floor index Fu corresponding to each user ID, the increase or decrease of users in the elevator means that the elevator stops at one of the floors, and the floor index Fu at this time is a factor affecting the user use index Use of each user ID, and the user analysis process is not executed for the floor where the elevator does not stop, and the floor index Fu of the floor will not be calculated accordingly;

[0070] Step 22: The analysis unit defines each round trip of the elevator from the lowest floor to the highest floor as a lifting cycle, and the analysis unit adds the floor index Fu of each user ID in a lifting cycle to obtain the lifting index Sj corresponding to each user ID;

[0071] Step 23: The analysis unit adds the rise and fall index Sj of each user ID in each recording period to obtain the user use index Use corresponding to each user ID;

[0072] The user analysis process specifically includes the following steps:

[0073] Step 221: The analysis unit presets a condition coefficient λ, the value range of λ is 0-1, and the analysis unit presets adjustment coefficients h, w1, w2, w3 and w4, and the specific value is determined according to the actual specifications of the elevator, and the value range of h is 2-3;

[0074] Step 222: The analysis unit presets a load weight coefficient wg and a floor weight coefficient wf, and obtains the total number of floors and the maximum load value of the elevator from the load module. The analysis unit divides the maximum load value of the elevator by the load g to obtain a specific value of the load weight coefficient wg, and divides the number of floors f by the total number of floors to obtain a specific value of the floor weight coefficient wf;

[0075] Step 223: Analyze the unit according to the formula The floor index Fu corresponding to each user ID is calculated, where h is 2, w1 is 10, w2 is 50, w3 is 100, w4 is 0.1, and λ is 0.8.

[0076] The correlation analysis procedure specifically includes the following steps:

[0077] Step 31: The analysis unit presets a frequency ratio coefficient wn and a duration ratio coefficient wt, the value range of the frequency ratio coefficient wn and the duration ratio coefficient wt are both 0 to 1, and the specific values ​​are determined according to the number of doors in a floor. The analysis unit marks the maximum value in the number of occurrences Mn as Max(Mn), and marks the maximum value in the duration Mt as Max(Mt);

[0078] Step 32: Analyze the cells according to the formula The correlation index Rel between each user ID and each numbered portal is calculated, the value of the time proportional coefficient wt is 0.3, and the value of the duration proportional coefficient wt is 0.1.

[0079] like Figure 1 As shown, a smart meter data transmission system includes a user meter, an elevator meter, an analysis unit, a visual unit and a load module, the visual unit includes an elevator visual module and a portal visual module, the output end of the elevator meter is connected to the input end of the analysis unit, the output end of the analysis unit is connected to the input end of the user meter, and the output ends of the elevator visual module, the portal visual module and the load module are all connected to the input end of the analysis unit;

[0080] The elevator vision module is installed on the top surface of the elevator. The image acquisition direction of the elevator vision module faces away from the elevator door and is tilted 45° downward. The portal vision module is installed on the top of the building aisle. The image acquisition direction of the portal vision module faces the portal and is tilted 45° downward. The elevator vision module acquires the image inside the elevator and transmits it to the analysis unit. The portal vision module acquires the image of the aisle in front of the portal and transmits it to the analysis unit. The load module is installed at the bottom of the elevator. The load module acquires the load g and the number of floors f of the elevator and transmits them to the analysis unit.

[0081] The elevator meter records the elevator's electricity consumption and transmits it to the analysis unit, and the user meter is used to receive the shared electricity sent by the analysis unit.

[0082] A smart meter storage medium includes a database, a port of the database establishes data communication with a port of an analysis unit, a computer program or instruction is stored in the database, and when the computer program or instruction is executed, any method in the specific implementation manner is implemented.

[0083] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart meter data transmission method, based on a user meter, an elevator meter, an analysis unit, an elevator visual module, a portal visual module and a load module, Features: The following steps are involved: Step 1: Analyze user avatars; Step 2: Calculate the user usage index. The analysis unit obtains the load g and the number of floors f of the elevator through the load module, and executes the elevator usage analysis program to obtain the user usage index Use corresponding to each user ID; Step 3: Matching relevance, the analysis unit executes the user identification program to obtain the user avatar, records the number of occurrences Mn and duration Mt of each user ID in the image of the portal visual module, and executes the relevance analysis program to obtain the relevance index Rel between the user ID and each numbered portal; Step 4: The analysis unit multiplies the user usage index Use of each user ID by the association index Rel of each numbered portal to obtain the allocation index of each user ID and the portals with different numbers. When the association index Rel between the user ID and the portal is not zero, the user ID is marked as the associated target of the portal, and the allocation indexes of all associated targets of the portal are accumulated to obtain the portal index, and all portal indices are added to obtain the total power consumption index; Step 5: The analysis unit obtains the elevator's electricity consumption from the elevator meter, multiplies the elevator's electricity consumption by the portal index of each portal and then divides it by the total electricity consumption index to obtain the shared electricity consumption of each portal, and sends the calculated shared electricity consumption to the user meter corresponding to the portal.

2. A smart meter data transmission method according to claim 1, It is characterized in that The specific process of analyzing the user's avatar in step 1 is as follows: The analysis unit obtains the image inside the elevator through the elevator vision module, executes the user recognition program to obtain the user avatar, presets a similarity threshold, compares the user avatar with the face image stored in the database to calculate the similarity Si, if the similarity between the user avatar and the face image is less than the similarity threshold, the user avatar less than the similarity threshold is marked as a new face image and assigned a user ID, the user ID is unique and corresponds to the face image one by one, and the newly marked face image is bound to the corresponding user ID; if the similarity Si between the user avatar and the face image is greater than or equal to the similarity threshold, the user avatar is classified into the face image; each time the similarity Si between the user avatar and the face image is calculated, the number of appearances Nu of the face image is accumulated.

3. A smart meter data transmission method according to claim 2, It is characterized in that The user identification procedure specifically includes the following steps: The database pre-stores model faces from three perspectives, namely, the downward slanting left face, the downward slanting front face, and the downward slanting right face; Step 11: The analysis unit measures the length of the elevator image as L1 and the width of the elevator image as L2. The analysis unit establishes a coordinate system in the elevator image using pixels as the measurement unit, and the intersection of the diagonal lines of the elevator image rectangle is the origin of the coordinate system. Step 12: The analysis unit establishes a vortex line in the coordinate system. The analysis unit presets the number of turns of the vortex line as a, and the value range of a is (3<a<20). The initial diameter of the inner circle of the vortex line is preset as b, and the value range of b is The expression formula of vortex line is t is a pixel point on the vortex line. The analysis unit presets a moving window, which is an ellipse. The vertical major axis of the moving window is The horizontal minor axis of the moving window is The analysis unit randomly selects values ​​from a value range according to a combination formula for the number of circles a and the initial diameter b of the inner circle to generate a vortex line. Each time the analysis unit generates a vortex line, step 121 is executed once until all the combination methods of the combination formula are used up. When all the combination methods are used up, the process jumps to step 13. Step 121: The analysis unit presets a first trigger threshold, and the analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel on the spiral line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces of the three viewing angles. When the similarity Si is greater than or equal to the first trigger threshold, the analysis unit records a trigger event. When the moving window moves to the end of the outer circle of the spiral line, the analysis unit counts the number of trigger events corresponding to the spiral line. Step 13: The analysis unit selects the vortex line with the most trigger events and marks it as a trajectory line. The analysis unit presets a second trigger threshold. The analysis unit moves the intersection of the vertical major axis and the horizontal minor axis of the moving window from the inside to the outside pixel by pixel on the trajectory line. Each time the moving window moves, the analysis unit calculates the similarity Si between the pixels in the moving window and the model faces of the three perspectives. When any one of the similarities Si of the model faces of the three perspectives is greater than or equal to the second trigger threshold, the analysis unit marks the pixels in the moving window as the user's avatar. Step 14: The analysis unit selects the maximum values ​​of the three model face similarities Si to classify the user avatars, namely left-face user avatars, right-face user avatars and front-face user avatars. The pixel with the maximum value of the similarity Si is marked as the corresponding user avatar type.

4. A smart meter data transmission method according to claim 3, It is characterized in that The similarity Si is calculated by the following steps: Step 15: The analysis unit converts both the user portrait and the face image into grayscale images, with the grayscale value ranging from 0 to 255. The analysis unit marks the grayscale image of the user portrait as x and the grayscale image of the face image as y. Step 16: The analysis unit calculates the pixel mean ux of the grayscale image x, calculates the pixel mean uy of the grayscale image y, and calculates the variance σx of the grayscale image x. 2 , calculate the variance σy of the grayscale image y 2 , calculate the covariance σxy of the grayscale image x and the grayscale image y; Step 17: Analyze the cells according to the formula Calculate the similarity index SSIM between the user's avatar and the face image, where C1 = (K1 × E) 2 , C2=(K2×E) 2 , C1 and C2 are constants, E is the pixel value random coefficient, the value range of E is (0~255), K1 and K2 are constants; The calculation of similarity Si is affected by three factors: brightness d1, contrast d2, and structure d3. Therefore, it is necessary to comprehensively analyze the similarity index SSIM of the user's head portrait and face image from brightness d1, contrast d2, and structure d3. The calculation formula of brightness d1 is: The calculation formula of contrast d2 is: The calculation formula of structure d3 is Step 18: The value range of the similarity index SSIM after calculation is (-1, 1), and the analysis unit is based on the formula The similarity index SSIM is normalized to obtain the similarity Si.

5. A smart meter data transmission method according to claim 4, It is characterized in that The elevator usage analysis procedure specifically includes the following steps: Step 21: The analysis unit establishes a load curve graph, the horizontal axis of the load curve graph is time, and the vertical axis is weight. The analysis unit obtains the load weight g from the load module and inputs it into the load curve graph to generate a load curve. The analysis unit determines the increase or decrease of users according to the change of the load curve. When the load curve rises in a step-by-step manner, it is determined that a user has been added to the elevator. When the load curve drops in a step-by-step manner, it is determined that a user has been reduced in the elevator. When the number of users in the elevator increases or decreases, the analysis unit executes a user analysis process to obtain the floor index Fu corresponding to each user ID. Step 22: The analysis unit defines each round trip of the elevator from the lowest floor to the highest floor as a lifting cycle, and the analysis unit adds the floor index Fu of each user ID in a lifting cycle to obtain the lifting index Sj corresponding to each user ID; Step 23: The analysis unit adds the rise and fall index Sj of each user ID in each recording period to obtain the user use index Use corresponding to each user ID; The user analysis process specifically includes the following steps: Step 221: the analysis unit presets a condition coefficient λ, the value range of λ is 0 to 1, and the analysis unit presets adjustment coefficients h, w1, w2, w3 and w4; Step 222: The analysis unit presets a load weight coefficient wg and a floor weight coefficient wf, and obtains the total number of floors and the maximum load value of the elevator from the load module. The analysis unit divides the maximum load value of the elevator by the load g to obtain a specific value of the load weight coefficient wg, and divides the number of floors f by the total number of floors to obtain a specific value of the floor weight coefficient wf; Step 223: Analyze the unit according to the formula The floor index Fu corresponding to each user ID is calculated.

6. A smart meter data transmission method according to claim 5, It is characterized in that The correlation analysis procedure specifically includes the following steps: Step 31: the analysis unit presets a frequency ratio coefficient wn and a duration ratio coefficient wt, the value ranges of the frequency ratio coefficient wn and the duration ratio coefficient wt are both 0 to 1, and the analysis unit marks the maximum value in the number of occurrences Mn as Max(Mn), and marks the maximum value in the duration Mt as Max(Mt); Step 32: Analyze the cells according to the formula The correlation index Rel between each user ID and each numbered portal is calculated, the value of the time proportional coefficient wt is 0.3, and the value of the duration proportional coefficient wt is 0.

1.

7. A smart meter data transmission system, It is characterized in that A smart meter data transmission method applied to any one of claims 1 to 6, comprising a user meter, an elevator meter, an analysis unit, a visual unit and a load module, wherein the visual unit comprises an elevator visual module and a portal visual module, the output end of the elevator meter is connected to the input end of the analysis unit, the output end of the analysis unit is connected to the input end of the user meter, and the output ends of the elevator visual module, the portal visual module and the load module are all connected to the input end of the analysis unit; The elevator vision module is installed on the top surface of the elevator interior, and the image acquisition direction of the elevator vision module is facing away from the elevator door and diagonally downward. The portal vision module is installed on the top of the building aisle, and the image acquisition direction of the portal vision module is facing the portal and diagonally downward. The elevator vision module acquires the image inside the elevator and transmits it to the analysis unit, and the portal vision module acquires the image of the aisle in front of the portal and transmits it to the analysis unit. The load module is installed at the bottom of the elevator, and the load module acquires the load g and the number of floors f of the elevator and transmits them to the analysis unit; The elevator electricity meter records the electricity consumption of the elevator and transmits it to the analysis unit, and the user electricity meter is used to receive the shared electricity consumption sent by the analysis unit.

8. A smart meter storage medium, It is characterized in that It comprises a database, a port of the database establishes data communication with a port of the analysis unit, a computer program or instruction is stored in the database, and when the computer program or instruction is executed, the method as claimed in any one of claims 1 to 6 is implemented.

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