OPTIMIZED METHOD FOR COLLECTING DATA FROM COMMUNICATING METERS VIA A CELLULAR NETWORK AND SYSTEM FOR CARRYING OUT THE METHOD.

Abstract

The invention relates to a method for collecting, via a cellular communication network (1000), data available in a set of communicating meters, the method being executed in a data collection system (100) connected to said network (1000) and the method comprising: obtaining first information representative (S1) of the behavior of said meters; establishing parameters representative of the behavior (S2) of each of the communicating meters from said first information; establishing a collection schedule (S3) of data from all or part of said meters from said parameters representative of the behavior of each of the meters, and transmitting data collection messages (S4) to said meters for which said schedule has been established. The invention also relates to a data collection system (100) configured to execute the method. Figure to be published with the abstract: Fig. 3

Description

Title of the invention: OPTIMIZED METHOD FOR COLLECTING DATA FROM COMMUNICATING METERS VIA A CELLULAR NETWORK AND SYSTEM TO RUN THE PROCESS. Technical field

[0001] The present invention relates to a method for collecting data available in communicating meters, executed by a data collection system and operated via a communication network of the cellular network type. At least one embodiment of the invention relates to a method for collecting data from electricity, water or gas meters configured to communicate with a data concentrator via an IP network of a mobile telephone operator. STATE OF PRIOR ART

[0002] Recent electricity consumption meters are so-called "intelligent" and / or "communicating" electronic devices capable of generating and transmitting data to a server or a remote system operating data collection functions, for example a consumption management server, through various communication networks, in particular for pricing and information collection services related to electricity, water or gas consumption. Some of these meters are configured to transmit the data they have generated via a cellular type communication network, such as, for example, a network of a mobile telephone operator. Such a network consists of a set of communication cells in which each cell is covered by a transmitting and receiving station called a base station.In other words, the territory covered by the cellular communication network is divided into cells, each comprising a base station. Each of the cells comprises one or more communicating meters generating information useful for the management of one or more service or product supplies. It is then necessary to collect data from these meters, taking into account the communication capacity of each of the cells in the communication network. Current techniques for collecting information from meters are likely to cause congestion effects in the cellular communication network, especially since communicating meters are sometimes equipment put on standby between two processing or information collection operations, which implies that it is first necessary to control the meter to wake it up, then to collect the useful information. Others . meters are temporarily out of network range due to disruptions. When a data collection targeting a meter fails, it must be repeated until the data is collected, which implies a redundant use of network resources for the same data. There is therefore a need to optimize collection methods to obtain a large amount of data from a set of meters, according to imposed calendar constraints, while limiting the use of network resources that are also used for other purposes. The situation can be improved. Statement of the invention

[0003] The aim of the invention is to propose a method for collecting data from communicating meters, according to calendar constraints, while limiting the use of network resources.

[0004] To this end, a method is proposed for collecting, via a cellular type communication network, data available in a set of communicating meters, the method being executed in a data collection system connected to said network and the method comprising:

[0005] - i) obtaining first information representative of the behavior of said counters,

[0006] - ii) establish parameters representative of the behavior of each of the counters communicators from said first information,

[0007] - iii) establish a data collection schedule for all or part of said counters from said parameters representative of the behavior of each of the counters, and,

[0008] - iv) transmitting data collection messages to said meters according to said established order.

[0009] Advantageously, it is thus possible, thanks to learning the typical behavior of each of the communicating meters, to plan to carry out data collection from a meter only when it has the greatest probability of responding (and therefore of responding on the first attempt). The sharing of the cellular communication network is then carried out in a reasonable manner between the collection system and the other applications which use the network. Furthermore, such a method advantageously makes it possible to detect seasonal phenomena in the behavior of the communicating meters connected to the cellular type communication network and to take this into account for the data collections to be carried out.

[0010] The method according to the invention may further comprise the optional characteristics considered in isolation or in combination:

[0011] - Said first information comprises at least: • a cell identifier of said network with reference to a communicating meter identifier, • a state of obtaining a response from a counter to a message addressed to it with reference to a timestamp, • a response time of a counter to a message addressed to it, where applicable, with reference to a timestamp.

[0012] Thus, it is possible to determine behavior parameters of each of the counters, such as, for example, an average response time and an average response rate for each counter, depending on the day and the time, and with reference to a cell via which it is accessible.

[0013] - Establishing a data collection schedule from the established parameters is carried out with reference, for each of the communicating meters, to a cell identifier from which a communicating meter is accessible.

[0014] - Establish the parameters representative of the behavior of each of the counters includes statistical analysis.

[0015] - The statistical analysis is carried out with reference to a maximum collection time.

[0016] - The statistical analysis is carried out with reference to a minimal use of the network of communication.

[0017] - Establish the parameters representative of the behavior of each of the counters includes learning using a classifier-type module.

[0018] - Learning using a classifier type module is carried out with reference to a maximum collection time.

[0019] - Learning using a classifier type module is carried out with reference to minimal use of the communications network.

[0020] The invention also relates to a system (or device) for collecting, via a cellular type communication network, data available in a set of communicating meters, the collection system comprising electronic circuitry configured to:

[0021] - i) obtaining first information representative of the behavior of said counters,

[0022] - ii) establish parameters representative of the behavior of each of the counters communicators from said first information,

[0023] - iii) establish a data collection schedule for all or part of said counters from said parameters representative of the behavior of each of the counters, and,

[0024] - iv) transmitting data collection messages to said meters according to said established order.

[0025] The collection system according to the invention may further comprise the charac- optional characteristics considered individually or in combination:

[0026] - The data collection system further comprises circuitry configured to process said first information including: • a cell identifier of said network with reference to a communicating meter identifier, • a state of obtaining a response from a counter to a message addressed to it with reference to a timestamp, • a response time of a counter to a message addressed to it, where applicable, with reference to a timestamp.

[0027] - The data collection system comprises electronic circuitry configured to establish a data collection schedule based on the parameters established with reference, for each of the communicating meters, to a cell identifier from which a communicating meter is accessible.

[0028] - The data collection system further comprises electronic circuitry configured to establish the parameters representative of the behavior of each of the counters by carrying out a statistical analysis.

[0029] - The data collection system further comprises electronic circuitry configured to operate the statistical analysis with reference to a maximum collection time.

[0030] - The data collection system further comprises electronic circuitry configured to operate statistical analysis with reference to minimal use of the communications network.

[0031] - The data collection system further comprises electronic circuitry electronics configured to establish the parameters representative of the behavior of each of the meters by learning using a classifier type module.

[0032] - The data collection system further comprises electronic circuitry configured to operate learning using a classifier-type module, with reference to a maximum collection time.

[0033] Another object of the invention is a computer program product comprising program code instructions for executing the steps of a method as previously described, when this program is executed by a processor of a data collection system.

[0034] Finally, the invention also relates to an information storage medium comprising a computer program product as mentioned above. Brief description of the drawings

[0035] The above-mentioned and other features of the invention will become more clearly apparent from the following description of at least one example of embodiment, said description being made in relation to the attached drawings, among which:

[0036] [Fig. 1] schematically illustrates a communicating meter generating data to be collected, as already known;

[0037] [Fig.2] is a schematic representation of a communication network of type cellular to which communicating meters are connected and comprising an improved system for collecting data generated by the meters, according to one embodiment;

[0038] [Fig.3] is a flowchart illustrating an improved data collection method executed in the communication network already shown in [Fig.2], according to one embodiment; and,

[0039] [Fig.4] is a diagram illustrating an internal architecture of a system or device for collecting data generated by communicating meters, according to one embodiment.

[0040] DETAILED DESCRIPTION OF EMBODIMENTS

[0041] [Fig.l] schematically illustrates a communicating meter 10, already known in the state of the art and configured to collect information on the supply of a physical quantity such as water, electricity or gas, for example. Such a communicating meter is also sometimes called a smart meter or "smartmeter" (from the English). The communicating meter 10 is arranged to operate a count of a distributed physical quantity and transiting between an input 12 of the communicating meter 10, connected to a network supplying the physical quantity, and an output 13 of the communicating meter 10, connected to a domestic, industrial or commercial installation, for example, consuming the distributed physical quantity and whose consumption must be measured and / or controlled remotely.The communicating meter 10 comprises a human-machine interface 14, also called a user interface, capable of entering and displaying information related to the use of the meter. The communicating meter 10 further comprises internal electronic circuitry comprising in particular one or more microcontrollers and a radio communication interface (not shown in the figure) connected to an antenna system 11. Thus, the communicating meter 10 is configured to communicate in a cellular type communication network.

[0042] [Fig. 2] schematically and generally illustrates a cellular type communication network 1000, comprising a communication subnetwork 1001 to which are connected three base stations STA-A, STA-B and STA-C configured to transmit and receive signals from and to third-party devices compatible with the communication network and connected to it. According to the example described, the base station STA-A is connected to the communication subnetwork 1001 via a wired communication link nication 1000a, the base station STA-B is connected to the communication subnetwork 1001 via a wired communication link 1000b and the base station STA-C is connected to the communication subnetwork 1001 via a wired communication link 1000c. Obviously, each of the base stations STA-A, STA-B or STA-C could be connected to the communication subnetwork 1001 via a wireless link. Each of the base stations STA-A, STA-B and STA-C covers a geographical area called a cell. According to the example described, the base station STA-A covers a cell A, the base station STA-B covers a cell B and the base station STA-C covers a cell C. Communicating meters 10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h and 10i, similar to the communicating meter 10 previously described in relation to [Fig.l], are installed in the coverage area of ​​the communication network 1000, established by the combination of the base stations STA-A, STA-B and STA-C respectively operating radio transmissions in cells A, B and C. In the example described, the number of cells of the communication network 1000 as well as the number of communicating meters are deliberately reduced to facilitate the reading of this description and its understanding. Obviously, in reality, the number of cells can be counted in tens, hundreds or thousands for a data collection system and the same is true for the number of meters concerned by a data collection.Although a communicating meter can be geographically positioned in several cells of the communication network 1000, it is considered here that such a communicating meter, connected to a fixed installation, is fixed and that it is consequently connected to a single cell of the communication network 1000 at a given time. According to one embodiment, a communicating meter located in several cells is connected to the communication network 1000 via the cell whose base station offers it the best communication performance. Thus, according to the exemplary embodiment described in [Fig.2], the communicating meters 10a, 10b and 10c are connected to the communication network 1000 via the base station STA-A operating in the cell A, the communicating meters 10d, 10e and 10f are connected to the communication network 1000 via the base station STA-B operating in the cell B and the communicating meters 10g, 10h and 10i are connected to the communication network 1000 via the base station STA-C operating in the cell C. The communication network 1000 further comprises a data collection system 100 configured to collect data generated by the communicating meters 10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h and 10i. According to the example described, the data collection system 100 is connected to the communication subnetwork 1001 via a wired communication link 101. According to a variant, the data collection system 100 is connected to the communication subnetwork 1001 via a wireless link.The data collection system 100 is also referred to herein as a data collection device or . management server of a service provider(s). Advantageously, the data collection system 100 is configured to implement a data collection method aimed at optimizing data collection by reducing the use of resources of the communication network 1000, which network, of the cellular type, is intended to be shared with other uses or applications, including in particular usual mobile telephony or telecommunications applications on a mobile telephony network (audio and / or video transport, for example). Thus, the data collection system 100 is configured to execute an improved collection method described in relation to [Fig.3].

[0043] [Fig. 3] illustrates steps of the improved data collection method performed in the communication network 1000 or in a similar communication network.

[0044] A step S0 corresponds to an initialization step at the end of which the data collection system 100, as well as all the systems of the communication network 1000, are normally configured to operate data transmissions through the communication network 1000. Thus, at the end of step S0, the data collection system 100 is able to send messages to the different communicating meters 10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h and 10i via, depending on the communicating meter concerned by a sending of message(s), the base station to which it is connected. The data collection system 100 is further configured to receive messages sent by the communicating meters in response to the messages addressed to them, as soon as the latter have the capacity to respond.Indeed, some communicating meters can be temporarily disrupted by obstacles to electromagnetic waves or by poor operating conditions of the communication network, or even depending on potential standby cycles aimed at achieving energy savings.

[0045] Cleverly, the improved data collection method executed by the system 100 for collecting data generated by the various communicating meters 10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h and 10i operates four main steps S1, S2, S3 and S4 also called here respectively “labeling phase”, “learning and planning phase”, “scheduling phase” and “data collection phase”.

[0046] During step SI, and throughout a period T of a predefined duration (for example a full week), the data collection system 100 addresses protocol messages to the various communicating meters 10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h and 10i and records numerous pieces of information relating to the behavior of the various meters. This involves knowing whether or not a meter responds to a protocol message addressed to it and, if so, in how much time. This information is recorded in correlation with an identifier of the communicating meter concerned. During this step SI, information relating to the cell of the communication network 1000 via which a communicating meter has responded or has not responded is obtained and stored, so as to also be able to store all of the information with reference to a cell identifier of the communication network 1000. The term “protocol message” here designates any message having a predefined format that a meter is supposed to be able to receive and interpret, so as to respond to it and that the data collection system 100 also understands or at least that it is capable of generating and for which the data collection system 100 can identify a response or an absence of response. The format of the protocol messages exchanged is not described here insofar as it does not contribute to the understanding of the invention.The messages exchanged between the data collection system 100 and the communicating meters can be exchanged as part of transmissions related to a service provision or simply as part of the search for information representative of the operation of the communicating meters, or even via a combination of these two ways of doing things. Thus, at the end of the period T of census of information representative of the operation of the meters, the data collection system 100 has information relating to each of the communicating meters, due to the numerous communication attempts made via the message exchanges described.

[0047] According to one embodiment, the data collection system 100 lists and records at least information correlated with each other and presented in the form of: - a cell identifier of the communication network 1000 with reference to a communicating meter identifier, - a state of obtaining a response from a counter to a message addressed to it with reference to a timestamp, for example "response" or "absence of response" at the current time, - a response time of a meter to a message addressed to it, where applicable, and with reference to a timestamp, for example a given meter responded at such time, after a delay of 10 seconds.

[0048] According to one embodiment, the data collection system 100 has information concerning the meters for which data collection is to be carried out and therefore the meters to which the improved data collection method should be applied. Thus, the data collection system 100 has, for example, a list of identifiers of the communicating meters concerned and does not need to carry out any phase of discovery of communicating meters present or potentially present in a geographical area covered by the communication network. communication 1000. For example, the data collection system 100 can send one or more messages to each of the meters present in a list and then use information relating to the communication protocol of the cellular type communication network 1000 to identify via which base station a meter could be reached and therefore, in which cell of the communication network it is located.

[0049] According to an exemplary embodiment, the information recorded and stored during step SI is grouped by cells of the communication network 1000, that is to say with reference to a cell identifier of the communication network 1000.

[0050] For example, a set of information obtained during the so-called labeling phase, during step SI, can be organized as in the table below:

[0051] [Tables 1] Identifier of Identifier Date Time State Time of cell of counter (d) (h) of obtaining response (s) (Ceid) (Coid) of a response (tr) (r) A 10a 29 / 09 / 2023 00:00 response 0.1 A 10b 29 / 09 / 2023 00:00 response 0.2 A 10c 30 / 09 / 2023 00:00 no response B lOd 09 / 30 / 2023 00:00 response 0.1 B 10e 10 / 01 / 2023 00:15 response 0.2 B lOf 10 / 01 / 2023 00:30 no response C 10g 10 / 01 / 2023 00:15 response 0.3 C lOh 10 / 01 / 2023 00:45 response 0.2 C lOi 10 / 02 / 2023 00:45 no response

[0052] Thus, cell identifier, counter identifier, timestamp, response obtained status and response time information are correlated with each other in a memory of the data collection system 100.

[0053] During step S2, the data collection system 100 establishes operating parameters specific to each of the communicating meters concerned by the collection or in other words the data collection system 100 establishes a profile behavior for each of the communicating meters, based on the information collected during step SI for a period of duration T and stored. This learning phase is carried out by proceeding by meter and by time slots, for example by meter and by hour or by meter and by quarter of an hour, or by meter and by five-minute slots, these examples not being limiting. According to a first embodiment, the data collection system 100 carries out this learning phase by carrying out a statistical analysis aimed at determining at what moment each of the communicating meters has the greatest probability of responding to a message addressed to it, in particular to a data collection message. According to a second embodiment, the data collection system 100 carries out this learning phase by carrying out a “machine learning” type learning capable of providing a probability of successful communication for each of the meters concerned by an upcoming collection.

[0054] Each of these embodiments can be produced according to variants having the respective objective of: - to respect a time constraint for the end of collection, such as for example aiming to have collected the data from all the meters before eight o'clock the next morning, or - to significantly reduce the use of the resources of the communication network used (here the communication network 1000) in order to avoid any redundancy in sending messages to the meter until the data to be collected is obtained.

[0055] According to one embodiment, when the analysis is of the statistical type and when the objective is to respect a time constraint, the data collection system 100 determines, for each of the counters, a time slot for which this counter has the fastest collection time while having a minimum threshold of 50% in response rate to messages addressed to it.

[0056] According to one embodiment, when the analysis is of the statistical type and when the objective is to reduce the use of the resources of the communication network used, the data collection system 100 determines a time slot for which a counter has a maximum response rate to messages addressed to it.

[0057] According to one embodiment, when the learning phase executes a “machine learning” type learning whose objective is to determine a probability of communication success depending on the circumstances of sending a message to a communicating meter (for example according to the day of the week, the date, the time, whether a day is a holiday or not, etc.) a “decision tree” type algorithm is used. According to one embodiment, the algorithm used is an algorithm for classifying (also called a classifier) ​​each time slot according to two classes respectively associated with a communicating meter and a non-communicating meter. Thus, each time slot will have a probability of belonging to a class for a given meter. According to one embodiment, it is considered that if a time slot has a probability of belonging greater than 0.6 for a class, then this time slot belongs to this class. Such an algorithm aims to maximize the success rate of a data collection by respecting a collection deadline or to minimize the number of communications necessary for a data collection available in a given communicating meter.

[0058] According to one embodiment, the training of the classifier, of the decision tree type, is based on two sets of information, one of which, the first, comprises all the first information previously collected in step SI, time-stamped (recorded in association with the current date and time), enriched with second information which is the day of the week, the month of the year, the holiday or not of the day concerned, the urban or rural nature of the position of the meter identified by the cell of the network via which it communicates, and the other, second, of which comprises the class to which each piece of information belongs (for example a class 0 according to which the meter communicates and a class 1 according to which the meter does not communicate).

[0059] Cleverly, the algorithm thus trained is used during step S2, if applicable (variant of a “machine learning” type learning mode) to determine what is the best time slot for each of the communicating meters concerned by a planned and future collection.

[0060] At the end of learning thus carried out in step S2, the data collection system 100 has the capacity to determine, in step S3, a collection schedule as a function of predefined constraints, for example as a function of calendar constraints of a data collection (which day, which duration, etc.).

[0061] According to one embodiment, the collection scheduling is carried out by proceeding cell by cell and with the aim of subsequently carrying out a collection in parallel (simultaneously) in each of the cells concerned in the communication network.

[0062] According to one embodiment, and depending on collection organization constraints, such as for example calendar constraints (for example, in the night from Tuesday to Wednesday, between 9 p.m. and 9 a.m., in an urban area), the scheduling is established so as to collect the data from all the meters which are identified as being able to respond at the planned time of the collection and also not being able to respond in the moments which follow the collection. The collection then concerns the maximum number of meters in each of the cells meeting this criterion by using the maximum number of resources of the communication network which are allocated (or dedicated) to the collection during the duration of the collection.

[0063] According to one embodiment, data collection tables are generated so as to establish a collection order (sequence), with reference to each of the cells A, B and C, of ​​the communication network 1000.

[0064] When a collection schedule has been established by the data collection system 100 by listing information on the observed behavior of meters concerned by the collection that is the subject of the scheduling, then by determining operating parameters (profile) for each of the meters, and taking into account an objective defined in terms of calendar constraints and / or level of network resources used, the data collection system 100 can execute the data collection during step S4, proceeding according to the established sequencing and operating in parallel (simultaneously) in the different cells A, B and C of the communication network.

[0065] According to one embodiment and when the collection is finished at the end of step S4, that is to say when the time allowed for collecting data has expired, an optional step S5 is executed by the data collection system 100, aimed at determining the rate of communicating meters that could be read successfully, that is to say the rate of communicating meters that could send the data to be collected in response to a collection message from the data collection system 100.

[0066] If the collection rate determined in step S5 is greater than a predefined collection objective (step S5, output “yes”), then the method is completed and no new collection operation will be carried out before a new collection phase is planned according to the defined collection policy. Conversely, if the collection rate determined in step S5 is insufficient (step S5, output “no”), then the method loops back to step S1 by modifying for example the parameter T of duration of the labeling phase aimed at obtaining new information representative of the behavior of the communicating meters present in the communication network 1000.Each new iteration of the SI step carried out when the collection completeness rate is deemed insufficient is considered to be a learning reinforcement phase which aims in particular to identify the communicating meters which are involved in the fact that it was not possible to achieve the targeted collection rate. According to one embodiment, a new iteration of the SI step is carried out with a collection duration of the information representative of the behavior of the meters greater than that initially defined (for example greater than seven days).

[0067] According to one embodiment, and considering that certain meters can be identified as being reluctant to respond, for example due to disturbances in communications by electromagnetic transmissions, a communication strategy with these meters can be advantageously established, for example by not not systematically repeating a communication attempt for all elementary time periods considered by the data collection system 100 but by only attempting to collect data from a predefined number of communicating meters to be read during an elementary time period. In other words, if the system repeats communication attempts with “difficult to reach” meters every five minutes, it will only attempt to reach 70 or 60% of the meters remaining to be reached, for example, per five-minute period, to avoid congestion of the communication network 1000. Obviously, the rate of meters subject to a new attempt can be any, between 0 and 100% depending on the results previously obtained.

[0068] According to one embodiment, and when a new communicating meter is inserted into the communication network 1000, the data collection system 100 plans to operate a data collection schedule identical to that planned for a communicating meter geographically close to a communicating meter already known to the collection system 100, in terms of behavior. If such a strategy does not allow data to be collected from this meter, without difficulty, then a learning phase will have to take place according to the method described.

[0069] It should be noted that in [Fig.3], the optional nature of step S5 is illustrated by dotted lines.

[0070] [Fig. 4] schematically illustrates an example of internal architecture of the data collection system 100, also called here data collection device or management server of a service provider(s). According to the example of hardware architecture represented in [Fig.4], the collection system 100 then comprises, connected by a communication bus 120: a processor or CPU (Central Processing Unit) 121; a RAM (Random Access Memory) 122; a ROM (Read Only Memory) 123; a storage unit such as a hard disk (or a storage media reader, such as an SD (Secure Digital) card reader) 124; at least one communication interface 125 allowing the data collection system 100 to communicate with devices present in the communication network comprising the data collection system 100, including in particular the communicating meters 10a, 10b, 10c, 10d, 10e, 10f and 10h, via the base stations STA-A, STA-B and STA-C.

[0071] The processor 121 is capable of executing instructions loaded into the RAM 122 from the ROM 123, an external memory (not shown), a storage medium (such as an SD card), or a communications network. When the data collection system 100 is powered on, the processor 121 is capable of reading instructions from the RAM 122 and executing them. These instructions form a computer program causing the processor 121 to implement the method described in relation to [Fig.3] or one of its variants.

[0072] All or part of the method implemented by the data collection system 100, or its described variants, may be implemented in software form by executing a set of instructions by a programmable machine, for example a DSP (Digital Signal Processor) or a microcontroller, or be implemented in hardware form by a machine or a dedicated component, for example an FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific In-tegrated Circuit). In general, the data collection system 100 comprises electronic circuitry configured to implement the described method in relation to itself as well as to devices connected to the communication network 1000.Obviously, the data collection system 100 further comprises all the elements usually present in a system comprising a control unit and its peripherals, such as a power supply circuit, a power supply supervision circuit, one or more clock circuits, a reset circuit, input-output ports, interrupt inputs, bus drivers, this list being non-exhaustive.

[0073] The invention is not limited to the embodiments and examples described but relates more broadly to any collection method, operating via a cellular type communication network, adapted to the collection of data available in a set of communicating meters and executed in a data collection device connected to this network, and comprising: obtaining first information representative of the behavior of the meters, then establishing representative parameters (or profiles) of the behavior of each of the communicating meters from this information, with a view to establishing a data collection schedule for all or part of the meters from the parameters (or profiles) representative of the behavior of each of the meters, and then transmitting data collection messages to the communicating meters according to the schedule that has been established.In particular, a classifier of a type other than a decision tree can be used for the learning phase carried out during step S2.

Claims

Claims

1. Method for collecting, via a cellular type communication network (1000), data available in a set of communicating meters (10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h, 10i), the method being executed in a data collection system (100) connected to said network (1000) and the method comprising: - i) obtaining initial information (SI) representative of the behavior of said counters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi), - ii) establishing parameters (S2) representative of the behavior of each of the communicating meters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi) from said first information, - iii) establishing a collection schedule (S3) of data from all or part of said meters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi) from said parameters representative of the behavior of each of the meters, and, - iv) transmitting data collection messages (S4) to said counters (10a, 10b, 10c, lOd, 10e, lOf, 10g, lOh, lOi) according to said established schedule.

2. Data collection method according to claim 1, wherein said first information comprises at least: - a cell identifier (Ceid) of said network with reference to a communicating meter identifier (Coid), - a state of obtaining (r) a response from a meter to a message addressed to it with reference to a timestamp (d, h), - a response time (tr) of a meter to a message addressed to it, where applicable, and with reference to a timestamp (d, h).

3. Data collection method according to claim 2, in which establishing a data collection schedule (S3) from said parameters is carried out with reference, for each of the communicating meters, to a cell identifier (Ceid) from which a communicating meter is accessible.

4. A method of collecting data according to one of claims 1 to 3, wherein establishing said parameters representative of the behavior of each of the meters comprises a statistical analysis.

5. A data collection method according to claim 4, wherein the statistical analysis is carried out with reference to a maximum collection time.

6. A method of collecting data according to claim 4, wherein the statistical analysis is carried out with reference to minimal use of said communication network.

7. Data collection method according to one of claims 1 to 3, in which establishing said parameters representative of the behavior of each of the meters comprises learning by means of a classifier type module.

8. A method of collecting data according to claim 7, wherein learning by means of a classifier type module is carried out with reference to a maximum collection time.

9. A method of collecting data according to claim 7, wherein the learning by means of a classifier type module is carried out with reference to minimal use of said communication network.

10. Data collection system (100), via a cellular type communication network (1000), of data available in a set of communicating meters, the collection system comprising electronic circuitry configured to: - i) obtain first information (SI) representative of the behavior of said meters, - ii) establish parameters representative of the behavior (S2) of each of the communicating meters from said first information, - iii) establish a collection schedule (S3) of data from all or part of said meters from said parameters representative of the behavior of each of the meters, and, - iv) transmit data collection messages (S4) to said meters according to said established schedule.

11. The data collection system (100) of claim 10, further comprising circuitry configured to process said first information comprising: - a cell identifier of said network with reference to a communicating meter identifier, - a state of obtaining a response from a meter to a message addressed to it with reference to a timestamp, - a response time of a meter to a message addressed to it, where applicable, and with reference to a timestamp.

12. Data collection system according to one of claims 10 and 11, comprising electronic circuitry configured to establish a data collection schedule from said parameters with reference, for each of the communicating meters, to a cell identifier from which a communicating meter is accessible.

13. A data collection system according to one of claims 10 to 12, further comprising electronic circuitry configured to establish said parameters representative of the behavior of each of the meters by performing a statistical analysis.

14. The data collection system of claim 13, further comprising electronic circuitry configured to perform the statistical analysis with reference to a maximum collection time.

15. The data collection system of claim 13, further comprising electronic circuitry configured to perform the statistical analysis with reference to minimal utilization of said communications network.

16. Data collection system according to one of claims 10 to 12, further comprising electronic circuitry configured to establish said parameters representative of the behavior of each of the meters by learning using a classifier type module.

17. A data collection system according to claim 16, further comprising electronic circuitry configured to operate said learning by means of a classifier type module, with reference to a maximum collection time.

18. A data collection system according to claim 16, further comprising electronic circuitry configured to operate said learning by means of a classifier type module, with reference to minimal use of said communication network.

19. Computer program product characterized in that it comprises program code instructions for performing the steps of the method according to any one of claims 1 to 9, when said program is executed by a processor of said data collection system

20. An information storage medium comprising a computer program product according to claim 19.

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