Data processing method of electronic information technology based on big data

Abstract

The invention discloses a data processing method of an electronic information technology based on big data, relates to the technical field of Internet of Things equipment, and solves the problem that the route of a robot is reasonably planned without being based on the actual equipment operation condition and the position in the Internet of Things equipment, so that the reliability of the robot is improved. According to the method, data inspection is performed on different devices in the Internet of Things devices, the corresponding data inspection frequency is determined preferentially based on the data generation frequency between the different devices, and then the associated frequency sequence is determined based on the corresponding data inspection frequency, so that the robot can achieve a better path planning effect. The confirmed frequency sequence is subjected to reconditioning segmentation, and related confirmation of the data segments is carried out based on different data performance conditions among different data columns, so that the corresponding standard data columns and the corresponding qualitative data columns are locked, and the inspection frequency in the optimal sequence can be ensured to belong to the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things devices, and in particular to a data processing method based on big data electronic information technology. Background Art

[0002] With the rapid development of information technology, the amount of data has exploded, and the era of big data has arrived. In many fields, such as finance, medical care, the Internet, industrial manufacturing, etc., a huge amount of data resources have been accumulated. These data contain huge value, but due to their large data scale, diverse data types, and fast data generation speed, traditional data processing technology faces many challenges. For example, in the era of big data, data is vulnerable to attacks. For IoT devices, attackers may modify the data content transmitted or stored by IoT devices.

[0003] Therefore, it is necessary to inspect and detect the data of IoT devices. Generally, the corresponding inspection robots are controlled to collect data from IoT-related devices. However, in the actual collection process, the robots repeat the same routes due to unreasonable route formulation and planning. The power consumption is high, and it takes a long time to execute a complete inspection process. The robot's route is not reasonably planned based on the operation status and location of the IoT devices, so that the robot can achieve better path planning effect. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a data processing method of electronic information technology based on big data, which solves the problem of not rationally planning the robot's route based on the actual device operation status and location in the Internet of Things device, so that the robot can achieve better path planning effect.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data processing method based on electronic information technology of big data, comprising:

[0006] Step 1: Confirm the data frequencies of different devices in the IoT device, and confirm the data inspection frequency based on the confirmed data frequency, and generate the frequency sorting sequence of the devices inside the corresponding IoT device. The specific sub-steps are:

[0007] S11. Confirm the data frequency of different devices from the cloud, and mark the confirmed data frequency as P i , where i represents different devices, and P i All are preset values;

[0008] S12, generating frequency P according to the confirmed data sets i , using XJ i =P i×C1 confirms data inspection frequency XJ i , where C1 is the preset value, and the data inspection frequency XJ confirmed by different devices i Sort by value from small to large, confirm the frequency sorting sequence, if there is the same XJ i , then the front and back can be randomly sorted;

[0009] Step 2: Based on the frequency sorting sequence confirmed between several devices in the IoT device, based on the distance arrangement between different devices in the frequency sorting sequence and the arrangement of data inspection frequency, adjust the data segment of the frequency sorting sequence, confirm the standard data column and the qualitative data column, and the specific sub-steps are:

[0010] S21, confirm the data inspection frequencies associated with the previous and next groups from the confirmed frequency sorting sequence, and mark the data inspection frequencies associated with the previous group as X1 J i , the data inspection frequency associated with the latter group is calibrated as X2J i , confirm the associated device based on the associated tag i, and determine the moving distance L between the two groups of devices from the IoT device model k , its IoT device model is the preset model, and then based on the preset patrol robot movement speed V, its associated movement time T is determined k , where T k =L k ÷V, identify the X1 J associated with the two groups of devices before and after i and X2J i The numerical relationship of: If X1 J i +T k <X2J i , mark the front and back two groups of equipment as pending equipment;

[0011] If X1 J i +T k =X2J i , the data segments associated with the two groups of devices are marked as qualitative data columns, and the data segments are the corresponding {X1 J i 、X2J i}, the time associated with this part of the data segment is a qualitative relationship, and other time cannot be inserted or adjusted. That is, after the robot inspects the previous set of equipment, it passes through the road section and can just inspect the next set of equipment. In this case, it is marked as a qualitative data column without any adjustment;

[0012] If X1 J i +T k <X2J i, then the data inspection frequency associated with the latter group of devices in the two groups is removed from the frequency sorting sequence, and the same processing method in step 2 is used again for the removed data inspection frequency to reconfirm the standard data column and the qualitative data column;

[0013] S22, based on the associated pending device, using the data segment {X1 J i 、X2J i} as the determination benchmark, the behavior time between the devices that appear successively after the device to be determined is determined based on the frequency sorting sequence, and is marked as: T k1 、T k2 ,……,T kn , if X1 J i +T k +T k1 >X2J i , then the data segment corresponding to the data inspection frequency associated with the pending device is marked as a qualitative data column;

[0014] If X1 J i +T k +T k1 =X2J i , then the group of devices after the pending device is divided into the pending device, and the data segments corresponding to the data inspection frequencies associated with the three groups of devices are marked as standard data columns;

[0015] If X1 J i +T k +T k1 <X2J i , then use X1 J i +T k +T k1 +……+T kj =Tz confirms its time characteristic value Tz, where j∈[2,n], so that j gradually increases and determines the corresponding time characteristic value Tz to Tz≥X2J i When the time stops, k1 To T k(j-1) The associated devices are divided into pending devices, and the data segments corresponding to the data inspection frequencies associated with these devices are marked as standard data columns;

[0016] Step 3: reorder several groups of equipment associated with the standard data column, and simultaneously confirm the equipment value tables associated with the several groups of equipment. Based on the reordered equipment sequence and the equipment value table, assign values ​​to the confirmed several groups of equipment sequences, and select the best sequence from the several groups of equipment sequences based on the total value generated. The specific sub-steps are:

[0017] S31. Based on several groups of devices and IoT device models associated with the standard data column, a group of devices is randomly selected from several groups of devices as the main device, and other devices in the several groups of devices are used as the secondary devices of the main device. The distance between the main device and different secondary devices is identified. The distance between the main device and different secondary devices is consistent with the method of determining the moving distance between devices in step S21. The determined different distances are calibrated as Lq, where q represents different secondary devices. The secondary devices are then sorted in an ascending order according to Lq, so as to obtain an associated sorting sequence belonging to the main device, and the associated assignment Fo at different positions in the associated sorting sequence is determined, where Fo=o×C2, where C2 is a preset fixed coefficient factor, and o represents different positions. The associated sorting sequence with the associated assignment is used as the device assignment table corresponding to the main device, and this method is repeated to confirm the device assignment table of each group of devices in turn.

[0018] S32, confirm the pending devices associated with the corresponding standard data column, use the first device of the pending devices as the first group of devices in the device sequence, use the last device of the pending devices as the last group of devices in the device sequence, and randomly sort the other devices between the first device and the last device, and confirm several groups of device sequences based on the random arrangement;

[0019] S33, confirming the total value assigned to each group of equipment sequence: starting from the first group of equipment in the equipment sequence, identifying the position of the next group of equipment in the value assignment table of the previous group of equipment, and confirming the associated value Fo corresponding to the position, confirming the Fo associated with each subsequent group of equipment in turn, summing up the confirmed groups of Fo, and confirming the total value Zp, where p represents different equipment sequences;

[0020] Based on the different total assignments Zp confirmed by different equipment sequences, the equipment sequence associated with Zpmin is marked as the best sequence, and the best sequence associated with each set of standard data columns is confirmed one by one;

[0021] Step 4: Based on the standard data column associated with the optimal sequence, the optimal sequence is re-filled to the same position based on the location of the standard data column, and the frequency sorted sequence after the filling process is used as the control sequence, and the inspection robot is controlled so that the inspection robot inspects and collects the equipment associated with the control sequence in turn.

[0022] Preferably, in step S21, the method for confirming the moving distance between the front and rear groups of devices includes:

[0023] Take one set of devices as the starting point and another set of devices as the end point, and generate several moving routes based on the confirmed starting point and end point, and then select a set of minimum moving distance L kmin is taken as the selected route, and the moving distance of the selected route is taken as the moving distance L between the two groups of devices. k , where k represents the moving distance between different devices.

[0024] Preferably, an intelligent inspection and collection system for IoT devices comprises:

[0025] The initial sequence generation end first confirms the data frequencies of different devices in the IoT device, and confirms the data inspection frequency based on the confirmed data frequency, and generates a frequency sorting sequence for the devices inside the corresponding IoT device;

[0026] The data column classification end adjusts the data segments of the frequency sorting sequence based on the frequency sorting sequence confirmed between several devices in the IoT device, based on the distance arrangement between different devices in the frequency sorting sequence and the arrangement of data inspection frequency, and confirms the standard data column and the qualitative data column;

[0027] The sequence optimization end reorders the several groups of equipment associated with the standard data column, and simultaneously confirms the equipment value tables associated with the several groups of equipment. Based on the reordered equipment sequence and the equipment value table, the confirmed several groups of equipment sequences are assigned values, and based on the generated total value, the best sequence is selected from the several groups of equipment sequences.

[0028] The control execution end re-fills the best sequence to the same position based on the location of the standard data column, uses the frequency sorted sequence after filling as the control sequence, and controls the inspection robot. The inspection robot collects data from the associated devices in turn and transmits the collected relevant data to the cloud for storage.

[0029] The present invention provides a data processing method based on electronic information technology of big data. Compared with the prior art, it has the following beneficial effects:

[0030] The present invention performs data inspection on different devices in the Internet of Things device, and first determines the corresponding data inspection frequency based on the data generation frequency between different devices, and then confirms the frequency sequence associated with it based on the corresponding data inspection frequency, and then trims and segments the confirmed frequency sequence, and performs relevant confirmation of the data segment based on the different data performance conditions between different data columns, so as to lock the corresponding standard data column and the corresponding qualitative data column;

[0031] Then, the performance data column is numerically adjusted and analyzed, each device is re-sorted, and the best sequence is determined based on the distance performance between the devices. The best sequence can not only ensure that the inspection frequency is in the best state, but also ensure that the associated distance data is also in the best state, thereby achieving a better inspection route determination effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the process of the present invention;

[0033] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] First embodiment

[0036] See also Figure 1 , the present application provides a data processing method based on big data electronic information technology, comprising the following steps:

[0037] Step 1: Confirm the data frequency of different devices in the IoT device, and confirm the data inspection frequency based on the confirmed data frequency, and generate the frequency sorting sequence of the devices inside the corresponding IoT device. Specifically, different devices inside each IoT device have different time intervals when generating data. Therefore, when performing inspections, data collection is required based on the order of the corresponding device time generation, so as to complete the data collection process of the devices inside the entire IoT device. The specific sub-steps for generating the frequency sorting sequence are:

[0038] S11. Confirm the data frequency of different devices from the cloud, and mark the confirmed data frequency as P i , where i represents different devices, and P i These are all preset values, which are prepared in advance by relevant operators based on the operation process of the internal devices of the IoT device;

[0039] S12, generating frequency P according to the confirmed data sets i , using XJ i =P i ×C1 confirms data inspection frequency XJ i, where C1 is a preset value, and its specific value is determined by the operator based on experience, and is a positive integer. The data inspection frequency XJ confirmed by different devices i Sort by value from small to large, confirm the frequency sorting sequence, if there is the same XJ i , then the front and back can be randomly sorted;

[0040] Specifically, the data generation frequency is XJ i To monitor the data generated by the corresponding equipment after operation, the corresponding equipment needs to be inspected after generating data or performing corresponding work to evaluate the data performance of the corresponding equipment after operation. The corresponding equipment needs to be inspected after generating a certain number of sets of data. The corresponding inspection frequency is determined based on the corresponding data generation frequency, so as to perform equipment inspection and maintenance.

[0041] Step 2: Based on the frequency sorting sequence confirmed between several devices in the IoT device, based on the distance arrangement between different devices in the frequency sorting sequence and the arrangement of data inspection frequency, adjust the data segment of the frequency sorting sequence, confirm the standard data column and the qualitative data column, wherein the specific sub-steps for confirmation are:

[0042] S21, confirm the data inspection frequencies associated with the previous and next groups from the confirmed frequency sorting sequence, and mark the data inspection frequencies associated with the previous group as X1 J i , the data inspection frequency associated with the latter group is calibrated as X2J i , confirm the associated device based on the associated tag i, and determine the moving distance L between the two groups of devices from the IoT device model k The IoT device model is a preset model, which is prepared in advance by relevant personnel. It takes one group of devices as the starting point and another group of devices as the end point. Based on the confirmed starting point and end point, several moving routes are generated, and then a set of minimum moving distance L is selected. k min is taken as the selected route, and the moving distance of the selected route is taken as the moving distance L between the two groups of devices. k , where k represents the moving distance between different devices, and then based on the preset inspection robot movement speed V, confirm its associated movement time T k , where T k =L k ÷V, identify the X1 J associated with the two groups of devices before and after i and X2J i The numerical relationship of: If X1 J i +T k <X2J i , mark the front and back two groups of equipment as pending equipment;

[0043] If X1 J i +T k =X2J i , the data segments associated with the two groups of devices are marked as qualitative data columns, and the data segments are the corresponding {X1 J i 、X2J i}, that is, a certain data segment in the corresponding frequency sorting sequence. The time associated with this data segment is a qualitative relationship, and other time cannot be inserted or adjusted. That is, after the robot inspects the previous set of equipment, it passes through the road section and can just inspect the next set of equipment. In this case, it is marked as a qualitative data column without any adjustment.

[0044] If X1 J i +T k <X2J i , then the data inspection frequency associated with the latter group of equipment in the front and rear two groups of equipment is removed from the frequency sorting sequence, and the same processing method in step 2 is used again for the removed data inspection frequency to reconfirm the standard data column and the qualitative data column. Specifically, each group of frequency sorting sequence corresponds to a group of inspection robots. When there are two groups of frequency sorting sequences, then two groups of inspection robots need to be dispatched at the same time to perform inspection work. When the time relationship between the current group and the latter group cannot meet the corresponding inspection process, it means that the time cannot be met, that is, one group of robots cannot meet this situation, then the frequency is directly removed to confirm the second group of sequences, so as to select another group of robots for inspection work to ensure that each device can be inspected;

[0045] S22, based on the associated pending device, using the data segment {X1 J i 、X2J i} as the determination benchmark, the behavior time between the devices that appear successively after the device to be determined is determined based on the frequency sorting sequence, and is marked as: T k1 、T k2 ,……,T kn , if X1 J i +T k +T k1 >X2J i , it means that the first group of subsequent devices cannot be interspersed between the pending devices, and the data segment corresponding to the data inspection frequency associated with the pending devices is marked as a qualitative data column;

[0046] If X1 J i +T k +T k1 =X2J i, then the group of devices after the pending device is divided into the pending device, and the data segments corresponding to the data inspection frequencies associated with the three groups of devices are marked as standard data columns;

[0047] If X1 J i +T k +T k1 <X2J i , then use X1 J i +T k +T k1 +……+T kj =Tz confirms its time characteristic value Tz, where j∈[2,n], so that j gradually increases and determines the corresponding time characteristic value Tz to Tz≥X2J i When the time stops, k1 To T k(j-1) The associated equipment is divided into pending equipment, and this part of equipment (including T k1 To T k(j-1) The data segment corresponding to the data inspection frequency associated with the associated equipment and the pending equipment is marked as a standard data column, and the priority of the qualitative data column is consistent with that of the standard data column. That is to say, when the standard data column encounters the qualitative data column when confirming the equipment step by step, the confirmation process is stopped, that is, the qualitative data column cannot be interfered with or split during the step-by-step confirmation process of the corresponding standard data column;

[0048] This part of the content is understood in conjunction with the corresponding examples: Assume that there are seven groups of devices in the current IoT device, namely: A, B, C, D, E, F and G, where the different data inspection frequencies associated with each different device are: 2h, 3h, 2.5h, 4h, 6h, 5h and 7h. Sorting is performed according to the corresponding sorting method, and the corresponding frequency sorting sequence is confirmed: 2-2.5-3-4-5-6-7, and the corresponding devices are: ACBDFEG. According to this sequence, the confirmed behavior time between adjacent devices is confirmed and calibrated as: 0.5, 0.1, 0.1, 0.1, 1.1, 0.5, then the behavior time between AC is 0.5, and the corresponding frequency sequence is 2-2.5, where 2+0.5=2.5, then AC belongs to the determined qualitative data column;

[0049] The sequence between its CB is 2.5-3, the corresponding difference is 0.5, the corresponding path time is 0.1, and 2.5+0.1<3, so CB belongs to the pending device. The same processing method as the pending device is used to confirm the standard data sequence. The subsequently associated 0.1 and 0.1 both meet the corresponding requirements, that is, 2.5+0.1+0.1+0.1<3, so CBDF belongs to the standard data sequence.

[0050] The time between FE is 1.1, and the corresponding sequence is 5-6. 5+1.1>6, so it cannot meet the inspection requirements. Therefore, F is directly eliminated. After elimination, the travel time between FG is reconfirmed for comprehensive evaluation. The sequence after elimination becomes: 2-2.5-3-4-5-7. The same method is used to confirm one by one, and the standard data column or qualitative data column is locked. The eliminated data can be inspected by the second group of inspection robots.

[0051] Step 3: Reorder several groups of equipment associated with the standard data column, and simultaneously confirm the equipment assignment table associated with several groups of equipment. Based on the reordered equipment sequence and the equipment assignment table, assign values ​​to the confirmed several groups of equipment sequences, and select the best sequence from the several groups of equipment sequences based on the total assignment generated. Specifically, this is the optimization process. Since the inspection cycle associated with its time in the corresponding standard data column has been determined to meet the corresponding situation, the corresponding distance analysis can be used to confirm the closest distance and determine the best sequence in sequence. The best sequence can not only ensure that its inspection frequency is in the best state, but also ensure that the associated distance data is in the best state, so as to achieve a better inspection route determination effect. The specific sub-steps for selecting the best sequence are:

[0052] S31. Based on several groups of devices and IoT device models associated with the standard data column, a group of devices is randomly selected from several groups of devices as the main device, and other devices in the several groups of devices are used as the secondary devices of the main device. The distance between the main device and different secondary devices is identified. The distance between the main device and different secondary devices is consistent with the method for determining the moving distance between devices in step S21. The determined different distances are calibrated as Lq, where q represents different secondary devices. Then, the secondary devices are sorted in an ascending order according to Lq, so as to obtain an associated sorting sequence belonging to the main device, and the associated assignment Fo at different positions in the associated sorting sequence is determined, where Fo=o×C2, where C2 is a preset fixed coefficient factor, and o represents different positions. The associated sorting sequence with the associated assignment is used as the device assignment table corresponding to the main device. The earlier the sorting position is, the smaller the generated associated assignment Fo is. This method is repeated to confirm the device assignment table of each group of devices in turn.

[0053] S32, confirm the pending devices associated with the corresponding standard data column, use the first device of the pending devices as the first group of devices in the device sequence, use the last device of the pending devices as the last group of devices in the device sequence, and randomly sort the other devices between the first device and the last device, and confirm several groups of device sequences based on the random arrangement;

[0054] S33, confirming the total assignment of each group of equipment sequence: starting from the first group of equipment in the equipment sequence, identifying the position of the next group of equipment in the previous group of equipment assignment table, and confirming the associated assignment Fo corresponding to the position, confirming the Fo associated with each subsequent group of equipment in turn, summing up the confirmed groups of Fo, and confirming the total assignment Zp, where p represents different equipment sequences. Specifically, the smaller the Zp, the closer the next group of equipment is to the previous group of equipment;

[0055] Based on the different total assignments Zp confirmed by different equipment sequences, the equipment sequence associated with Zpmin is marked as the best sequence, and the best sequence associated with each set of standard data columns is confirmed one by one;

[0056] Step 4: Based on the standard data column associated with the optimal sequence, the optimal sequence is re-filled to the same position based on the location of the standard data column, and the frequency sorted sequence after the filling process is used as the control sequence, and the inspection robot is controlled so that the inspection robot inspects and collects the equipment associated with the control sequence in turn.

[0057] Second embodiment

[0058] Combination Figure 2 , an intelligent inspection and collection system for IoT devices, comprising:

[0059] The initial sequence generation end first confirms the data frequencies of different devices in the IoT device, and confirms the data inspection frequency based on the confirmed data frequency, and generates a frequency sorting sequence for the devices inside the corresponding IoT device;

[0060] The data column classification end adjusts the data segments of the frequency sorting sequence based on the frequency sorting sequence confirmed between several devices in the IoT device, based on the distance arrangement between different devices in the frequency sorting sequence and the arrangement of data inspection frequency, and confirms the standard data column and the qualitative data column;

[0061] The sequence optimization end reorders the several groups of equipment associated with the standard data column, and simultaneously confirms the equipment value tables associated with the several groups of equipment. Based on the reordered equipment sequence and the equipment value table, the confirmed several groups of equipment sequences are assigned values, and based on the generated total value, the best sequence is selected from the several groups of equipment sequences.

[0062] The control execution end re-fills the best sequence to the same position based on the location of the standard data column, uses the frequency sorted sequence after filling as the control sequence, and controls the inspection robot. The inspection robot collects data from the associated devices in turn and transmits the collected relevant data to the cloud for storage.

[0063] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0064] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data processing method based on electronic information technology of big data, characterized in that: include: Step 1: confirm the data frequencies of different devices in the IoT device, and confirm the data inspection frequency based on the confirmed data frequency, and generate a frequency sorting sequence of the devices inside the corresponding IoT device; Step 2: Based on the frequency sorting sequence confirmed between several devices in the IoT device, based on the distance arrangement between different devices in the frequency sorting sequence and the arrangement of data inspection frequency, the frequency sorting sequence is adjusted for data segments, and the standard data column and the qualitative data column are confirmed; Step 3: reordering the several groups of equipment associated with the standard data column, and simultaneously confirming the equipment value tables associated with the several groups of equipment, assigning values ​​to the confirmed several groups of equipment sequences based on the reordered equipment sequence and the equipment value table, and selecting the best sequence from the several groups of equipment sequences based on the generated total value; Step 4: Based on the standard data column associated with the optimal sequence, the optimal sequence is re-filled to the same position based on the location of the standard data column, and the frequency sorted sequence after the filling process is used as the control sequence, and the inspection robot is controlled so that the inspection robot inspects and collects the equipment associated with the control sequence in turn.

2. A data processing method based on electronic information technology of big data according to claim 1, characterized in that: In step 1, the specific sub-steps of generating the frequency sorting sequence are: S11. Confirm the data frequency of different devices from the cloud, and mark the confirmed data frequency as P i , where i represents different devices, and P i All are preset values; S12, generating frequency P according to the confirmed data sets i , using XJ i =P i ×C1 confirms data inspection frequency XJ i , where C1 is the preset value, and the data inspection frequency XJ confirmed by different devices i Sort by value from small to large, confirm the frequency sorting sequence, if there is the same XJ i , then the front and back can be randomly sorted.

3. The data processing method based on electronic information technology of big data according to claim 1 is characterized in that: In step 2, the specific sub-steps of confirming the standard data column and the qualitative data column are: S21, confirm the data inspection frequencies associated with the previous and next groups from the confirmed frequency sorting sequence, and mark the data inspection frequencies associated with the previous group as X1 J i , the data inspection frequency associated with the latter group is calibrated as X2J i , confirm the associated device based on the associated tag i, and determine the moving distance L between the two groups of devices from the IoT device model k , its IoT device model is the preset model, and then based on the preset patrol robot movement speed V, its associated movement time T is determined k , where T k =L k ÷V, identify the X1 J associated with the two groups of devices before and after i and X2J i The numerical relationship of: If X1 J i +T k <X2J i , mark the front and back two groups of equipment as pending equipment; If X1 J i +T k =X2J i , the data segments associated with the two groups of devices are marked as qualitative data columns, and the data segments are the corresponding {X1 J i 、X2J i }, the time associated with this part of the data segment is a qualitative relationship, and other time cannot be inserted or adjusted. That is, after the robot inspects the previous set of equipment, it passes through the road section and can just inspect the next set of equipment. In this case, it is marked as a qualitative data column without any adjustment; If X1 J i +T k <X2J i , then the data inspection frequency associated with the latter group of devices in the two groups is removed from the frequency sorting sequence, and the same processing method in step 2 is used again for the removed data inspection frequency to reconfirm the standard data column and the qualitative data column; S22, based on the associated pending device, using the data segment {X1 J i 、X2J i } as the determination benchmark, the behavior time between the devices that appear successively after the device to be determined is determined based on the frequency sorting sequence, and is marked as: T k1 , T k2 ,……,T kn , if X1 J i +T k +T k1 >X2J i , then the data segment corresponding to the data inspection frequency associated with the pending device is marked as a qualitative data column; If X1 J i +T k +T k1 =X2J i , then the group of devices after the pending device is divided into the pending device, and the data segments corresponding to the data inspection frequencies associated with the three groups of devices are marked as standard data columns; If X1 J i +T k +T k1 <X2J i , then use X1 J i +T k +T k1 +……+T kj =Tz confirms its time characteristic value Tz, where j∈[2,n], so that j gradually increases and determines the corresponding time characteristic value Tz to Tz≥X2J i When the time stops, k1 To T k(j-1) The associated devices are classified into pending devices, and the data segments corresponding to the data inspection frequencies associated with these devices are marked as standard data columns.

4. A data processing method based on electronic information technology of big data according to claim 3, characterized in that: In step S21, the method for confirming the moving distance between the front and rear groups of devices includes: Take one set of devices as the starting point and another set of devices as the end point, and generate several moving routes based on the confirmed starting point and end point, and then select a set of minimum moving distance L k min is taken as the selected route, and the moving distance of the selected route is taken as the moving distance L between the two groups of devices. k , where k represents the moving distance between different devices.

5. The data processing method based on electronic information technology of big data according to claim 1 is characterized in that: In step 3, the specific sub-steps for selecting the best sequence are: S31. Based on several groups of devices and IoT device models associated with the standard data column, a group of devices is randomly selected from several groups of devices as the main device, and other devices in the several groups of devices are used as the secondary devices of the main device. The distance between the main device and different secondary devices is identified. The distance between the main device and different secondary devices is consistent with the method of determining the moving distance between devices in step S21. The determined different distances are calibrated as Lq, where q represents different secondary devices. The secondary devices are then sorted in an ascending order according to Lq, so as to obtain an associated sorting sequence belonging to the main device, and the associated assignment Fo at different positions in the associated sorting sequence is determined, where Fo=o×C2, where C2 is a preset fixed coefficient factor, and o represents different positions. The associated sorting sequence with the associated assignment is used as the device assignment table corresponding to the main device, and this method is repeated to confirm the device assignment table of each group of devices in turn. S32, confirm the pending devices associated with the corresponding standard data column, use the first device of the pending devices as the first group of devices in the device sequence, use the last device of the pending devices as the last group of devices in the device sequence, and randomly sort the other devices between the first device and the last device, and confirm several groups of device sequences based on the random arrangement; S33, confirming the total value assigned to each group of equipment sequence: starting from the first group of equipment in the equipment sequence, identifying the position of the next group of equipment in the value assignment table of the previous group of equipment, and confirming the associated value Fo corresponding to the position, confirming the Fo associated with each subsequent group of equipment in turn, summing up the confirmed groups of Fo, and confirming the total value Zp, where p represents different equipment sequences; Based on the different total assignments Zp confirmed by different equipment sequences, the equipment sequence associated with Zpmin is calibrated as the optimal sequence, and the optimal sequence associated with each set of standard data columns is confirmed one by one.

6. An intelligent inspection and collection system for Internet of Things devices, the collection system is used to execute a data processing method based on big data electronic information technology as described in any one of claims 1 to 5, characterized in that: include: The initial sequence generation end first confirms the data frequencies of different devices in the IoT device, and confirms the data inspection frequency based on the confirmed data frequency, and generates a frequency sorting sequence for the devices inside the corresponding IoT device; The data column classification end adjusts the data segments of the frequency sorting sequence based on the frequency sorting sequence confirmed between several devices in the IoT device, based on the distance arrangement between different devices in the frequency sorting sequence and the arrangement of data inspection frequency, and confirms the standard data column and the qualitative data column; The sequence optimization end reorders the several groups of equipment associated with the standard data column, and simultaneously confirms the equipment value tables associated with the several groups of equipment. Based on the reordered equipment sequence and the equipment value table, the confirmed several groups of equipment sequences are assigned values, and based on the generated total value, the best sequence is selected from the several groups of equipment sequences. The control execution end re-fills the best sequence to the same position based on the location of the standard data column, uses the frequency sorted sequence after filling as the control sequence, and controls the inspection robot. The inspection robot collects data from the associated devices in turn and transmits the collected relevant data to the cloud for storage.

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