Waveform data matching method and device, computer device, and storage medium

Abstract

The present application relates to the technical field of computer, disclose waveform data matching method, device, computer equipment and storage medium, including: obtaining waveform expression, waveform total starting time and first waveform attribute information;Waveform expression is analyzed, and the calculation tree is generated, wherein the calculation tree includes at least one node, and the node information of each node;When determining that the calculation tree includes at least one node of the type to be updated according to the node information of each node, the update operation corresponding to the type to be updated is executed on the calculation tree, and the updated calculation tree is obtained;Hash calculation is carried out on the updated calculation tree, and the target hash value is obtained;In the preset storage location, the waveform data matched with the target hash value and the first waveform attribute information are found;Waveform data and waveform total starting time are sent to wave generating equipment, to generate microwave pulse.The present application can reduce the problem of resource waste.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to waveform data matching methods, apparatus, computer equipment, and storage media. Background Technology

[0002] In the field of computer technology, when experimental equipment performs each waveform generation task, it first needs to obtain the waveform expression and waveform attribute information, and then send the waveform expression and waveform attribute information to the waveform parsing device. The waveform parsing device generates waveform data based on the waveform expression and waveform attribute information, and sends it to the waveform generation device. The waveform generation device can then generate the corresponding microwave pulse based on the received waveform data.

[0003] However, since the same waveform may be generated in different waveform generation tasks, if the waveform parsing device generates waveform data based on the waveform expression and transmits it to the waveform generator every time, it will lead to a waste of resources. Summary of the Invention

[0004] In view of this, the present invention provides a waveform data matching method, apparatus, computer equipment, storage medium, and program product to solve the problem of resource waste.

[0005] In a first aspect, the present invention provides a waveform data matching method, the method comprising:

[0006] Obtain the waveform expression, the total start time of the waveform, and the attribute information of the first waveform;

[0007] The waveform expression is parsed to generate a computation tree, wherein the computation tree includes at least one node and node information for each node;

[0008] When it is determined, based on the node information of each node, that the computation tree contains at least one type of node to be updated, perform the update operation corresponding to the type to be updated on the computation tree, and obtain the updated computation tree;

[0009] Perform a hash calculation on the updated computation tree to obtain the target hash value;

[0010] Search for waveform data that matches both the target hash value and the first waveform attribute information in the preset storage location;

[0011] The waveform data and the total start time of the waveform are sent to the wave generation device to generate microwave pulses.

[0012] The waveform data matching method provided by this invention has the following advantages:

[0013] Upon obtaining the waveform expression and the first waveform attribute information, the waveform expression is first parsed to obtain a computational tree, which includes at least one node and its corresponding node information. First, based on the node information of each node, if it is determined that the computational tree contains a node of the type to be updated, the corresponding update operation is performed on the computational tree to obtain the updated computational tree. Then, a hash calculation is performed on the updated computational tree to obtain the target hash value. If waveform data matching both the target hash value and the first waveform attribute information can be found in a preset storage location, it is not necessary to generate waveform data based on the node information of each node. Instead, the waveform data matching the target hash value can be directly read from the preset storage location and a microwave pulse can be generated. In this way, for the same waveform, the waveform parsing device does not need to repeatedly generate waveform data; instead, it uses the previously generated waveform data to generate the microwave pulse, reducing the computational resources required for generating waveform data. In addition, since the impact of nodes of the type to be updated on waveform data can be removed in a corresponding way, and the waveform data can be adjusted again in the future based on the total start time, by performing the update operation corresponding to the type to be updated on the computation tree, the probability that the updated computation tree has the same structure as the computation tree generated based on the preceding waveform expression can be increased, thereby making the calculated hash value the same, increasing the probability of finding waveform data, and further reducing the resources occupied by generating waveform data.

[0014] In one optional implementation, the nodes in the computation tree of the type to be updated are waveform nodes; when it is determined, based on the node information of each node, that the computation tree includes at least one type of node to be updated, an update operation corresponding to the type to be updated is performed on the computation tree, and the updated computation tree is obtained, including:

[0015] Select the minimum start time from the start time included in the node information of each waveform node in the computation tree;

[0016] Based on the minimum start time, the start time included in the node information of each waveform node in the computation tree is updated to obtain the updated computation tree.

[0017] Specifically, if two waveform expressions differ only in their total start time but are identical in all other information, and the hash value of the computation tree corresponding to the two waveform expressions needs to be determined subsequently, in order to reduce the resources consumed in generating waveform data, the computation tree can be processed so that the total start time corresponding to each waveform expression starts from a fixed time. This ensures that the computation trees corresponding to waveform expressions that differ only in their start time are identical, thus making the calculated hash values ​​identical as well. This improves the efficiency of finding waveform data later and can greatly reduce the resource waste caused by generating waveform data.

[0018] In one optional implementation, the nodes of the type to be updated in the computational tree include waveform nodes, translation operator nodes, and translation length nodes. Multiple nodes in the computational tree are arranged from highest to lowest level. The next level of the translation operator node includes translation length nodes, and the subordinate levels of the translation operator node include waveform nodes. When it is determined, based on the node information of each node, that the computational tree includes at least one type of node to be updated, an update operation corresponding to the type to be updated is performed on the computational tree, and the updated computational tree is obtained, including:

[0019] Based on the node information of each translation operator node and the node information of the translation length nodes included in the next level of the translation operator node, determine the time adjustment parameters of the waveform nodes included in the subordinate level of each translation operator node.

[0020] The node information of the target waveform node is updated according to the time adjustment parameter of the target waveform node, where the target waveform node is any waveform node in the computation tree;

[0021] Select the minimum start time from the start time included in the updated node information of each waveform node in the computation tree;

[0022] Based on the minimum start time, update the start time included in the updated node information of each waveform node in the computation tree;

[0023] Remove all translation operator nodes and all translation length nodes from the computation tree to obtain the updated computation tree.

[0024] Specifically, the computation tree corresponding to a waveform expression includes waveform nodes, translation operator nodes, and translation length nodes. The translation operator and translation length affect the start time of the waveform they affect. Therefore, the waveform parsing device processes these types of nodes in the computation tree, moving the time offset caused by the translation operation into the waveform function, standardizing the start time within the waveform, and adjusting the waveform's start time to a fixed time. This ensures that the computation trees corresponding to waveform expressions that differ only in start time and translation length are identical, resulting in identical hash values. This improves the efficiency of subsequently finding waveform data and significantly reduces the resource waste caused by generating waveform data.

[0025] In one alternative implementation, multiple nodes of the computation tree are arranged in descending order of hierarchy. The computation tree includes multiplication operator nodes and coefficient nodes, and the next level of the hierarchy to which the multiplication operator node belongs includes coefficient nodes.

[0026] The method also includes:

[0027] Determine whether the updated computation tree includes multiplication operator nodes that meet preset conditions. Multiplication operator nodes that meet preset conditions are multiplication operator nodes that perform multiplication operations on the node information of all waveform nodes in the updated computation tree.

[0028] When it is determined that the updated computation tree includes multiplication operator nodes that meet the preset conditions, the multiplication operator nodes that meet the preset conditions, as well as the coefficient nodes included in the next level of the level to which the multiplication operator nodes that meet the preset conditions belong, are removed from the updated computation tree to obtain a newly updated computation tree, which is used to calculate the target hash value.

[0029] Obtain the first waveform coefficient values ​​of the updated computation tree;

[0030] Search for a second waveform coefficient value that matches both the target hash value and the first waveform attribute information in a preset storage location;

[0031] The third waveform coefficient value is determined based on the first waveform coefficient value and the second waveform coefficient value;

[0032] When it is determined that the value of the third waveform coefficient is not the preset value, the waveform data is adjusted according to the value of the third waveform coefficient to obtain the adjusted waveform data, which is then sent to the wave generation device together with the total start time.

[0033] Specifically, the values ​​included in the coefficient nodes affect the amplitude of the waveform data. Therefore, by further extracting the waveform coefficient values, the computation trees corresponding to waveform expressions with the same other information but different waveform coefficient values ​​can be identical, thus ensuring that the calculated hash values ​​are the same and increasing the probability of finding the waveform data. Subsequent adjustments to the waveform data can then be made using the waveform coefficient values. This adjustment involves only multiplication operations, which is simpler than directly generating waveform data from the computation tree. This approach ensures that the waveform data conforms to the waveform expression while reducing the resources wasted in generating waveform data from the computation tree.

[0034] In one optional implementation, obtaining the first waveform coefficient values ​​of the updated computation tree includes:

[0035] Extract waveform coefficient values ​​from the node information of the coefficient nodes in the next level of the level to which the multiplication operator node belongs that meets the preset conditions;

[0036] The first waveform coefficient value is determined based on each extracted waveform coefficient value.

[0037] In one optional implementation, determining whether the updated computation tree includes multiplication operator nodes that meet preset conditions includes:

[0038] When the target node at the highest level of the updated computation tree is determined to be a multiplication operator node, and the node at the next level below the highest level includes a coefficient node, the target node is determined to meet the preset conditions.

[0039] or,

[0040] If the target node is determined not to be a multiplication operator node, the updated computation tree is determined not to include multiplication operator nodes that meet the preset conditions.

[0041] In one optional implementation, searching for waveform data that matches both the target hash value and the first waveform attribute information in a preset storage location includes:

[0042] Determine whether the preset storage location includes a hash value that matches the target hash value;

[0043] When it is determined whether the preset storage location includes a hash value that is consistent with the target hash value, the second waveform attribute information corresponding to the hash value is read from the preset storage location.

[0044] Determine whether the first waveform attribute information and the second waveform attribute information are consistent;

[0045] When it is determined that the first waveform attribute information is consistent with the first waveform attribute information, the waveform data corresponding to the hash value is read from the preset storage location.

[0046] In a second aspect, the present invention provides a waveform data matching device, the device comprising:

[0047] The acquisition module is used to acquire the waveform expression, the total start time of the waveform, and the attribute information of the first waveform;

[0048] The parsing module is used to parse the waveform expression and generate a computation tree, wherein the computation tree includes at least one node and node information for each node;

[0049] The update module is used to perform an update operation corresponding to the type to be updated on the computation tree when it is determined, based on the node information of each node, that the computation tree includes at least one type of node to be updated, and to obtain the updated computation tree.

[0050] The calculation module is used to perform hash calculations on the updated calculation tree to obtain the target hash value;

[0051] The search module is used to search for waveform data that matches both the target hash value and the first waveform attribute information in a preset storage location;

[0052] The transmitting module is used to send waveform data and the total start time of the waveform to the wave generation device to generate microwave pulses.

[0053] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the waveform data matching method of the first aspect or any corresponding embodiment described above.

[0054] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the waveform data matching method of the first aspect or any corresponding embodiment thereof.

[0055] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the waveform data matching method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the architecture of a computer system according to an embodiment of the present invention;

[0058] Figure 2 This is a flowchart illustrating the waveform data matching method according to an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the structure of a computation tree according to an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of another computation tree structure according to an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of another computation tree structure according to an embodiment of the present invention;

[0062] Figure 6 This is a schematic diagram of another computation tree structure according to an embodiment of the present invention;

[0063] Figure 7 This is a flowchart illustrating another waveform data matching method according to an embodiment of the present invention;

[0064] Figure 8 This is a schematic diagram of waveform data stream according to an embodiment of the present invention;

[0065] Figure 9 This is a structural block diagram of a waveform data matching device according to an embodiment of the present invention;

[0066] Figure 10 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] The embodiments of the present invention can be applied to computer systems, such as... Figure 1 As shown, the computer system may include a wave resolving device, a wave generating device, and an experimental computer. Both the wave resolving device and the wave generating device are instrument-level devices. For example, the wave generating device may be an Arbitrary Waveform Generator (AWG). The experimental computer is used to transmit pre-constructed waveform indication information to the wave resolving device. The wave resolving device can parse the received waveform indication information, obtain the corresponding waveform data, and send it to the wave generating device, which then generates microwave pulses based on the waveform data.

[0069] In the field of computer technology, quantum computing is a novel computing technology with high computational efficiency. Currently, superconducting qubits are one of the mainstream approaches to realizing quantum computers. In the laboratory, wave generation equipment typically generates relevant microwave pulses based on waveform data sent by waveform analysis equipment to drive the flipping and reading of qubits, thereby realizing information transmission.

[0070] This invention provides a waveform data matching method. After parsing and updating the waveform expression, a computation tree is obtained, and a hash value is calculated on the computation tree. Then, the matching waveform data is found by using the hash value and the first waveform attribute information. In this way, it is not necessary to regenerate the waveform data each time, but the previously generated waveform data can be used, which reduces the resources occupied by generating waveform data.

[0071] According to an embodiment of the present invention, a waveform data matching method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0072] This embodiment provides a waveform data matching method, which can be executed by the aforementioned waveform parsing device. Figure 2 This is a flowchart of a waveform data matching method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0073] Step S201: Obtain the waveform expression, the total start time of the waveform, and the first waveform attribute information.

[0074] The first waveform attribute information may include indications such as whether it is in IQ two-channel format, whether time-domain data has been generated, and whether correction has been performed. IQ two-channel format is a form used to represent complex signals.

[0075] Specifically, technicians can construct waveform indication information on the experimental computer, which then sends the waveform indication information to the waveform analysis device. Since waveform data (information such as waveform amplitude at multiple time points, or waveform amplitude at multiple frequencies) is large in volume, directly generating waveform data from the experimental computer based on the waveform indication information and sending it to the waveform analysis device would consume significant network resources. This solution sends the waveform indication information (including the aforementioned waveform expression and first waveform attribute information) to the waveform analysis device. Since the waveform expression has a smaller data volume compared to the waveform data, this reduces the waste of network resources.

[0076] After obtaining the waveform expression, the waveform parsing device can extract the start time of each waveform function included in the waveform expression and take the minimum start time as the total start time of the waveform. For example, the waveform expression can be "Gaussian[0.8, 0.5, 1.0, 0.0, 0.0]'+Rect[3.0, 2, 0.5, 0.0, 1.0]<<2", and the total start time can be "0.8".

[0077] Step S202: Parse the waveform expression and generate a computation tree.

[0078] The computation tree can include at least one node and node information for each node, with multiple nodes arranged hierarchically from highest to lowest. Nodes can be operator nodes, waveform nodes, or numerical nodes. Operator nodes can be translation operator nodes, addition (+) operator nodes, subtraction (-) operator nodes, multiplication (*) operator nodes, division ( / ) operator nodes, unary negative operator nodes, differentiation (') operator nodes, mixing (@) operator nodes, or conjugate (#) operator nodes. Numerical nodes can be translation length nodes, coefficient nodes, etc. Translation operator nodes can be left translation operator (<<) nodes or right translation operator (>>) nodes. Waveform node information can include waveform function identification information and at least one waveform parameter value. Waveform parameter values ​​can be start time, duration, amplitude, etc. Operator node information can include operator identification information and child node identification information. Numerical node information can include numerical values, such as floating-point, integer, or complex numbers.

[0079] Specifically, the waveform analysis device can parse waveform indication information according to preset analysis rules to obtain at least one node and node information for each node. Then, according to preset arrangement rules and the node information of each node, it arranges each node hierarchically to obtain a computation tree. For example, the preset analysis rules may include shift-reduce. The preset arrangement rules may include the priority of each string, which includes the identifier information of the operator and the identifier information of the waveform function. Furthermore, the preset arrangement rules may specify that the priority of numerical values ​​is the lowest.

[0080] Step S203: When it is determined that the computation tree includes at least one type of node to be updated based on the node information of each node, perform an update operation corresponding to the type to be updated on the computation tree and obtain the updated computation tree.

[0081] Specifically, if two waveform expressions differ only in their total start time but are identical in all other information, and the hash value of the computation tree corresponding to the two waveform expressions needs to be determined subsequently, in order to reduce the resources consumed in generating waveform data, the computation tree can be processed so that the total start time corresponding to each waveform expression starts from a fixed time.

[0082] A waveform expression typically includes at least a waveform function and its corresponding waveform parameter values. In some cases, it may also include a translation operator and its corresponding translation length. The translation operator and translation length affect the start time in the waveform parameter values ​​of the waveform function it applies to. The waveform function corresponds to a waveform node in the computation tree, the translation operator corresponds to a translation operator node, and the translation length corresponds to a translation length node. Therefore, waveform parsing devices can process these types of nodes in the computation tree, moving the time offset caused by the translation operation into the waveform function, and standardizing the start time within the waveform function so that waveform functions with only different start times can have the same start time. Specific processing can include the following two cases:

[0083] Case 1: The node in the computation tree that needs to be updated is a waveform node.

[0084] Step 1: Select the minimum start time from the start time included in the node information of each waveform node in the computation tree.

[0085] Step 2: Based on the minimum start time, update the start time included in the node information of each waveform node in the computation tree to obtain the updated computation tree.

[0086] Specifically, the waveform analysis device can determine the minimum start time based on the start time included in the updated node information of each waveform node. For the start time of each waveform node, the waveform analysis device can subtract the minimum start time from the start time of that waveform node to obtain the latest start time of that waveform node. In this way, the start time of the node information of each waveform node can be updated again.

[0087] Example 1: The waveform expression is Gaussian[0.8, 0.5, 1.0, 0.0, 0.0], where the start time of the Gaussian waveform is "0.8" and the minimum start time is "0.8". After updating the start time included in the node information of the Gaussian waveform node according to the minimum start time, the waveform expression corresponding to the obtained computation tree is Gaussian[0, 0.5, 1.0, 0.0, 0.0].

[0088] Example 2: The waveform expression is Gaussian[0.8, 0.5, 1.0, 0.0, 0.0] + Rect[3.0, 2, 0.5, 0.0, 1.0]. The starting time of the Gaussian waveform is "0.8", the starting time of the rectangular square wave is "3.0", and the minimum starting time is "0.8". After updating the starting time included in the node information of each waveform node according to the minimum starting time, the waveform expression corresponding to the obtained computation tree is Gaussian[0, 0.5, 1.0, 0.0, 0.0] + Rect[2.2, 2, 0.5, 0.0, 1.0].

[0089] Scenario 2: The nodes in the computation tree that are to be updated include waveform nodes, translation operator nodes, and translation length nodes. Multiple nodes in the computation tree are arranged from high to low level. The next level of the level to which the translation operator node belongs includes translation length nodes, and the subordinate levels of the translation operator node include waveform nodes.

[0090] Step 1: Based on the node information of each translation operator node and the node information of the translation length nodes included in the next level of the translation operator node, determine the time adjustment parameters of the waveform nodes included in the subordinate level of each translation operator node.

[0091] Step 2: Update the node information of the target waveform node according to the time adjustment parameters of the target waveform node.

[0092] The target waveform node is any waveform node in the computation tree.

[0093] Step 3: Select the minimum start time from the start time included in the updated node information of each waveform node in the computation tree.

[0094] Step 4: Update the start time of each waveform node in the computation tree according to the minimum start time.

[0095] Step 5: Remove all translation operator nodes and all translation length nodes from the computation tree to obtain the updated computation tree.

[0096] Specifically, for each path in the computation tree, the waveform analysis device can traverse each node of the computation tree from high to low level to determine the time adjustment parameters for each level. Upon traversing a node, the type of the current node can be extracted from its node information. When the current node is determined to be a translation operator node based on its type, the time adjustment parameters of the previous level corresponding to the current node can be updated based on the node information of the translation length node in the next level below the current node. This yields the time adjustment parameters corresponding to the current level. When the current level is the highest level, the time adjustment parameters corresponding to the previous level are preset times.

[0097] Specifically, the waveform analysis device can extract the translation length from the node information of the next-level translation length node, and calculate the time adjustment parameter of the current level based on the translation direction, translation length, and time adjustment parameter of the previous level corresponding to the translation operator. For example, if the current node is a right shift operator, the translation length can be added to the time adjustment parameter of the previous level to obtain the time adjustment parameter of the current level; if the current node is a left shift operator, the translation length can be subtracted from the time adjustment parameter of the previous level to obtain the time adjustment parameter of the current level.

[0098] Alternatively, if the current node is determined not to be a translation operator node based on its type, the time adjustment parameter of the previous level can be used as the time adjustment parameter of the current level. The time adjustment parameter of each waveform node in each path is the time adjustment parameter corresponding to the level in which that waveform node belongs.

[0099] Then, the waveform analysis device can update the start time included in the target waveform node according to the time adjustment parameters of the target waveform node, where the target waveform node is any waveform node in the computation tree. For example, the sum of the start time included in the target waveform node and the time adjustment parameters corresponding to the level to which the target waveform node belongs is determined, and this sum is updated as the new start time in the node information of the target waveform node to complete the update operation of the node information of the target waveform node.

[0100] Finally, the waveform analysis device can determine the minimum start time based on the start time included in the updated node information of each waveform node. For the start time of each waveform node, the waveform analysis device can subtract the minimum start time from the start time of the waveform node to obtain the latest start time of the waveform node. In this way, the start time of the node information of each waveform node can be updated again.

[0101] The waveform parsing device can remove all translation operator nodes and all translation length nodes from the computation tree. When a translation operator node's child nodes include waveform nodes, the waveform child nodes can be moved to the translation operator node's position, resulting in an updated computation tree.

[0102] For example, the waveform expression is (Gaussian[0.8, 0.5, 1.0, 0.0, 0.0] + Rect[3.0, 2, 0.5, 0.0, 1.0] << 3.5) >> 2, and its computation tree can be as follows: Figure 3 As shown. For each path in the computation tree, the first level has a translation operator, and the time adjustment parameter for the first level is calculated as 0 + 2 = 2. The second level does not have a translation operator, but inherits the time adjustment parameter from the previous level, so the time adjustment parameter for the second level is +2. The time adjustment parameter corresponding to the Gaussian waveform node is the time adjustment parameter for the second level, and the start time of the Gaussian waveform node is adjusted by 0.8 + 2 = 2.8. The second node of the third level has a translation operator, and the time adjustment parameter for the third level is calculated as 2 - 3.5 = -1.5. The fourth level does not have a translation operator, but it can inherit the time adjustment parameter from the previous level, so the time adjustment parameter for the fourth level is -1.5. The start time of the rectangular square wave node in the fourth level is adjusted by 3 - 1.5 = 1.5. The minimum start time of the two waveform nodes is "1.5", so the start time of the rectangular square wave node is updated to "0" based on the minimum start time, and the start time of the Gaussian wave node is updated to 2.8 - 1.5 = 1.3. After removing the translation operator node and the translation length node, the updated computation tree is obtained as follows: Figure 4 As shown, the corresponding waveform expression can be Gaussian[1.3, 0.5, 1.0, 0.0, 0.0] + Rect[0, 2, 0.5, 0.0, 1.0].

[0103] Step S204: Perform hash calculation on the updated computation tree to obtain the target hash value.

[0104] Specifically, the waveform analysis device can input the node information of all nodes in the updated computational tree as a whole into a preset hash algorithm to obtain the target hash value. Alternatively, the waveform analysis device can input the node information of each node in the updated computational tree into the preset hash algorithm to obtain a hash value corresponding to each node. Then, the hash values ​​are arranged according to the hierarchy of each node to obtain a fourth hash value sequence. Finally, the fourth hash value sequence is hashed to obtain the target hash value.

[0105] Step S205: Search for waveform data that matches both the target hash value and the first waveform attribute information in the preset storage location.

[0106] The preset storage location can be the cache of the waveform analysis device.

[0107] Specifically, waveform analysis devices can locate waveform data by following these steps:

[0108] Determine whether the preset storage location contains a hash value that matches the target hash value. If it is determined that the preset storage location contains a hash value that matches the target hash value, read the second waveform attribute information corresponding to the hash value from the preset storage location. Determine whether the first waveform attribute information and the second waveform attribute information are consistent. If it is determined that the first waveform attribute information and the second waveform attribute information are consistent, read the waveform data corresponding to the hash value from the preset storage location.

[0109] Specifically, the waveform analysis device first determines whether a hash value matching the target hash value exists in the preset storage location. If not, waveform data can be generated based on the updated computation tree. If it exists, the device can search for first waveform attribute information matching the target hash value in the preset storage location. Then, it can determine whether the first waveform attribute information matching the target hash value is consistent with the first waveform attribute information obtained in step S201 (specifically, it can determine whether each of the included indication information items is consistent). If they are consistent, the device can search for waveform data matching the target hash value in the preset storage location (this waveform data matches the first waveform attribute information). If they are inconsistent, waveform data can be generated again based on the updated computation tree.

[0110] In some alternative implementations, the above operations can also be performed by a wave generating device. In this way, the wave generating device can directly use the found waveform data and the total waveform start time to generate microwave pulses without transmitting the waveform data and the total waveform start time, thus saving network transmission resources.

[0111] Step S206: Send the waveform data and the total start time of the waveform to the wave generation device.

[0112] Specifically, once waveform data matching both the target hash value and the first waveform attribute information is found, the waveform data and the total waveform start time can be sent to the wave generation device. Upon receiving the waveform data and the total waveform start time, the wave generation device can then generate a microwave pulse based on the waveform data at the time corresponding to the total waveform start time.

[0113] In some optional implementations, the computation tree may also include multiplication operator nodes and coefficient nodes. The child nodes of the next level of the multiplication operator node include coefficient nodes. The values ​​included in the coefficient nodes affect the amplitude of the waveform data. In order to improve the probability of finding the waveform data later, the influence of the coefficients on the waveform data can be removed, and the corresponding waveform coefficient values ​​can be extracted for subsequent calculations. Accordingly, the waveform parsing device may also perform the following steps:

[0114] Step 1: Determine whether the updated computation tree includes multiplication operator nodes that meet the preset conditions.

[0115] Among them, the multiplication operator node that meets the preset conditions is the multiplication operator node that performs multiplication operations on the node information of all waveform nodes in the updated computation tree.

[0116] Specifically, if the target node at the highest level of the updated computation tree is determined to be a multiplication operator node, and the nodes at the next level below the highest level include coefficient nodes, then the target node is determined to meet a preset condition. Alternatively, if the target node is determined not to be a multiplication operator node, then the updated computation tree does not include multiplication operator nodes that meet the preset condition.

[0117] Step 2: When it is determined that the updated computation tree includes multiplication operator nodes that meet the preset conditions, the multiplication operator nodes that meet the preset conditions, as well as the coefficient nodes included in the next level of the level to which the multiplication operator nodes that meet the preset conditions belong, are removed from the updated computation tree to obtain a newly updated computation tree, which is used to calculate the target hash value.

[0118] Step 3: Obtain the first waveform coefficient values ​​of the updated computation tree.

[0119] Specifically, the waveform analysis device can extract waveform coefficient values ​​from the node information of coefficient nodes in the next level of the level to which the multiplication operator node belongs, provided that the multiplication operator node meets preset conditions. Based on each extracted waveform coefficient value, a first waveform coefficient value is determined. For example, the level of each waveform coefficient value is determined as the first waveform coefficient value.

[0120] Step 4: Locate the second waveform coefficient value that matches both the target hash value and the first waveform attribute information in the preset storage location.

[0121] Step 5: Determine the third waveform coefficient value based on the first waveform coefficient value and the second waveform coefficient value.

[0122] Specifically, the waveform analysis device can determine the ratio of the first waveform coefficient value and the second waveform coefficient value as the third waveform coefficient value.

[0123] Step 6: When it is determined that the value of the third waveform coefficient is not the preset value, the waveform data is adjusted according to the value of the third waveform coefficient to obtain the adjusted waveform data, which is then sent to the wave generation device together with the total start time.

[0124] The preset value can be 1.

[0125] For example, the waveform expression is 3*((Gaussian[0.8, 0.5, 1.0, 0.0, 0.0]+Rect[3.0, 2, 0.5, 0.0, 1.0]<<5)>>2), and its corresponding computation tree can be as follows: Figure 5 As shown, when the first node is the "*" operator and its corresponding child nodes include coefficient nodes, the coefficient "3" is extracted, and "3" and "*" are removed. After combining this with the update operation in step S203, the updated computation tree can be obtained as follows. Figure 4 As shown, the corresponding waveform expression can be Gaussian[0, 0.5, 1.0, 0.0, 0.0]+Rect[2.2, 2, 0.5, 0.0, 1.0]<<5)>>2, and the extracted first waveform coefficient value is "3".

[0126] In some alternative implementations, the waveform analysis device may only perform the computation tree update operation corresponding to the multiplication operator node that meets the preset conditions, and not perform the above-mentioned computation tree update operation corresponding to the translation operator node.

[0127] For example, the waveform expression is 3*((Gaussian[0.8, 0.5, 1.0, 0.0, 0.0]+Rect[3.0, 2, 0.5, 0.0, 1.0]<<5)>>2), and its corresponding computation tree is as follows: Figure 5 As shown, the extracted waveform coefficient value is "3", and the updated computation tree is as follows. Figure 6 As shown, the corresponding waveform expression is Gaussian[0.8, 0.5, 1.0, 0.0, 0.0] + Rect[3.0, 2, 0.5, 0.0, 1.0] << 5) >> 2.

[0128] The waveform data matching method provided in this embodiment, upon obtaining the waveform expression and the first waveform attribute information, first parses the waveform expression to obtain a computational tree, which includes at least one node and its corresponding node information. Firstly, based on the node information of each node, if it is determined that the computational tree contains a node of the type to be updated, the corresponding update operation is performed on the computational tree to obtain the updated computational tree. Then, a hash calculation is performed on the updated computational tree to obtain the target hash value. If waveform data matching both the target hash value and the first waveform attribute information can be found in a preset storage location, it is not necessary to generate waveform data based on the node information of each node. Instead, the waveform data matching the target hash value can be directly read from the preset storage location and a microwave pulse can be generated. In this way, for the same waveform, the waveform parsing device does not need to repeatedly generate waveform data, but instead uses the previously generated waveform data to generate the microwave pulse, reducing the computational resources occupied by generating waveform data. In addition, since the impact of nodes of the type to be updated on waveform data can be removed in a corresponding way, and the waveform data can be adjusted again in the future based on the total start time, by performing the update operation corresponding to the type to be updated on the computation tree, the probability that the updated computation tree has the same structure as the computation tree generated based on the preceding waveform expression can be increased, thereby making the calculated hash value the same, increasing the probability of finding waveform data, and further reducing the resources occupied by generating waveform data.

[0129] The waveform data matching method described above is illustrated below with a specific example. The process can be as follows: Figure 7 As shown.

[0130] Technicians can predefine waveform sequence classes (e.g., named "wave sequence") to represent all waveforms in a single transmission channel (i.e., any one of multiple transmission channels between the waveform analysis device and the wave generation device). The waveform sequence class can include a list of waveform expressions (including at least one waveform expression). It can also include waveform attribute information corresponding to each waveform expression. The waveform sequence class can further include a data serialization method. This method can be used to convert the data (waveform data, total waveform time, waveform attribute information, etc.) into binary format for network transmission before transmission, or to deserialize the data after network transmission for subsequent parsing and analysis.

[0131] Additionally, technicians can define virtual AWG classes, which can include the `sendData()` and `receiveData()` functions. The `sendData()` function is used to send data, and the `receiveData()` function is used to receive data. For example... Figure 8As shown, the storage area of ​​a waveform parsing device can be divided into a waveform buffer, a waveform cache, and a waveform distribution channel.

[0132] When the waveform parsing device receives a waveform sequence class via `receive Data()`, it can store the waveform sequence class in a waveform buffer. The waveform parsing device can then process each waveform expression in the waveform buffer to obtain the corresponding waveform data. This waveform data, along with the total start time, is then loaded into the waveform distribution area and sent to the waveform generation device. The specific steps are as follows:

[0133] Step 1: The waveform parsing device can parse each waveform expression in the waveform sequence class to obtain the total start time corresponding to each waveform expression. Then, according to the order of the waveform expressions in the waveform sequence class, and based on the total start time corresponding to each waveform expression, it constructs a delay trigger table corresponding to the waveform sequence class.

[0134] Step two: The waveform parsing device can parse each waveform expression and generate a corresponding computation tree. Then, the computation tree is simplified through the following steps to increase the probability of finding waveform data in the waveform buffer. The simplification operation can specifically include the following steps:

[0135] First, remove the start time of the waveform.

[0136] Step 1: Remove the translation operator node.

[0137] Technicians can predefine the recursive function `_cal_shift(node, op, time)`. Here, `op` represents the shift operator passed from the previous level, and `time` represents the time adjustment parameter transmitted from the previous level. If the current level is the highest level, `op` can be "None", and `time` can be "0". Waveform analysis devices can use this recursive function `_cal_shift(node, op, time)` to traverse each node in the computation tree. When a node is encountered that is a `<<` or `>>` operator node, the recursive function can be called on its waveform child nodes. Specifically, if the encountered node is a `<<` operator node, the shift time of the encountered node needs to be inversely proportional to the `time` parameter passed from the previous level, and then passed to the next level of recursion. That is, `op` = `<<` corresponds to addition, and `op` = `>>` corresponds to subtraction. When the traversed node is a waveform node, the start time in the waveform node's node information can be modified using the "op" and "time" parameters passed from the previous level. In this way, after traversing all nodes, the start time offset caused by the translation operator can be transferred to the start time of the waveform itself.

[0138] After the recursive call to _cal_shift is completed, all nodes are recursively traversed. When a node is encountered that is a "<<" or ">>" operator node, it can be replaced with its waveform child node, and its translation length child node can be removed. In this way, the current entire computation tree does not include translation operator nodes and translation length child nodes.

[0139] Step 2: Recursively traverse the entire computation tree to find the minimum value among the start times of all waveform nodes.

[0140] Step 3: Recursively traverse the entire computation tree, subtracting the minimum value found in the previous step from the start time of each waveform node. This ensures that the start time of the entire waveform expression begins from 0.

[0141] Second, remove all coefficients from the waveform and return the coefficients.

[0142] Technicians can predefine the recursive function `eliminate_coeff(node, coeff = 1.0)`. The waveform analysis device can traverse each node of the latest computational tree. When a node is encountered that uses the "*" operator, the coefficients in its child nodes are multiplied by the `coeff` parameter. This returns the identifier of the encountered node and the latest `coeff` parameter (i.e., the waveform coefficient value), allowing the traversal operation to continue. Based on the returned node identifier, the waveform analysis device can remove the corresponding node from the computational tree, as well as its coefficient child nodes. This results in a computational tree and waveform coefficient values ​​that do not contain the values ​​themselves.

[0143] After the above operations, a simplified computation tree corresponding to each waveform expression can be obtained. At this point, a hash calculation can be performed on each simplified computation tree to obtain a hash value corresponding to each waveform expression.

[0144] Step three: The waveform parsing device can generate a Least Recently Used (LRU) cache as a waveform buffer using the Ordered Dict dictionary in Python. The dictionary keys are the hash values ​​of the simplified computation tree, and the values ​​are Block Wave Data objects. The waveform buffer is used to cache waveform data, waveform attribute information, waveform coefficient values, etc., corresponding to each waveform expression.

[0145] The waveform parsing device can encapsulate the waveform coefficient values ​​and waveform attribute information obtained in the previous step in a Block WaveData class for caching and comparison. For each waveform expression, the waveform parsing device can compare its corresponding hash value with all hash values ​​existing in the cache. If the same hash value exists, it further compares whether the waveform attribute information is consistent. If they are also consistent, it can calculate the quotient of the waveform coefficient value of the waveform and the corresponding waveform coefficient value of the existing waveform data in the cache. If the quotient is 1, the waveform data is directly used as the waveform data corresponding to the waveform expression. If the quotient is not 1, the waveform data in the cache is multiplied by the quotient to obtain the new waveform data as the waveform data corresponding to the waveform expression.

[0146] The waveform parsing device can load the waveform data and delay trigger table corresponding to each waveform expression in the waveform expression list into the corresponding channel of the waveform distribution area and then distribute it to the waveform generation device.

[0147] If a consistent hash value does not exist in the cache, the simplified computation tree can be processed using the current waveform data generation method to generate waveform data. The simplified computation tree's hash value is used as the key, and the corresponding waveform data, waveform coefficient values, and waveform attribute information are stored in the cache for later lookup. Furthermore, the waveform parsing device can multiply the waveform data by the waveform coefficient values ​​to obtain the waveform data corresponding to the waveform expression, and then send it to the waveform generation device.

[0148] This embodiment also provides a waveform data matching device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0149] This embodiment provides a waveform data matching device, such as... Figure 9 As shown, it includes:

[0150] The acquisition module 901 is used to acquire the waveform expression, the total start time of the waveform, and the attribute information of the first waveform;

[0151] The parsing module 902 is used to parse the waveform expression and generate a computation tree, wherein the computation tree includes at least one node and node information for each node;

[0152] The update module 903 is used to perform an update operation corresponding to the type to be updated on the computation tree when it is determined, based on the node information of each node, that the computation tree includes at least one type of node to be updated, and to obtain the updated computation tree.

[0153] Calculation module 904 is used to perform hash calculations on the updated computation tree to obtain the target hash value;

[0154] The lookup module 905 is used to search for waveform data that matches both the target hash value and the first waveform attribute information in a preset storage location;

[0155] In one optional implementation, the node to be updated in the computation tree is a waveform node; the update module 903 is specifically used for:

[0156] Select the minimum start time from the start time included in the node information of each waveform node in the computation tree;

[0157] Based on the minimum start time, the start time included in the node information of each waveform node in the computation tree is updated to obtain the updated computation tree.

[0158] In one optional implementation, the nodes of the type to be updated in the computation tree include waveform nodes, translation operator nodes, and translation length nodes. Multiple nodes in the computation tree are arranged from high to low according to the hierarchy. The next level of the hierarchy to which the translation operator node belongs includes translation length nodes, and the subordinate levels of the translation operator node include waveform nodes.

[0159] Update module 903, specifically for:

[0160] Based on the node information of each translation operator node and the node information of the translation length nodes included in the next level of the translation operator node, determine the time adjustment parameters of the waveform nodes included in the subordinate level of each translation operator node.

[0161] The node information of the target waveform node is updated according to the time adjustment parameter of the target waveform node, where the target waveform node is any waveform node in the computation tree;

[0162] Select the minimum start time from the start time included in the updated node information of each waveform node in the computation tree;

[0163] Based on the minimum start time, update the start time included in the updated node information of each waveform node in the computation tree;

[0164] Remove all translation operator nodes and all translation length nodes from the computation tree to obtain the updated computation tree.

[0165] In one alternative implementation, multiple nodes of the computation tree are arranged in descending order of hierarchy. The computation tree includes multiplication operator nodes and coefficient nodes, and the next level of the hierarchy to which the multiplication operator node belongs includes coefficient nodes.

[0166] Update module 903 is also used for:

[0167] Determine whether the updated computation tree includes multiplication operator nodes that meet preset conditions. Multiplication operator nodes that meet preset conditions are multiplication operator nodes that perform multiplication operations on the node information of all waveform nodes in the updated computation tree.

[0168] When it is determined that the updated computation tree includes multiplication operator nodes that meet the preset conditions, the multiplication operator nodes that meet the preset conditions, as well as the coefficient nodes included in the next level of the level to which the multiplication operator nodes that meet the preset conditions belong, are removed from the updated computation tree to obtain a newly updated computation tree, which is used to calculate the target hash value.

[0169] Obtain the first waveform coefficient values ​​of the updated computation tree;

[0170] Search for a second waveform coefficient value that matches both the target hash value and the first waveform attribute information in a preset storage location;

[0171] The third waveform coefficient value is determined based on the first waveform coefficient value and the second waveform coefficient value;

[0172] When it is determined that the value of the third waveform coefficient is not the preset value, the waveform data is adjusted according to the value of the third waveform coefficient to obtain the adjusted waveform data, which is then sent to the wave generation device together with the total start time.

[0173] In one alternative implementation, the update module 903 is specifically used for:

[0174] Extract waveform coefficient values ​​from the node information of the coefficient nodes in the next level of the level to which the multiplication operator node belongs that meets the preset conditions;

[0175] The first waveform coefficient value is determined based on each extracted waveform coefficient value.

[0176] In one alternative implementation, the update module 903 is specifically used for:

[0177] When the target node at the highest level of the updated computation tree is determined to be a multiplication operator node, and the node at the next level below the highest level includes a coefficient node, the target node is determined to meet the preset conditions.

[0178] or,

[0179] If the target node is determined not to be a multiplication operator node, the updated computation tree is determined not to include multiplication operator nodes that meet the preset conditions.

[0180] In one alternative implementation, the lookup module 905 is specifically used for:

[0181] Determine whether the preset storage location includes a hash value that matches the target hash value;

[0182] When it is determined whether the preset storage location includes a hash value that is consistent with the target hash value, the second waveform attribute information corresponding to the hash value is read from the preset storage location.

[0183] Determine whether the first waveform attribute information and the second waveform attribute information are consistent;

[0184] When it is determined that the first waveform attribute information is consistent with the first waveform attribute information, the waveform data corresponding to the hash value is read from the preset storage location.

[0185] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0186] In this embodiment, the waveform data matching device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0187] This invention also provides a computer device having the above-described features. Figure 9 The waveform data matching device shown.

[0188] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 10 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0189] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware integrated circuit. The aforementioned hardware integrated circuit may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0190] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0191] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0192] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0193] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0194] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0195] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0196] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A waveform data matching method, characterized in that, The method includes: Obtain the waveform expression, the total start time of the waveform, and the attribute information of the first waveform; The waveform expression is parsed to generate a computation tree, wherein the computation tree includes at least one node and node information for each node; When it is determined, based on the node information of each node, that the computation tree includes at least one type of node to be updated, and that at least one type of node to be updated is a waveform node, the minimum start time is selected from the start time included in the node information of each waveform node in the computation tree. Based on the minimum start time, the start time included in the node information of each waveform node in the computation tree is updated to obtain the updated computation tree; Perform a hash calculation on the updated computation tree to obtain the target hash value; Search for waveform data that matches both the target hash value and the first waveform attribute information in a preset storage location; The waveform data and the total start time of the waveform are sent to the wave generation device to generate microwave pulses.

2. The method according to claim 1, characterized in that, When it is determined that the nodes of the type to be updated in the computation tree include waveform nodes, translation operator nodes, and translation length nodes, the multiple nodes in the computation tree are arranged from high to low according to the hierarchy. The next level of the hierarchy to which the translation operator node belongs includes the translation length node, and the subordinate level of the translation operator node includes waveform nodes. The method further includes: Based on the node information of each translation operator node and the node information of the translation length nodes included in the next level of the translation operator node, determine the time adjustment parameters of the waveform nodes included in the subordinate level of each translation operator node; The node information of the target waveform node is updated according to the time adjustment parameter of the target waveform node, wherein the target waveform node is any waveform node in the computation tree; Select the minimum start time from the start time included in the updated node information of each waveform node in the computation tree; Based on the minimum start time, the start time included in the updated node information of each waveform node in the computation tree is updated; The updated computation tree is obtained by removing all translation operator nodes and all translation length nodes from the computation tree.

3. The method according to claim 1 or 2, characterized in that, The multiple nodes of the computation tree are arranged from high to low according to the hierarchy. The computation tree includes multiplication operator nodes and coefficient nodes. The next level of the hierarchy to which the multiplication operator node belongs includes coefficient nodes. The method further includes: Determine whether the updated computation tree includes multiplication operator nodes that meet preset conditions, wherein the multiplication operator nodes that meet the preset conditions are multiplication operator nodes that perform multiplication operations on the node information of all waveform nodes in the updated computation tree; When it is determined that the updated computation tree includes a multiplication operator node that meets the preset conditions, the multiplication operator node that meets the preset conditions, as well as the coefficient nodes included in the next level of the level to which the multiplication operator node that meets the preset conditions belongs, are removed from the updated computation tree to obtain the updated computation tree again, which is used to calculate the target hash value. Obtain the updated first waveform coefficient values ​​of the computation tree; Search the preset storage location for a second waveform coefficient value that matches both the target hash value and the first waveform attribute information; The third waveform coefficient value is determined based on the first waveform coefficient value and the second waveform coefficient value; When it is determined that the third waveform coefficient value is not a preset value, the waveform data is adjusted according to the third waveform coefficient value to obtain the adjusted waveform data, which is then sent to the wave generation device together with the total start time.

4. The method according to claim 3, characterized in that, The step of obtaining the updated first waveform coefficient value of the computation tree includes: Extract waveform coefficient values ​​from the node information of the coefficient nodes in the next level of the level to which the multiplication operator node belongs that meets the preset conditions; The first waveform coefficient value is determined based on each extracted waveform coefficient value.

5. The method according to claim 3, characterized in that, The step of determining whether the updated computation tree includes multiplication operator nodes that meet preset conditions includes: When it is determined that the highest level target node in the updated computation tree is the multiplication operator node, and the nodes in the next level below the highest level include coefficient nodes, the target node is determined to meet the preset conditions. or, If it is determined that the target node is not the multiplication operator node, it is determined that the updated computation tree does not include multiplication operator nodes that meet the preset conditions.

6. The method according to claim 1 or 2, characterized in that, The step of searching for waveform data that matches both the target hash value and the first waveform attribute information in a preset storage location includes: Determine whether the preset storage location includes a hash value that matches the target hash value; When it is determined whether the preset storage location includes a hash value that is consistent with the target hash value, the second waveform attribute information corresponding to the hash value is read from the preset storage location; Determine whether the first waveform attribute information and the second waveform attribute information are consistent; When it is determined that the first waveform attribute information and the first waveform attribute information are consistent, the waveform data corresponding to the hash value is read from the preset storage location.

7. A waveform data matching device, characterized in that, The device includes: The acquisition module is used to acquire the waveform expression, the total start time of the waveform, and the attribute information of the first waveform; A parsing module is used to parse the waveform expression and generate a computation tree, wherein the computation tree includes at least one node and node information for each node; An update module is configured to, when determining, based on the node information of each node, that the computational tree includes at least one type of node to be updated, and that at least one type of node to be updated is a waveform node, select a minimum start time from the start time included in the node information of each waveform node in the computational tree; and update the start time included in the node information of each waveform node in the computational tree according to the minimum start time, thereby obtaining an updated computational tree; The calculation module is used to perform hash calculations on the updated calculation tree to obtain the target hash value; The search module is used to search for waveform data that matches both the target hash value and the first waveform attribute information in a preset storage location; The transmitting module is used to send the waveform data and the total start time of the waveform to the wave generation device to generate microwave pulses.

8. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the waveform data matching method of any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the waveform data matching method according to any one of claims 1 to 6.

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